From 3b2004915105f29405515ce709b385e95f58c2ce Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 8 Sep 2025 05:50:12 +0000 Subject: [PATCH 01/18] Initial plan From a6c7fed7e58f81d62d032ea39bfdf469ff719aa1 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 8 Sep 2025 05:56:14 +0000 Subject: [PATCH 02/18] Implement degree_adoption_diagnostic function with comprehensive tests Co-authored-by: gvegayon <893619+gvegayon@users.noreply.github.com> --- R/stats.R | 280 ++++++++++++++++++ .../test-degree-adoption-diagnostic.R | 186 ++++++++++++ 2 files changed, 466 insertions(+) create mode 100644 tests/testthat/test-degree-adoption-diagnostic.R diff --git a/R/stats.R b/R/stats.R index dadcac6..070cc7f 100644 --- a/R/stats.R +++ b/R/stats.R @@ -1359,3 +1359,283 @@ matrix_compare <- function(A, B, fun) { #' @export compare_matrix <- matrix_compare +#' Degree and Time of Adoption Diagnostic +#' +#' Analyzes the correlation between in-degree, out-degree, and time of adoption +#' to identify whether opinion leaders were early adopters (supporters) or late +#' adopters (opposers). +#' +#' @param graph A \code{\link{diffnet}} object or graph data structure. +#' @param degree_strategy Character scalar. How to aggregate degree measures across +#' time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}. +#' @param bootstrap Logical scalar. Whether to compute bootstrap confidence intervals. +#' @param R Integer scalar. Number of bootstrap replicates (default 1000). +#' @param conf.level Numeric scalar. Confidence level for bootstrap intervals (default 0.95). +#' @param valued Logical scalar. Whether to use edge weights in degree calculations. +#' @param ... Additional arguments (currently unused). +#' +#' @details +#' This diagnostic function computes correlations between degree centrality measures +#' (in-degree and out-degree) and time of adoption. Positive correlations suggest +#' that central actors (opinion leaders) adopted early, while negative correlations +#' suggest they adopted late. +#' +#' The degree measures can be aggregated across time periods in different ways: +#' \itemize{ +#' \item \code{"mean"}: Average degree across all time periods +#' \item \code{"first"}: Degree in the first time period +#' \item \code{"last"}: Degree in the last time period +#' } +#' +#' When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to +#' compute bootstrap confidence intervals for the correlations. This helps +#' assess the statistical significance of the observed correlations. +#' +#' @return A list with components: +#' \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} +#' \item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} +#' \item{call}{The matched call} +#' \item{degree_strategy}{The degree aggregation strategy used} +#' \item{sample_size}{Number of nodes included in the analysis (excludes non-adopters)} +#' +#' @examples +#' # Example with Korean Family Planning data +#' data(kfamilyDiffNet) +#' +#' # Basic diagnostic +#' diag <- degree_adoption_diagnostic(kfamilyDiffNet) +#' print(diag$correlations) +#' +#' # With bootstrap confidence intervals +#' diag_boot <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = TRUE, R = 100) +#' print(diag_boot$bootstrap) +#' +#' # Using different degree aggregation strategies +#' diag_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") +#' diag_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +#' +#' @seealso \code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} +#' @family statistics +#' @export +degree_adoption_diagnostic <- function( + graph, + degree_strategy = c("mean", "first", "last"), + bootstrap = TRUE, + R = 1000, + conf.level = 0.95, + valued = getOption("diffnet.valued", FALSE), + ... +) { + + # Input validation + if (!inherits(graph, "diffnet")) + stop("'graph' must be a diffnet object.", call. = FALSE) + + degree_strategy <- match.arg(degree_strategy) + + if (!is.logical(bootstrap) || length(bootstrap) != 1) + stop("'bootstrap' must be a logical scalar.", call. = FALSE) + + if (!is.numeric(R) || length(R) != 1 || R < 1) + stop("'R' must be a positive integer.", call. = FALSE) + + if (!is.numeric(conf.level) || length(conf.level) != 1 || + conf.level <= 0 || conf.level >= 1) + stop("'conf.level' must be between 0 and 1.", call. = FALSE) + + # Extract time of adoption + toa <- graph$toa + + # Only include adopters in the analysis + adopters <- !is.na(toa) + if (sum(adopters) < 3) { + stop("At least 3 adopters are required for correlation analysis.", call. = FALSE) + } + + toa_adopters <- toa[adopters] + + # Compute degree measures + indegree_full <- dgr(graph, cmode = "indegree", valued = valued) + outdegree_full <- dgr(graph, cmode = "outdegree", valued = valued) + + # Aggregate degree measures according to strategy + if (degree_strategy == "mean") { + indegree_agg <- rowMeans(indegree_full, na.rm = TRUE) + outdegree_agg <- rowMeans(outdegree_full, na.rm = TRUE) + } else if (degree_strategy == "first") { + indegree_agg <- indegree_full[, 1] + outdegree_agg <- outdegree_full[, 1] + } else { # "last" + indegree_agg <- indegree_full[, ncol(indegree_full)] + outdegree_agg <- outdegree_full[, ncol(outdegree_full)] + } + + # Subset to adopters only + indegree_adopters <- indegree_agg[adopters] + outdegree_adopters <- outdegree_agg[adopters] + + # Compute correlations + cor_indegree_toa <- stats::cor(indegree_adopters, toa_adopters, use = "complete.obs") + cor_outdegree_toa <- stats::cor(outdegree_adopters, toa_adopters, use = "complete.obs") + + correlations <- c( + indegree_toa = cor_indegree_toa, + outdegree_toa = cor_outdegree_toa + ) + + # Bootstrap analysis if requested + bootstrap_results <- NULL + if (bootstrap) { + + # Bootstrap function for correlation + boot_cor <- function(data, indices) { + # data is a matrix with columns: indegree, outdegree, toa + d <- data[indices, ] + c( + stats::cor(d[, 1], d[, 3], use = "complete.obs"), # indegree-toa correlation + stats::cor(d[, 2], d[, 3], use = "complete.obs") # outdegree-toa correlation + ) + } + + # Prepare data for bootstrap + boot_data <- cbind(indegree_adopters, outdegree_adopters, toa_adopters) + + # Remove any incomplete cases + complete_cases <- stats::complete.cases(boot_data) + boot_data <- boot_data[complete_cases, , drop = FALSE] + + if (nrow(boot_data) < 3) { + warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) + } else { + # Perform bootstrap + boot_result <- boot::boot(boot_data, boot_cor, R = R) + + # Compute confidence intervals + alpha <- 1 - conf.level + + tryCatch({ + ci_indegree <- boot::boot.ci(boot_result, type = "perc", index = 1, conf = conf.level) + ci_outdegree <- boot::boot.ci(boot_result, type = "perc", index = 2, conf = conf.level) + + bootstrap_results <- list( + indegree = list( + correlation = boot_result$t0[1], + bias = mean(boot_result$t[, 1]) - boot_result$t0[1], + std_error = stats::sd(boot_result$t[, 1]), + conf_int = ci_indegree$percent[4:5], + conf_level = conf.level + ), + outdegree = list( + correlation = boot_result$t0[2], + bias = mean(boot_result$t[, 2]) - boot_result$t0[2], + std_error = stats::sd(boot_result$t[, 2]), + conf_int = ci_outdegree$percent[4:5], + conf_level = conf.level + ), + R = R, + boot_object = boot_result + ) + }, error = function(e) { + warning("Bootstrap confidence intervals could not be computed: ", e$message, call. = FALSE) + bootstrap_results <<- list( + indegree = list( + correlation = boot_result$t0[1], + bias = mean(boot_result$t[, 1]) - boot_result$t0[1], + std_error = stats::sd(boot_result$t[, 1]) + ), + outdegree = list( + correlation = boot_result$t0[2], + bias = mean(boot_result$t[, 2]) - boot_result$t0[2], + std_error = stats::sd(boot_result$t[, 2]) + ), + R = R, + boot_object = boot_result + ) + }) + } + } + + # Return result + structure( + list( + correlations = correlations, + bootstrap = bootstrap_results, + call = match.call(), + degree_strategy = degree_strategy, + sample_size = sum(adopters) + ), + class = "degree_adoption_diagnostic" + ) +} + +#' @export +print.degree_adoption_diagnostic <- function(x, ...) { + cat("Degree and Time of Adoption Diagnostic\n") + cat("======================================\n\n") + + cat("Degree aggregation strategy:", x$degree_strategy, "\n") + cat("Sample size (adopters only):", x$sample_size, "\n\n") + + cat("Correlations:\n") + cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", x$correlations[["indegree_toa"]])) + cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", x$correlations[["outdegree_toa"]])) + + if (!is.null(x$bootstrap)) { + cat("\nBootstrap Results:\n") + cat(sprintf(" Replicates: %d\n", x$bootstrap$R)) + cat(sprintf(" Confidence level: %.1f%%\n", x$bootstrap$indegree$conf_level * 100)) + + if (!is.null(x$bootstrap$indegree$conf_int)) { + cat("\n In-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) + cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) + cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) + cat(sprintf(" %%.1f%% CI: [%6.3f, %6.3f]\n", + x$bootstrap$indegree$conf_level * 100, + x$bootstrap$indegree$conf_int[1], + x$bootstrap$indegree$conf_int[2])) + + cat("\n Out-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) + cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) + cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) + cat(sprintf(" %%.1f%% CI: [%6.3f, %6.3f]\n", + x$bootstrap$outdegree$conf_level * 100, + x$bootstrap$outdegree$conf_int[1], + x$bootstrap$outdegree$conf_int[2])) + } else { + cat("\n Bootstrap estimates (CI unavailable):\n") + cat(sprintf(" In-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", + x$bootstrap$indegree$correlation, + x$bootstrap$indegree$bias, + x$bootstrap$indegree$std_error)) + cat(sprintf(" Out-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", + x$bootstrap$outdegree$correlation, + x$bootstrap$outdegree$bias, + x$bootstrap$outdegree$std_error)) + } + } + + cat("\nInterpretation:\n") + indegree_cor <- x$correlations[["indegree_toa"]] + outdegree_cor <- x$correlations[["outdegree_toa"]] + + if (indegree_cor > 0.1) { + cat(" In-degree: Central actors (high in-degree) tended to adopt early (supporters)\n") + } else if (indegree_cor < -0.1) { + cat(" In-degree: Central actors (high in-degree) tended to adopt late (opposers)\n") + } else { + cat(" In-degree: Weak relationship between centrality and adoption timing\n") + } + + if (outdegree_cor > 0.1) { + cat(" Out-degree: Influential actors (high out-degree) tended to adopt early (supporters)\n") + } else if (outdegree_cor < -0.1) { + cat(" Out-degree: Influential actors (high out-degree) tended to adopt late (opposers)\n") + } else { + cat(" Out-degree: Weak relationship between influence and adoption timing\n") + } + + invisible(x) +} + diff --git a/tests/testthat/test-degree-adoption-diagnostic.R b/tests/testthat/test-degree-adoption-diagnostic.R new file mode 100644 index 0000000..ad479e5 --- /dev/null +++ b/tests/testthat/test-degree-adoption-diagnostic.R @@ -0,0 +1,186 @@ +context("Degree and Time of Adoption Diagnostic") + +test_that("degree_adoption_diagnostic works with basic inputs", { + # Create a simple test diffnet + set.seed(123) + dn <- rdiffnet(30, 4, seed.p.adopt = 0.2) + + # Basic test + result <- degree_adoption_diagnostic(dn, bootstrap = FALSE) + + expect_s3_class(result, "degree_adoption_diagnostic") + expect_named(result, c("correlations", "bootstrap", "call", "degree_strategy", "sample_size")) + expect_named(result$correlations, c("indegree_toa", "outdegree_toa")) + expect_type(result$correlations, "double") + expect_length(result$correlations, 2) + expect_null(result$bootstrap) + expect_equal(result$degree_strategy, "mean") + expect_gt(result$sample_size, 0) +}) + +test_that("degree_adoption_diagnostic works with different degree strategies", { + set.seed(456) + dn <- rdiffnet(25, 3, seed.p.adopt = 0.3) + + result_mean <- degree_adoption_diagnostic(dn, degree_strategy = "mean", bootstrap = FALSE) + result_first <- degree_adoption_diagnostic(dn, degree_strategy = "first", bootstrap = FALSE) + result_last <- degree_adoption_diagnostic(dn, degree_strategy = "last", bootstrap = FALSE) + + expect_equal(result_mean$degree_strategy, "mean") + expect_equal(result_first$degree_strategy, "first") + expect_equal(result_last$degree_strategy, "last") + + # Different strategies should potentially give different results + expect_true( + !all(result_mean$correlations == result_first$correlations) || + !all(result_mean$correlations == result_last$correlations) + ) +}) + +test_that("degree_adoption_diagnostic bootstrap works", { + set.seed(789) + dn <- rdiffnet(20, 3, seed.p.adopt = 0.4) + + # Skip if boot package not available + skip_if_not_installed("boot") + + result <- degree_adoption_diagnostic(dn, bootstrap = TRUE, R = 50, conf.level = 0.9) + + expect_false(is.null(result$bootstrap)) + expect_named(result$bootstrap, c("indegree", "outdegree", "R", "boot_object")) + expect_equal(result$bootstrap$R, 50) + + # Check bootstrap structure for indegree + expect_named(result$bootstrap$indegree, + c("correlation", "bias", "std_error", "conf_int", "conf_level")) + expect_equal(result$bootstrap$indegree$conf_level, 0.9) + expect_length(result$bootstrap$indegree$conf_int, 2) + + # Check bootstrap structure for outdegree + expect_named(result$bootstrap$outdegree, + c("correlation", "bias", "std_error", "conf_int", "conf_level")) + expect_equal(result$bootstrap$outdegree$conf_level, 0.9) + expect_length(result$bootstrap$outdegree$conf_int, 2) +}) + +test_that("degree_adoption_diagnostic handles edge cases", { + # Test with diffnet that has no adopters + set.seed(111) + dn <- rdiffnet(10, 3, seed.p.adopt = 0.0) + dn$toa[] <- NA # Force no adopters + + expect_error( + degree_adoption_diagnostic(dn, bootstrap = FALSE), + "At least 3 adopters are required" + ) + + # Test with insufficient adopters + set.seed(222) + dn <- rdiffnet(10, 3, seed.p.adopt = 0.1) + # Force only 2 adopters + dn$toa[!is.na(dn$toa)][-(1:2)] <- NA + + expect_error( + degree_adoption_diagnostic(dn, bootstrap = FALSE), + "At least 3 adopters are required" + ) +}) + +test_that("degree_adoption_diagnostic input validation", { + set.seed(333) + dn <- rdiffnet(15, 3, seed.p.adopt = 0.3) + + # Test non-diffnet input + expect_error( + degree_adoption_diagnostic("not a diffnet"), + "'graph' must be a diffnet object" + ) + + # Test invalid degree_strategy + expect_error( + degree_adoption_diagnostic(dn, degree_strategy = "invalid"), + "'arg' should be one of" + ) + + # Test invalid bootstrap + expect_error( + degree_adoption_diagnostic(dn, bootstrap = "yes"), + "'bootstrap' must be a logical scalar" + ) + + # Test invalid R + expect_error( + degree_adoption_diagnostic(dn, R = -1), + "'R' must be a positive integer" + ) + + # Test invalid conf.level + expect_error( + degree_adoption_diagnostic(dn, conf.level = 1.5), + "'conf.level' must be between 0 and 1" + ) +}) + +test_that("degree_adoption_diagnostic works with Korean Family Planning data", { + # Test with real data + data(kfamilyDiffNet) + + # Basic test with real data + result <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) + + expect_s3_class(result, "degree_adoption_diagnostic") + expect_gt(result$sample_size, 100) # Korean data should have many adopters + expect_true(all(is.finite(result$correlations))) + + # Test that correlations are within valid range + expect_true(all(result$correlations >= -1 & result$correlations <= 1)) +}) + +test_that("degree_adoption_diagnostic print method works", { + set.seed(444) + dn <- rdiffnet(20, 3, seed.p.adopt = 0.3) + + result <- degree_adoption_diagnostic(dn, bootstrap = FALSE) + + # Test that print doesn't error and returns invisibly + expect_output(print(result), "Degree and Time of Adoption Diagnostic") + expect_output(print(result), "Correlations:") + expect_output(print(result), "In-degree") + expect_output(print(result), "Out-degree") + expect_output(print(result), "Interpretation:") + + # Test that it returns the object invisibly + returned <- capture.output(printed_result <- print(result)) + expect_identical(result, printed_result) +}) + +test_that("degree_adoption_diagnostic reproduces manual calculation", { + # Create a simple case where we can manually verify + set.seed(555) + dn <- rdiffnet(15, 3, seed.p.adopt = 0.4) + + result <- degree_adoption_diagnostic(dn, degree_strategy = "mean", bootstrap = FALSE) + + # Manual calculation to verify + adopters <- !is.na(dn$toa) + toa_adopters <- dn$toa[adopters] + + # Compute degrees manually + indegree_full <- dgr(dn, cmode = "indegree", valued = FALSE) + outdegree_full <- dgr(dn, cmode = "outdegree", valued = FALSE) + + indegree_mean <- rowMeans(indegree_full, na.rm = TRUE) + outdegree_mean <- rowMeans(outdegree_full, na.rm = TRUE) + + indegree_adopters <- indegree_mean[adopters] + outdegree_adopters <- outdegree_mean[adopters] + + # Manual correlations + manual_cor_in <- cor(indegree_adopters, toa_adopters, use = "complete.obs") + manual_cor_out <- cor(outdegree_adopters, toa_adopters, use = "complete.obs") + + # Compare + expect_equal(result$correlations[["indegree_toa"]], manual_cor_in, tolerance = 1e-10) + expect_equal(result$correlations[["outdegree_toa"]], manual_cor_out, tolerance = 1e-10) + expect_equal(result$sample_size, sum(adopters)) +}) \ No newline at end of file From 42b6876babf56c12b051e844a522a76cd19f3ff0 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 8 Sep 2025 06:01:41 +0000 Subject: [PATCH 03/18] Fix print method formatting and complete implementation Co-authored-by: gvegayon <893619+gvegayon@users.noreply.github.com> --- R/stats.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/stats.R b/R/stats.R index 070cc7f..3e3ca04 100644 --- a/R/stats.R +++ b/R/stats.R @@ -1590,7 +1590,7 @@ print.degree_adoption_diagnostic <- function(x, ...) { cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) - cat(sprintf(" %%.1f%% CI: [%6.3f, %6.3f]\n", + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", x$bootstrap$indegree$conf_level * 100, x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) @@ -1599,7 +1599,7 @@ print.degree_adoption_diagnostic <- function(x, ...) { cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) - cat(sprintf(" %%.1f%% CI: [%6.3f, %6.3f]\n", + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", x$bootstrap$outdegree$conf_level * 100, x$bootstrap$outdegree$conf_int[1], x$bootstrap$outdegree$conf_int[2])) From c12576e09d461c28647826c1538cd019b1144bbb Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 9 Sep 2025 17:39:45 -0300 Subject: [PATCH 04/18] Review changes: - Expanded degree_adoption_diagnostic to accept generic graphs + TOA - Improved print method with CI-aware interpretation - Updated tests for new input validation --- DESCRIPTION | 2 +- NAMESPACE | 2 + R/stats.R | 383 ++++++++++++------ man/bass.Rd | 1 + man/classify_adopters.Rd | 1 + man/cumulative_adopt_count.Rd | 1 + man/degree_adoption_diagnostic.Rd | 130 ++++++ man/dgr.Rd | 1 + man/ego_variance.Rd | 1 + man/exposure.Rd | 1 + man/hazard_rate.Rd | 1 + man/infection.Rd | 1 + man/moran.Rd | 1 + man/struct_equiv.Rd | 1 + man/threshold.Rd | 1 + man/vertex_covariate_dist.Rd | 1 + .../test-degree-adoption-diagnostic.R | 82 ++-- 17 files changed, 449 insertions(+), 162 deletions(-) create mode 100644 man/degree_adoption_diagnostic.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 0d960f0..be64f9c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -51,7 +51,7 @@ Suggests: survival VignetteBuilder: knitr LinkingTo: Rcpp, RcppArmadillo -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 Encoding: UTF-8 URL: https://github.com/USCCANA/netdiffuseR, https://USCCANA.github.io/netdiffuseR/ diff --git a/NAMESPACE b/NAMESPACE index 0e4f522..d3a9d6e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -66,6 +66,7 @@ S3method(plot_diffnet2,diffnet) S3method(plot_threshold,array) S3method(plot_threshold,default) S3method(plot_threshold,diffnet) +S3method(print,degree_adoption_diagnostic) S3method(print,diffnet) S3method(print,diffnet_bootnet) S3method(print,diffnet_diffmap) @@ -99,6 +100,7 @@ export(classify_adopters) export(classify_graph) export(compare_matrix) export(cumulative_adopt_count) +export(degree_adoption_diagnostic) export(dgr) export(diag_expand) export(diffmap) diff --git a/R/stats.R b/R/stats.R index 3e3ca04..c069e9d 100644 --- a/R/stats.R +++ b/R/stats.R @@ -1365,14 +1365,23 @@ compare_matrix <- matrix_compare #' to identify whether opinion leaders were early adopters (supporters) or late #' adopters (opposers). #' -#' @param graph A \code{\link{diffnet}} object or graph data structure. +#' @param graph A \code{\link{diffnet}} object or a graph data structure (classes include +#' \code{array} (\eqn{n\times n \times T}{n*n*T}), \code{dgCMatrix} (sparse), +#' \code{igraph}, etc.; see \link{netdiffuseR-graphs}). +#' @param toa Integer vector of length \eqn{n} with time of adoption. +#' Required when \code{graph} is not a \code{diffnet}. +#' @param t0,t1 Optional integer scalars defining the first and last observed +#' periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 +#' and \code{t1} to \code{max(toa, na.rm=TRUE)}. +#' @param name,behavior Optional character scalars used only when coercing +#' inputs into a \code{diffnet} object (passed to \code{new_diffnet}). #' @param degree_strategy Character scalar. How to aggregate degree measures across #' time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}. #' @param bootstrap Logical scalar. Whether to compute bootstrap confidence intervals. #' @param R Integer scalar. Number of bootstrap replicates (default 1000). #' @param conf.level Numeric scalar. Confidence level for bootstrap intervals (default 0.95). #' @param valued Logical scalar. Whether to use edge weights in degree calculations. -#' @param ... Additional arguments (currently unused). +#' @param ... Additional arguments passed on when coercing to \code{diffnet}. #' #' @details #' This diagnostic function computes correlations between degree centrality measures @@ -1386,6 +1395,10 @@ compare_matrix <- matrix_compare #' \item \code{"first"}: Degree in the first time period #' \item \code{"last"}: Degree in the last time period #' } +#' +#' The function accepts either a ready-to-use \code{diffnet} or generic graph +#' inputs plus a \code{toa} vector. When needed, inputs are coerced to a +#' \code{diffnet} via an internal helper #' #' When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to #' compute bootstrap confidence intervals for the correlations. This helps @@ -1399,97 +1412,105 @@ compare_matrix <- matrix_compare #' \item{sample_size}{Number of nodes included in the analysis (excludes non-adopters)} #' #' @examples -#' # Example with Korean Family Planning data +#' # Basic usage with Korean Family Planning data #' data(kfamilyDiffNet) -#' -#' # Basic diagnostic -#' diag <- degree_adoption_diagnostic(kfamilyDiffNet) -#' print(diag$correlations) +#' # Results show correlations and interpretation +#' result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) +#' print(result_basics) +#' print(result_basics$correlations) #' #' # With bootstrap confidence intervals -#' diag_boot <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = TRUE, R = 100) -#' print(diag_boot$bootstrap) +#' result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) +#' print(result_boot) +#' +#' # Different degree aggregation strategies +#' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") +#' print(result_first) #' -#' # Using different degree aggregation strategies -#' diag_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") -#' diag_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +#' print(result_last) +#' +#' # Using a sparse static adjacency matrix + toa ----------------------------- +#' \dontrun{ +#' library(Matrix) +#' set.seed(1) +#' A <- rsparsematrix(50, 50, 0.05); A <- abs(A); A@x[A@x>0] <- 1 +#' toa <- sample(1:6, 50, replace = TRUE) +#' diag2 <- degree_adoption_diagnostic(A, toa = toa, valued = FALSE) +#' } #' #' @seealso \code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} #' @family statistics #' @export degree_adoption_diagnostic <- function( - graph, - degree_strategy = c("mean", "first", "last"), - bootstrap = TRUE, - R = 1000, - conf.level = 0.95, - valued = getOption("diffnet.valued", FALSE), - ... + graph, + degree_strategy = c("mean", "first", "last"), + bootstrap = TRUE, + R = 1000, + conf.level = 0.95, + valued = getOption("diffnet.valued", FALSE), + toa = NULL, + t0 = NULL, + t1 = NULL, + name = "A diffusion network", + behavior = "Innovation", + ... ) { - - # Input validation - if (!inherits(graph, "diffnet")) - stop("'graph' must be a diffnet object.", call. = FALSE) - + # Coerce to diffnet if needed ---------------------------------------------- + if (!inherits(graph, "diffnet")) { + if (is.null(toa)) + stop("When 'graph' is not a diffnet, argument 'toa' must be provided.", call. = FALSE) + dn <- .ndd_coerce_to_diffnet(graph, toa = toa, t0 = t0, t1 = t1, + name = name, behavior = behavior, valued = valued, ...) + graph <- dn + } + degree_strategy <- match.arg(degree_strategy) - + if (!is.logical(bootstrap) || length(bootstrap) != 1) stop("'bootstrap' must be a logical scalar.", call. = FALSE) - if (!is.numeric(R) || length(R) != 1 || R < 1) stop("'R' must be a positive integer.", call. = FALSE) - - if (!is.numeric(conf.level) || length(conf.level) != 1 || - conf.level <= 0 || conf.level >= 1) + if (!is.numeric(conf.level) || length(conf.level) != 1 || conf.level <= 0 || conf.level >= 1) stop("'conf.level' must be between 0 and 1.", call. = FALSE) - - # Extract time of adoption + + # Extract time of adoption and keep adopters -------------------------------- toa <- graph$toa - - # Only include adopters in the analysis adopters <- !is.na(toa) - if (sum(adopters) < 3) { + if (sum(adopters) < 3) stop("At least 3 adopters are required for correlation analysis.", call. = FALSE) - } - toa_adopters <- toa[adopters] - - # Compute degree measures - indegree_full <- dgr(graph, cmode = "indegree", valued = valued) + + # Degree measures ----------------------------------------------------------- + indegree_full <- dgr(graph, cmode = "indegree", valued = valued) outdegree_full <- dgr(graph, cmode = "outdegree", valued = valued) - - # Aggregate degree measures according to strategy + if (degree_strategy == "mean") { - indegree_agg <- rowMeans(indegree_full, na.rm = TRUE) + indegree_agg <- rowMeans(indegree_full, na.rm = TRUE) outdegree_agg <- rowMeans(outdegree_full, na.rm = TRUE) } else if (degree_strategy == "first") { - indegree_agg <- indegree_full[, 1] + indegree_agg <- indegree_full[, 1] outdegree_agg <- outdegree_full[, 1] - } else { # "last" - indegree_agg <- indegree_full[, ncol(indegree_full)] + } else { + indegree_agg <- indegree_full[, ncol(indegree_full)] outdegree_agg <- outdegree_full[, ncol(outdegree_full)] } - - # Subset to adopters only - indegree_adopters <- indegree_agg[adopters] + + indegree_adopters <- indegree_agg[adopters] outdegree_adopters <- outdegree_agg[adopters] - - # Compute correlations - cor_indegree_toa <- stats::cor(indegree_adopters, toa_adopters, use = "complete.obs") + + cor_indegree_toa <- stats::cor(indegree_adopters, toa_adopters, use = "complete.obs") cor_outdegree_toa <- stats::cor(outdegree_adopters, toa_adopters, use = "complete.obs") - + correlations <- c( - indegree_toa = cor_indegree_toa, + indegree_toa = cor_indegree_toa, outdegree_toa = cor_outdegree_toa ) - - # Bootstrap analysis if requested + + # Bootstrap analysis -------------------------------------------------------- bootstrap_results <- NULL if (bootstrap) { - - # Bootstrap function for correlation boot_cor <- function(data, indices) { - # data is a matrix with columns: indegree, outdegree, toa d <- data[indices, ] c( stats::cor(d[, 1], d[, 3], use = "complete.obs"), # indegree-toa correlation @@ -1499,8 +1520,6 @@ degree_adoption_diagnostic <- function( # Prepare data for bootstrap boot_data <- cbind(indegree_adopters, outdegree_adopters, toa_adopters) - - # Remove any incomplete cases complete_cases <- stats::complete.cases(boot_data) boot_data <- boot_data[complete_cases, , drop = FALSE] @@ -1509,28 +1528,23 @@ degree_adoption_diagnostic <- function( } else { # Perform bootstrap boot_result <- boot::boot(boot_data, boot_cor, R = R) - - # Compute confidence intervals - alpha <- 1 - conf.level - tryCatch({ ci_indegree <- boot::boot.ci(boot_result, type = "perc", index = 1, conf = conf.level) ci_outdegree <- boot::boot.ci(boot_result, type = "perc", index = 2, conf = conf.level) - bootstrap_results <- list( indegree = list( correlation = boot_result$t0[1], - bias = mean(boot_result$t[, 1]) - boot_result$t0[1], - std_error = stats::sd(boot_result$t[, 1]), - conf_int = ci_indegree$percent[4:5], - conf_level = conf.level + bias = mean(boot_result$t[, 1]) - boot_result$t0[1], + std_error = stats::sd(boot_result$t[, 1]), + conf_int = ci_indegree$percent[4:5], + conf_level = conf.level ), outdegree = list( correlation = boot_result$t0[2], - bias = mean(boot_result$t[, 2]) - boot_result$t0[2], - std_error = stats::sd(boot_result$t[, 2]), - conf_int = ci_outdegree$percent[4:5], - conf_level = conf.level + bias = mean(boot_result$t[, 2]) - boot_result$t0[2], + std_error = stats::sd(boot_result$t[, 2]), + conf_int = ci_outdegree$percent[4:5], + conf_level = conf.level ), R = R, boot_object = boot_result @@ -1540,13 +1554,13 @@ degree_adoption_diagnostic <- function( bootstrap_results <<- list( indegree = list( correlation = boot_result$t0[1], - bias = mean(boot_result$t[, 1]) - boot_result$t0[1], - std_error = stats::sd(boot_result$t[, 1]) + bias = mean(boot_result$t[, 1]) - boot_result$t0[1], + std_error = stats::sd(boot_result$t[, 1]) ), outdegree = list( correlation = boot_result$t0[2], - bias = mean(boot_result$t[, 2]) - boot_result$t0[2], - std_error = stats::sd(boot_result$t[, 2]) + bias = mean(boot_result$t[, 2]) - boot_result$t0[2], + std_error = stats::sd(boot_result$t[, 2]) ), R = R, boot_object = boot_result @@ -1554,18 +1568,71 @@ degree_adoption_diagnostic <- function( }) } } - - # Return result - structure( - list( - correlations = correlations, - bootstrap = bootstrap_results, - call = match.call(), - degree_strategy = degree_strategy, - sample_size = sum(adopters) - ), - class = "degree_adoption_diagnostic" - ) + + structure(list( + correlations = correlations, + bootstrap = bootstrap_results, + call = match.call(), + degree_strategy = degree_strategy, + sample_size = sum(adopters) + ), class = "degree_adoption_diagnostic") +} + +# Internal: Coerce to diffnet from common graph inputs ------------------------ +.ndd_coerce_to_diffnet <- function(graph, toa, t0 = NULL, t1 = NULL, + name = "A diffusion network", + behavior = "Innovation", + valued = getOption("diffnet.valued", FALSE), + ...) { + # infer periods + if (is.null(t0)) t0 <- 1L + if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) + + # build list of adjacency matrices (length T) in dgCMatrix format + to_list_dgC <- function(g1) { + if (inherits(g1, "dgCMatrix")) { + G <- g1 + } else if (inherits(g1, "matrix")) { + G <- methods::as(g1, "dgCMatrix") + } else if (inherits(g1, "igraph")) { + G <- methods::as(igraph::as_adjacency_matrix(g1, sparse = TRUE), "dgCMatrix") + } else if (inherits(g1, "network")) { + tmp <- as_generic_graph.network(g1) + G <- tmp$graph[[1]] + } else if (inherits(g1, "list")) { + return(lapply(g1, to_list_dgC)) + } else if (inherits(g1, "array")) { + return(lapply(seq_len(dim(g1)[3]), function(i) methods::as(g1[, , i], "dgCMatrix"))) + } else { + stop("Unsupported graph class for coercion.") + } + + if (!valued) G@x <- rep(1, length(G@x)) + list(G) + } + + Glist <- if (inherits(graph, "list")) unlist(lapply(graph, to_list_dgC), recursive = FALSE) + else if (inherits(graph, "array")) lapply(seq_len(dim(graph)[3]), function(i) methods::as(graph[, , i], "dgCMatrix")) + else to_list_dgC(graph) + + # If only one slice provided, repeat across [t0, t1] + Tlen <- length(Glist) + if (Tlen == 1L && (t1 - t0 + 1L) > 1L) { + Glist <- rep(Glist, t1 - t0 + 1L) + names(Glist) <- as.character(seq.int(t0, t1)) + } + + # Align vertex names (if any) with toa names + rn <- rownames(Glist[[1]]) + if (!is.null(names(toa)) && length(rn) && !identical(names(toa), rn)) { + # try to reorder by names + ord <- match(rn, names(toa)) + if (anyNA(ord)) stop("Names in 'toa' do not match graph vertex names.") + toa <- toa[ord] + } + + new_diffnet(graph = Glist, toa = toa, t0 = t0, t1 = t1, + name = name, behavior = behavior) } #' @export @@ -1583,59 +1650,129 @@ print.degree_adoption_diagnostic <- function(x, ...) { if (!is.null(x$bootstrap)) { cat("\nBootstrap Results:\n") cat(sprintf(" Replicates: %d\n", x$bootstrap$R)) - cat(sprintf(" Confidence level: %.1f%%\n", x$bootstrap$indegree$conf_level * 100)) - + cat(sprintf(" Confidence level: %.1f%%\n\n", x$bootstrap$indegree$conf_level * 100)) + if (!is.null(x$bootstrap$indegree$conf_int)) { - cat("\n In-degree correlation:\n") + cat(" In-degree correlation:\n") cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", x$bootstrap$indegree$conf_level * 100, - x$bootstrap$indegree$conf_int[1], - x$bootstrap$indegree$conf_int[2])) - - cat("\n Out-degree correlation:\n") + x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) + + cat(" Out-degree correlation:\n") cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", x$bootstrap$outdegree$conf_level * 100, - x$bootstrap$outdegree$conf_int[1], - x$bootstrap$outdegree$conf_int[2])) + x$bootstrap$outdegree$conf_int[1], x$bootstrap$outdegree$conf_int[2])) } else { - cat("\n Bootstrap estimates (CI unavailable):\n") - cat(sprintf(" In-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", + cat(" Bootstrap estimates (CI unavailable):\n") + cat(sprintf(" In-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", x$bootstrap$indegree$correlation, x$bootstrap$indegree$bias, x$bootstrap$indegree$std_error)) - cat(sprintf(" Out-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", + cat(sprintf(" Out-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n\n", x$bootstrap$outdegree$correlation, x$bootstrap$outdegree$bias, x$bootstrap$outdegree$std_error)) } } - - cat("\nInterpretation:\n") - indegree_cor <- x$correlations[["indegree_toa"]] - outdegree_cor <- x$correlations[["outdegree_toa"]] - - if (indegree_cor > 0.1) { - cat(" In-degree: Central actors (high in-degree) tended to adopt early (supporters)\n") - } else if (indegree_cor < -0.1) { - cat(" In-degree: Central actors (high in-degree) tended to adopt late (opposers)\n") - } else { - cat(" In-degree: Weak relationship between centrality and adoption timing\n") - } - - if (outdegree_cor > 0.1) { - cat(" Out-degree: Influential actors (high out-degree) tended to adopt early (supporters)\n") - } else if (outdegree_cor < -0.1) { - cat(" Out-degree: Influential actors (high out-degree) tended to adopt late (opposers)\n") - } else { - cat(" Out-degree: Weak relationship between influence and adoption timing\n") + + # Concise CI-aware interpretation ------------------------------------------ + cat("Interpretation:\n") + thr <- 0.10 + indeg <- x$correlations[["indegree_toa"]] + outdeg <- x$correlations[["outdegree_toa"]] + + has_boot <- !is.null(x$bootstrap) + indeg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL + outdeg_ci <- if (has_boot && !is.null(x$bootstrap$outdegree$conf_int)) x$bootstrap$outdegree$conf_int else NULL + lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level*100 else NA_real_ + + # helper to print a single line following the required phrasing + explain <- function(label, r, ci) { + # Handle NA correlations gracefully + if (is.na(r)) { + if (is.null(ci)) { + cat(sprintf( + " %s: Weak relationship between centrality and adoption timing:\n r is NA; no CI.\n", + label + )) + } else { + # if CI exists but r is NA, we still treat as inconclusive text-wise + lvl_local <- if (!is.na(lvl)) lvl else 95 + ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) + cat(sprintf( + " %s: Weak relationship between centrality and adoption timing; %s statistically supported:\n r is NA; CI (%.1f%%) %s 0.\n", + label, + if (ci_includes_zero) "NOT" else "", + lvl_local, + if (ci_includes_zero) "includes" else "excludes" + )) + } + return(invisible()) + } + + abs_big <- abs(r) > thr + + if (is.null(ci)) { + if (!abs_big) { + cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \u2264 %.1f; no CI.\n", + label, + if (label == "In-degree") "in-degree" else "in-degree", # text requested by user examples + thr)) + } else if (r > 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr)) + } else { + cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers):\n |r| > %.1f; no CI.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr)) + } + return(invisible()) + } + + # CI available + ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) + lvl_local <- if (!is.na(lvl)) lvl else 95 + + if (!abs_big && !ci_includes_zero) { + cat(sprintf(" %s: Weak relationship between centrality and adoption timing; statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) excludes 0.\n", + label, thr, lvl_local)) + } else if (!abs_big && ci_includes_zero) { + cat(sprintf(" %s: Weak relationship between centrality and adoption timing; NOT statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) includes 0.\n", + label, thr, lvl_local)) + } else if (abs_big && !ci_includes_zero && r > 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr, lvl_local)) + } else if (abs_big && !ci_includes_zero && r < 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr, lvl_local)) + } else if (abs_big && ci_includes_zero && r > 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr, lvl_local)) + } else if (abs_big && ci_includes_zero && r < 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (opposers); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", + label, + if (label == "In-degree") "in-degree" else "out-degree", + thr, lvl_local)) + } } - + + explain("In-degree", indeg, indeg_ci) + explain("Out-degree", outdeg, outdeg_ci) invisible(x) } diff --git a/man/bass.Rd b/man/bass.Rd index c6512d3..af305b0 100644 --- a/man/bass.Rd +++ b/man/bass.Rd @@ -138,6 +138,7 @@ Available at: \url{https://web.archive.org/web/20220331222618/http://www.bassbas Other statistics: \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/classify_adopters.Rd b/man/classify_adopters.Rd index 7d32e0a..4d3d41d 100644 --- a/man/classify_adopters.Rd +++ b/man/classify_adopters.Rd @@ -138,6 +138,7 @@ Valente, T. W. (1995). "Network models of the diffusion of innovations" Other statistics: \code{\link{bass}}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/cumulative_adopt_count.Rd b/man/cumulative_adopt_count.Rd index c78f340..61d443b 100644 --- a/man/cumulative_adopt_count.Rd +++ b/man/cumulative_adopt_count.Rd @@ -31,6 +31,7 @@ it is only calculated fot \eqn{t>1}. Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/degree_adoption_diagnostic.Rd b/man/degree_adoption_diagnostic.Rd new file mode 100644 index 0000000..5f8c3dd --- /dev/null +++ b/man/degree_adoption_diagnostic.Rd @@ -0,0 +1,130 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/stats.R +\name{degree_adoption_diagnostic} +\alias{degree_adoption_diagnostic} +\title{Degree and Time of Adoption Diagnostic} +\usage{ +degree_adoption_diagnostic( + graph, + degree_strategy = c("mean", "first", "last"), + bootstrap = TRUE, + R = 1000, + conf.level = 0.95, + valued = getOption("diffnet.valued", FALSE), + toa = NULL, + t0 = NULL, + t1 = NULL, + name = "A diffusion network", + behavior = "Innovation", + ... +) +} +\arguments{ +\item{graph}{A \code{\link{diffnet}} object or a graph data structure (classes include +\code{array} (\eqn{n\times n \times T}{n*n*T}), \code{dgCMatrix} (sparse), +\code{igraph}, etc.; see \link{netdiffuseR-graphs}).} + +\item{degree_strategy}{Character scalar. How to aggregate degree measures across +time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}.} + +\item{bootstrap}{Logical scalar. Whether to compute bootstrap confidence intervals.} + +\item{R}{Integer scalar. Number of bootstrap replicates (default 1000).} + +\item{conf.level}{Numeric scalar. Confidence level for bootstrap intervals (default 0.95).} + +\item{valued}{Logical scalar. Whether to use edge weights in degree calculations.} + +\item{toa}{Integer vector of length \eqn{n} with time of adoption. +Required when \code{graph} is not a \code{diffnet}.} + +\item{t0, t1}{Optional integer scalars defining the first and last observed +periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 +and \code{t1} to \code{max(toa, na.rm=TRUE)}.} + +\item{name, behavior}{Optional character scalars used only when coercing +inputs into a \code{diffnet} object (passed to \code{new_diffnet}).} + +\item{...}{Additional arguments passed on when coercing to \code{diffnet}.} +} +\value{ +A list with components: +\item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} +\item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} +\item{call}{The matched call} +\item{degree_strategy}{The degree aggregation strategy used} +\item{sample_size}{Number of nodes included in the analysis (excludes non-adopters)} +} +\description{ +Analyzes the correlation between in-degree, out-degree, and time of adoption +to identify whether opinion leaders were early adopters (supporters) or late +adopters (opposers). +} +\details{ +This diagnostic function computes correlations between degree centrality measures +(in-degree and out-degree) and time of adoption. Positive correlations suggest +that central actors (opinion leaders) adopted early, while negative correlations +suggest they adopted late. + +The degree measures can be aggregated across time periods in different ways: +\itemize{ + \item \code{"mean"}: Average degree across all time periods + \item \code{"first"}: Degree in the first time period + \item \code{"last"}: Degree in the last time period +} + +The function accepts either a ready-to-use \code{diffnet} or generic graph +inputs plus a \code{toa} vector. When needed, inputs are coerced to a +\code{diffnet} via an internal helper + +When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to +compute bootstrap confidence intervals for the correlations. This helps +assess the statistical significance of the observed correlations. +} +\examples{ +# Basic usage with Korean Family Planning data +data(kfamilyDiffNet) +# Results show correlations and interpretation +result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) +print(result_basics) +print(result_basics$correlations) + +# With bootstrap confidence intervals +result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) +print(result_boot) + +# Different degree aggregation strategies +result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") +print(result_first) + +result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +print(result_last) + +# Using a sparse static adjacency matrix + toa ----------------------------- +\dontrun{ +library(Matrix) +set.seed(1) +A <- rsparsematrix(50, 50, 0.05); A <- abs(A); A@x[A@x>0] <- 1 +toa <- sample(1:6, 50, replace = TRUE) +diag2 <- degree_adoption_diagnostic(A, toa = toa, valued = FALSE) +} + +} +\seealso{ +\code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} + +Other statistics: +\code{\link{bass}}, +\code{\link{classify_adopters}()}, +\code{\link{cumulative_adopt_count}()}, +\code{\link{dgr}()}, +\code{\link{ego_variance}()}, +\code{\link{exposure}()}, +\code{\link{hazard_rate}()}, +\code{\link{infection}()}, +\code{\link{moran}()}, +\code{\link{struct_equiv}()}, +\code{\link{threshold}()}, +\code{\link{vertex_covariate_dist}()} +} +\concept{statistics} diff --git a/man/dgr.Rd b/man/dgr.Rd index cd9c08b..7450590 100644 --- a/man/dgr.Rd +++ b/man/dgr.Rd @@ -104,6 +104,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, \code{\link{hazard_rate}()}, diff --git a/man/ego_variance.Rd b/man/ego_variance.Rd index 5a7d6dc..9560895 100644 --- a/man/ego_variance.Rd +++ b/man/ego_variance.Rd @@ -49,6 +49,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{exposure}()}, \code{\link{hazard_rate}()}, diff --git a/man/exposure.Rd b/man/exposure.Rd index 62ee4cb..82f3531 100644 --- a/man/exposure.Rd +++ b/man/exposure.Rd @@ -278,6 +278,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{hazard_rate}()}, diff --git a/man/hazard_rate.Rd b/man/hazard_rate.Rd index c2f8fcc..0fe42da 100644 --- a/man/hazard_rate.Rd +++ b/man/hazard_rate.Rd @@ -131,6 +131,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/infection.Rd b/man/infection.Rd index 02f2bad..40531e7 100644 --- a/man/infection.Rd +++ b/man/infection.Rd @@ -149,6 +149,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/moran.Rd b/man/moran.Rd index d8b285b..756ab5d 100644 --- a/man/moran.Rd +++ b/man/moran.Rd @@ -65,6 +65,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/struct_equiv.Rd b/man/struct_equiv.Rd index d2495b8..07673ef 100644 --- a/man/struct_equiv.Rd +++ b/man/struct_equiv.Rd @@ -111,6 +111,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/threshold.Rd b/man/threshold.Rd index 8926c02..23032cb 100644 --- a/man/threshold.Rd +++ b/man/threshold.Rd @@ -67,6 +67,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/man/vertex_covariate_dist.Rd b/man/vertex_covariate_dist.Rd index 6cac7bc..9f09489 100644 --- a/man/vertex_covariate_dist.Rd +++ b/man/vertex_covariate_dist.Rd @@ -88,6 +88,7 @@ Other statistics: \code{\link{bass}}, \code{\link{classify_adopters}()}, \code{\link{cumulative_adopt_count}()}, +\code{\link{degree_adoption_diagnostic}()}, \code{\link{dgr}()}, \code{\link{ego_variance}()}, \code{\link{exposure}()}, diff --git a/tests/testthat/test-degree-adoption-diagnostic.R b/tests/testthat/test-degree-adoption-diagnostic.R index ad479e5..0af61ab 100644 --- a/tests/testthat/test-degree-adoption-diagnostic.R +++ b/tests/testthat/test-degree-adoption-diagnostic.R @@ -4,10 +4,10 @@ test_that("degree_adoption_diagnostic works with basic inputs", { # Create a simple test diffnet set.seed(123) dn <- rdiffnet(30, 4, seed.p.adopt = 0.2) - + # Basic test result <- degree_adoption_diagnostic(dn, bootstrap = FALSE) - + expect_s3_class(result, "degree_adoption_diagnostic") expect_named(result, c("correlations", "bootstrap", "call", "degree_strategy", "sample_size")) expect_named(result$correlations, c("indegree_toa", "outdegree_toa")) @@ -21,15 +21,15 @@ test_that("degree_adoption_diagnostic works with basic inputs", { test_that("degree_adoption_diagnostic works with different degree strategies", { set.seed(456) dn <- rdiffnet(25, 3, seed.p.adopt = 0.3) - + result_mean <- degree_adoption_diagnostic(dn, degree_strategy = "mean", bootstrap = FALSE) result_first <- degree_adoption_diagnostic(dn, degree_strategy = "first", bootstrap = FALSE) result_last <- degree_adoption_diagnostic(dn, degree_strategy = "last", bootstrap = FALSE) - + expect_equal(result_mean$degree_strategy, "mean") expect_equal(result_first$degree_strategy, "first") expect_equal(result_last$degree_strategy, "last") - + # Different strategies should potentially give different results expect_true( !all(result_mean$correlations == result_first$correlations) || @@ -40,24 +40,24 @@ test_that("degree_adoption_diagnostic works with different degree strategies", { test_that("degree_adoption_diagnostic bootstrap works", { set.seed(789) dn <- rdiffnet(20, 3, seed.p.adopt = 0.4) - + # Skip if boot package not available skip_if_not_installed("boot") - + result <- degree_adoption_diagnostic(dn, bootstrap = TRUE, R = 50, conf.level = 0.9) - + expect_false(is.null(result$bootstrap)) expect_named(result$bootstrap, c("indegree", "outdegree", "R", "boot_object")) expect_equal(result$bootstrap$R, 50) - + # Check bootstrap structure for indegree - expect_named(result$bootstrap$indegree, + expect_named(result$bootstrap$indegree, c("correlation", "bias", "std_error", "conf_int", "conf_level")) expect_equal(result$bootstrap$indegree$conf_level, 0.9) expect_length(result$bootstrap$indegree$conf_int, 2) - + # Check bootstrap structure for outdegree - expect_named(result$bootstrap$outdegree, + expect_named(result$bootstrap$outdegree, c("correlation", "bias", "std_error", "conf_int", "conf_level")) expect_equal(result$bootstrap$outdegree$conf_level, 0.9) expect_length(result$bootstrap$outdegree$conf_int, 2) @@ -66,20 +66,20 @@ test_that("degree_adoption_diagnostic bootstrap works", { test_that("degree_adoption_diagnostic handles edge cases", { # Test with diffnet that has no adopters set.seed(111) - dn <- rdiffnet(10, 3, seed.p.adopt = 0.0) + dn <- rdiffnet(100, 3, seed.p.adopt = 0.1) dn$toa[] <- NA # Force no adopters - + expect_error( degree_adoption_diagnostic(dn, bootstrap = FALSE), "At least 3 adopters are required" ) - + # Test with insufficient adopters set.seed(222) - dn <- rdiffnet(10, 3, seed.p.adopt = 0.1) + dn <- rdiffnet(100, 3, seed.p.adopt = 0.1) # Force only 2 adopters dn$toa[!is.na(dn$toa)][-(1:2)] <- NA - + expect_error( degree_adoption_diagnostic(dn, bootstrap = FALSE), "At least 3 adopters are required" @@ -89,31 +89,37 @@ test_that("degree_adoption_diagnostic handles edge cases", { test_that("degree_adoption_diagnostic input validation", { set.seed(333) dn <- rdiffnet(15, 3, seed.p.adopt = 0.3) - - # Test non-diffnet input + + # Test invalid toa for non-diffnet input expect_error( degree_adoption_diagnostic("not a diffnet"), - "'graph' must be a diffnet object" + "When 'graph' is not a diffnet, argument 'toa' must be provided." ) - + + # Test valid sparse matrix input + A <- Matrix::rsparsematrix(10,10,0.1); A@x[] <- 1 + toa <- sample(1:3, 10, TRUE) + x <- degree_adoption_diagnostic(A, toa = toa, bootstrap = FALSE) + expect_s3_class(x, "degree_adoption_diagnostic") + # Test invalid degree_strategy expect_error( degree_adoption_diagnostic(dn, degree_strategy = "invalid"), "'arg' should be one of" ) - + # Test invalid bootstrap expect_error( degree_adoption_diagnostic(dn, bootstrap = "yes"), "'bootstrap' must be a logical scalar" ) - + # Test invalid R expect_error( degree_adoption_diagnostic(dn, R = -1), "'R' must be a positive integer" ) - + # Test invalid conf.level expect_error( degree_adoption_diagnostic(dn, conf.level = 1.5), @@ -124,14 +130,14 @@ test_that("degree_adoption_diagnostic input validation", { test_that("degree_adoption_diagnostic works with Korean Family Planning data", { # Test with real data data(kfamilyDiffNet) - + # Basic test with real data result <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) - + expect_s3_class(result, "degree_adoption_diagnostic") expect_gt(result$sample_size, 100) # Korean data should have many adopters expect_true(all(is.finite(result$correlations))) - + # Test that correlations are within valid range expect_true(all(result$correlations >= -1 & result$correlations <= 1)) }) @@ -139,16 +145,16 @@ test_that("degree_adoption_diagnostic works with Korean Family Planning data", { test_that("degree_adoption_diagnostic print method works", { set.seed(444) dn <- rdiffnet(20, 3, seed.p.adopt = 0.3) - + result <- degree_adoption_diagnostic(dn, bootstrap = FALSE) - + # Test that print doesn't error and returns invisibly expect_output(print(result), "Degree and Time of Adoption Diagnostic") expect_output(print(result), "Correlations:") expect_output(print(result), "In-degree") expect_output(print(result), "Out-degree") expect_output(print(result), "Interpretation:") - + # Test that it returns the object invisibly returned <- capture.output(printed_result <- print(result)) expect_identical(result, printed_result) @@ -158,29 +164,29 @@ test_that("degree_adoption_diagnostic reproduces manual calculation", { # Create a simple case where we can manually verify set.seed(555) dn <- rdiffnet(15, 3, seed.p.adopt = 0.4) - + result <- degree_adoption_diagnostic(dn, degree_strategy = "mean", bootstrap = FALSE) - + # Manual calculation to verify adopters <- !is.na(dn$toa) toa_adopters <- dn$toa[adopters] - + # Compute degrees manually indegree_full <- dgr(dn, cmode = "indegree", valued = FALSE) outdegree_full <- dgr(dn, cmode = "outdegree", valued = FALSE) - + indegree_mean <- rowMeans(indegree_full, na.rm = TRUE) outdegree_mean <- rowMeans(outdegree_full, na.rm = TRUE) - + indegree_adopters <- indegree_mean[adopters] outdegree_adopters <- outdegree_mean[adopters] - + # Manual correlations manual_cor_in <- cor(indegree_adopters, toa_adopters, use = "complete.obs") manual_cor_out <- cor(outdegree_adopters, toa_adopters, use = "complete.obs") - + # Compare expect_equal(result$correlations[["indegree_toa"]], manual_cor_in, tolerance = 1e-10) expect_equal(result$correlations[["outdegree_toa"]], manual_cor_out, tolerance = 1e-10) expect_equal(result$sample_size, sum(adopters)) -}) \ No newline at end of file +}) From 32de3fd06ee6b9c6e13b2b773ce2dcbad57c7a61 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Thu, 11 Sep 2025 00:37:22 -0300 Subject: [PATCH 05/18] multi-diffusion support (behavior/combine), undirected network support, safer bootstrap & printer; tests + docs added --- R/stats.R | 741 +++++++++++++----- man/degree_adoption_diagnostic.Rd | 77 +- .../test-degree-adoption-diagnostic.R | 223 +++++- 3 files changed, 797 insertions(+), 244 deletions(-) diff --git a/R/stats.R b/R/stats.R index c069e9d..2c07b16 100644 --- a/R/stats.R +++ b/R/stats.R @@ -1368,13 +1368,21 @@ compare_matrix <- matrix_compare #' @param graph A \code{\link{diffnet}} object or a graph data structure (classes include #' \code{array} (\eqn{n\times n \times T}{n*n*T}), \code{dgCMatrix} (sparse), #' \code{igraph}, etc.; see \link{netdiffuseR-graphs}). -#' @param toa Integer vector of length \eqn{n} with time of adoption. -#' Required when \code{graph} is not a \code{diffnet}. +#' @param toa Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} +#' matrix (multi-behavior) with times of adoption. Required when \code{graph} is not a \code{diffnet}. #' @param t0,t1 Optional integer scalars defining the first and last observed #' periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 #' and \code{t1} to \code{max(toa, na.rm=TRUE)}. -#' @param name,behavior Optional character scalars used only when coercing +#' @param name Optional character scalars used only when coercing #' inputs into a \code{diffnet} object (passed to \code{new_diffnet}). +#' @param behavior Which behaviors to include when \code{toa} is a matrix (multi-diffusion). +#' Can be \code{NULL} (all), a numeric index vector, or a character vector matching \code{colnames(toa)}. +#' @param combine Character scalar. How to combine multiple behaviors when \code{toa} is a matrix: +#' \code{"none"} (analyze each behavior separately), \code{"pooled"} (stack rows across behaviors), +#' \code{"average"} (per-actor mean of TOA across selected behaviors), or +#' \code{"earliest"} (per-actor minimum TOA). Ignored for single-behavior. +#' @param min_adopters Integer scalar. Minimum number of adopters required to compute correlations +#' for any analysis cell (default 3). #' @param degree_strategy Character scalar. How to aggregate degree measures across #' time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}. #' @param bootstrap Logical scalar. Whether to compute bootstrap confidence intervals. @@ -1386,7 +1394,7 @@ compare_matrix <- matrix_compare #' @details #' This diagnostic function computes correlations between degree centrality measures #' (in-degree and out-degree) and time of adoption. Positive correlations suggest -#' that central actors (opinion leaders) adopted early, while negative correlations +#' that central actors (opinion leaders) adopted early, while negative correlations #' suggest they adopted late. #' #' The degree measures can be aggregated across time periods in different ways: @@ -1396,28 +1404,40 @@ compare_matrix <- matrix_compare #' \item \code{"last"}: Degree in the last time period #' } #' -#' The function accepts either a ready-to-use \code{diffnet} or generic graph -#' inputs plus a \code{toa} vector. When needed, inputs are coerced to a -#' \code{diffnet} via an internal helper -#' #' When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to -#' compute bootstrap confidence intervals for the correlations. This helps -#' assess the statistical significance of the observed correlations. +#' compute bootstrap confidence intervals for the correlations. +#' +#' When \code{toa} is a matrix (multi-diffusion), degree vectors are computed once and +#' reused; the time of adoption is combined according to \code{combine}: +#' \itemize{ +#' \item \code{"none"}: computes separate results per behavior (see Value). +#' \item \code{"pooled"}: stacks (actor, behavior) rows for adopters and runs a single analysis. +#' \item \code{"average"}: one row per actor using the mean TOA of adopted behaviors. +#' \item \code{"earliest"}: one row per actor using the minimum TOA of adopted behaviors. +#' } #' -#' @return A list with components: +#' @return When analyzing a single behavior (or when \code{combine!="none"}), a list with: #' \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} #' \item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} #' \item{call}{The matched call} #' \item{degree_strategy}{The degree aggregation strategy used} -#' \item{sample_size}{Number of nodes included in the analysis (excludes non-adopters)} +#' \item{sample_size}{Number of rows included in the analysis (adopter rows)} +#' \item{combine}{\code{NULL} for single-behavior; otherwise the combination rule used.} +#' +#' When \code{combine="none"} with multiple behaviors, returns the same structure, except: +#' \itemize{ +#' \item \code{correlations} is a \eqn{2\times Q^*}{2 x Q*} matrix with rows \code{c("indegree_toa","outdegree_toa")} +#' and one column per analyzed behavior. +#' \item \code{bootstrap} is a named list with one entry per behavior (each like the single-behavior case), or \code{NULL} if \code{bootstrap=FALSE}. +#' \item \code{sample_size} is an integer vector named by behavior. +#' \item \code{combine} is \code{"none"}. +#' } #' #' @examples #' # Basic usage with Korean Family Planning data #' data(kfamilyDiffNet) -#' # Results show correlations and interpretation #' result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) #' print(result_basics) -#' print(result_basics$correlations) #' #' # With bootstrap confidence intervals #' result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) @@ -1425,18 +1445,20 @@ compare_matrix <- matrix_compare #' #' # Different degree aggregation strategies #' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") -#' print(result_first) +#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") #' -#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") -#' print(result_last) -#' -#' # Using a sparse static adjacency matrix + toa ----------------------------- +#' # Multi-diffusion (toy) ---------------------------------------------------- #' \dontrun{ -#' library(Matrix) -#' set.seed(1) -#' A <- rsparsematrix(50, 50, 0.05); A <- abs(A); A@x[A@x>0] <- 1 -#' toa <- sample(1:6, 50, replace = TRUE) -#' diag2 <- degree_adoption_diagnostic(A, toa = toa, valued = FALSE) +#' set.seed(999) +#' n <- 40; t <- 5; q <- 2 +#' garr <- rgraph_ws(n, t, p=.3) +#' diffnet_multi <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.1), q)) +#' +#' # pooled (one combined analysis) +#' degree_adoption_diagnostic(diffnet_multi, combine = "pooled", bootstrap = FALSE) +#' +#' # per-behavior (matrix of correlations; one column per behavior) +#' degree_adoption_diagnostic(diffnet_multi, combine = "none", bootstrap = FALSE) #' } #' #' @seealso \code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} @@ -1453,19 +1475,26 @@ degree_adoption_diagnostic <- function( t0 = NULL, t1 = NULL, name = "A diffusion network", - behavior = "Innovation", + behavior = NULL, + combine = c("none","pooled","average","earliest"), + min_adopters = 3L, ... -) { +) +{ # Coerce to diffnet if needed ---------------------------------------------- if (!inherits(graph, "diffnet")) { if (is.null(toa)) stop("When 'graph' is not a diffnet, argument 'toa' must be provided.", call. = FALSE) - dn <- .ndd_coerce_to_diffnet(graph, toa = toa, t0 = t0, t1 = t1, - name = name, behavior = behavior, valued = valued, ...) + dn <- .ndd_coerce_to_diffnet( + graph, toa = toa, t0 = t0, t1 = t1, + name = name, behavior = behavior, + valued = valued, ... + ) graph <- dn } degree_strategy <- match.arg(degree_strategy) + combine <- match.arg(combine) if (!is.logical(bootstrap) || length(bootstrap) != 1) stop("'bootstrap' must be a logical scalar.", call. = FALSE) @@ -1473,162 +1502,322 @@ degree_adoption_diagnostic <- function( stop("'R' must be a positive integer.", call. = FALSE) if (!is.numeric(conf.level) || length(conf.level) != 1 || conf.level <= 0 || conf.level >= 1) stop("'conf.level' must be between 0 and 1.", call. = FALSE) + if (!is.numeric(min_adopters) || length(min_adopters) != 1 || min_adopters < 1) + stop("'min_adopters' must be a positive integer.", call. = FALSE) - # Extract time of adoption and keep adopters -------------------------------- - toa <- graph$toa - adopters <- !is.na(toa) - if (sum(adopters) < 3) - stop("At least 3 adopters are required for correlation analysis.", call. = FALSE) - toa_adopters <- toa[adopters] + # Extract TOA (vector or matrix) ------------------------------------------- + toa_obj <- graph$toa + is_multi <- is.matrix(toa_obj) - # Degree measures ----------------------------------------------------------- + # Degree measures (aggregated once) ---------------------------------------- indegree_full <- dgr(graph, cmode = "indegree", valued = valued) outdegree_full <- dgr(graph, cmode = "outdegree", valued = valued) + # Detect undirected: either meta flag or indegree==outdegree everywhere + undirected_flag <- isTRUE(graph$meta$undirected) + if (!undirected_flag) { + if (identical(dim(indegree_full), dim(outdegree_full))) { + undirected_flag <- all(indegree_full == outdegree_full, na.rm = TRUE) + } + } + + # Aggregate degrees according to strategy if (degree_strategy == "mean") { indegree_agg <- rowMeans(indegree_full, na.rm = TRUE) outdegree_agg <- rowMeans(outdegree_full, na.rm = TRUE) } else if (degree_strategy == "first") { indegree_agg <- indegree_full[, 1] outdegree_agg <- outdegree_full[, 1] - } else { + } else { # "last" indegree_agg <- indegree_full[, ncol(indegree_full)] outdegree_agg <- outdegree_full[, ncol(outdegree_full)] } - indegree_adopters <- indegree_agg[adopters] - outdegree_adopters <- outdegree_agg[adopters] - - cor_indegree_toa <- stats::cor(indegree_adopters, toa_adopters, use = "complete.obs") - cor_outdegree_toa <- stats::cor(outdegree_adopters, toa_adopters, use = "complete.obs") + # If undirected, collapse to a single degree vector (use "degree") + if (undirected_flag) { + degree_full <- dgr(graph, cmode = "degree", valued = valued) + degree_agg <- switch(degree_strategy, + mean = rowMeans(degree_full, na.rm = TRUE), + first = degree_full[, 1], + last = degree_full[, ncol(degree_full)] + ) + indegree_agg <- degree_agg + outdegree_agg <- degree_agg + } - correlations <- c( - indegree_toa = cor_indegree_toa, - outdegree_toa = cor_outdegree_toa - ) + # Safe correlation (avoid warnings on zero variance) + cor_safely <- function(x, y) { + x <- as.numeric(x); y <- as.numeric(y) + if (sum(stats::complete.cases(x, y)) < 2) return(NA_real_) + sx <- stats::sd(x, na.rm = TRUE); sy <- stats::sd(y, na.rm = TRUE) + if (!is.finite(sx) || !is.finite(sy) || sx == 0 || sy == 0) return(NA_real_) + stats::cor(x, y, use = "complete.obs") + } - # Bootstrap analysis -------------------------------------------------------- - bootstrap_results <- NULL - if (bootstrap) { + # Helper: bootstrap one dataset (3 cols: indeg, outdeg, toa) + boot_one <- function(boot_data) { boot_cor <- function(data, indices) { - d <- data[indices, ] - c( - stats::cor(d[, 1], d[, 3], use = "complete.obs"), # indegree-toa correlation - stats::cor(d[, 2], d[, 3], use = "complete.obs") # outdegree-toa correlation - ) + d <- data[indices, , drop = FALSE] + r1 <- cor_safely(d[, 1], d[, 3]) + r2 <- cor_safely(d[, 2], d[, 3]) + c(r1, r2) } - - # Prepare data for bootstrap - boot_data <- cbind(indegree_adopters, outdegree_adopters, toa_adopters) - complete_cases <- stats::complete.cases(boot_data) - boot_data <- boot_data[complete_cases, , drop = FALSE] - - if (nrow(boot_data) < 3) { - warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) - } else { - # Perform bootstrap - boot_result <- boot::boot(boot_data, boot_cor, R = R) - tryCatch({ - ci_indegree <- boot::boot.ci(boot_result, type = "perc", index = 1, conf = conf.level) - ci_outdegree <- boot::boot.ci(boot_result, type = "perc", index = 2, conf = conf.level) - bootstrap_results <- list( - indegree = list( - correlation = boot_result$t0[1], - bias = mean(boot_result$t[, 1]) - boot_result$t0[1], - std_error = stats::sd(boot_result$t[, 1]), - conf_int = ci_indegree$percent[4:5], - conf_level = conf.level - ), - outdegree = list( - correlation = boot_result$t0[2], - bias = mean(boot_result$t[, 2]) - boot_result$t0[2], - std_error = stats::sd(boot_result$t[, 2]), - conf_int = ci_outdegree$percent[4:5], - conf_level = conf.level - ), - R = R, - boot_object = boot_result - ) - }, error = function(e) { - warning("Bootstrap confidence intervals could not be computed: ", e$message, call. = FALSE) - bootstrap_results <<- list( - indegree = list( - correlation = boot_result$t0[1], - bias = mean(boot_result$t[, 1]) - boot_result$t0[1], - std_error = stats::sd(boot_result$t[, 1]) - ), - outdegree = list( - correlation = boot_result$t0[2], - bias = mean(boot_result$t[, 2]) - boot_result$t0[2], - std_error = stats::sd(boot_result$t[, 2]) - ), - R = R, - boot_object = boot_result - ) + boot_result <- boot::boot(boot_data, boot_cor, R = R) + + # Post-process allowing NAs in replicates + mk <- function(idx) { + t0 <- boot_result$t0[idx] + tv <- boot_result$t[, idx] + tv <- tv[is.finite(tv)] + out <- list(correlation = t0) + if (length(tv) >= 1) out$bias <- mean(tv) - t0 + if (length(tv) >= 2) out$std_error <- stats::sd(tv) + if (length(tv) >= 3) { + alpha <- (1 - conf.level)/2 + qs <- stats::quantile(tv, probs = c(alpha, 1 - alpha), na.rm = TRUE, names = FALSE, type = 7) + out$conf_int <- as.numeric(qs) + out$conf_level <- conf.level + } + out + } + + list( + R = R, + boot_object = boot_result, + indegree = mk(1), + outdegree = mk(2) + ) + } + + # ---------- SINGLE BEHAVIOR PATH ---------- + if (!is_multi) { + adopters <- !is.na(toa_obj) + if (sum(adopters) < min_adopters) + stop("At least ", min_adopters, " adopters are required for correlation analysis.", call. = FALSE) + + toa_adopters <- toa_obj[adopters] + indegree_adopters <- indegree_agg[adopters] + outdegree_adopters <- outdegree_agg[adopters] + + cor_indegree_toa <- cor_safely(indegree_adopters, toa_adopters) + cor_outdegree_toa <- cor_safely(outdegree_adopters, toa_adopters) + + correlations <- c(indegree_toa = cor_indegree_toa, + outdegree_toa = cor_outdegree_toa) + + bootstrap_results <- NULL + if (bootstrap) { + boot_data <- cbind(indegree_adopters, outdegree_adopters, toa_adopters) + boot_data <- boot_data[stats::complete.cases(boot_data), , drop = FALSE] + if (nrow(boot_data) >= min_adopters) { + bootstrap_results <- boot_one(boot_data) + } else { + warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) + } + } + + return(structure(list( + correlations = correlations, + bootstrap = bootstrap_results, + call = match.call(), + degree_strategy = degree_strategy, + sample_size = sum(adopters), + combine = NULL, + undirected = undirected_flag + ), class = "degree_adoption_diagnostic")) + } + + # ---------- MULTI-BEHAVIOR PATH ---------- + Q <- ncol(toa_obj) + beh_names <- colnames(toa_obj) + if (is.null(beh_names)) beh_names <- paste0("B", seq_len(Q)) + + # Resolve behavior subset + if (is.null(behavior)) { + q_set <- seq_len(Q) + } else if (is.numeric(behavior)) { + q_set <- as.integer(behavior) + if (any(q_set < 1 | q_set > Q)) stop("Some 'behavior' indices are out of range 1..", Q, ".") + } else if (is.character(behavior)) { + mm <- match(behavior, beh_names) + if (anyNA(mm)) stop("Some 'behavior' names not found in TOA colnames.") + q_set <- mm + } else { + stop("'behavior' must be NULL, numeric indices, or character names.") + } + beh_names <- beh_names[q_set] + + # Build analysis dataset depending on 'combine' + make_boot_data <- switch( + combine, + pooled = function() { + rows <- lapply(q_set, function(q) { + a <- which(!is.na(toa_obj[, q])) + if (length(a) == 0L) return(NULL) + cbind(indegree_agg[a], outdegree_agg[a], toa_obj[a, q]) + }) + do.call(rbind, rows) + }, + average = function() { + toa_mean <- rowMeans(toa_obj[, q_set, drop = FALSE], na.rm = TRUE) + adopters_any <- rowSums(!is.na(toa_obj[, q_set, drop = FALSE])) > 0 + a <- which(adopters_any & !is.nan(toa_mean)) + if (!length(a)) return(NULL) + cbind(indegree_agg[a], outdegree_agg[a], toa_mean[a]) + }, + earliest = function() { + toa_min <- apply(toa_obj[, q_set, drop = FALSE], 1, function(v) { + v <- v[!is.na(v)] + if (!length(v)) return(Inf) + min(v) }) + adopters_any <- is.finite(toa_min) + a <- which(adopters_any) + if (!length(a)) return(NULL) + cbind(indegree_agg[a], outdegree_agg[a], toa_min[a]) + }, + none = NULL + ) + + if (combine != "none") { + boot_data <- make_boot_data() + if (is.null(boot_data) || nrow(boot_data) < min_adopters) + stop("Not enough adopter rows after combining behaviors (combine='", combine, "').", call. = FALSE) + + cor_indegree_toa <- cor_safely(boot_data[,1], boot_data[,3]) + cor_outdegree_toa <- cor_safely(boot_data[,2], boot_data[,3]) + correlations <- c(indegree_toa = cor_indegree_toa, + outdegree_toa = cor_outdegree_toa) + + bootstrap_results <- NULL + if (bootstrap) { + boot_data2 <- boot_data[stats::complete.cases(boot_data), , drop = FALSE] + if (nrow(boot_data2) >= min_adopters) { + bootstrap_results <- boot_one(boot_data2) + } else { + warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) + } + } + + return(structure(list( + correlations = correlations, + bootstrap = bootstrap_results, + call = match.call(), + degree_strategy = degree_strategy, + sample_size = nrow(boot_data), + combine = combine, + undirected = undirected_flag + ), class = "degree_adoption_diagnostic")) + } + + # combine == "none": per-behavior results --------------------------------- + K <- length(q_set) + corr_mat <- matrix(NA_real_, nrow = 2, ncol = K, + dimnames = list(c("indegree_toa","outdegree_toa"), beh_names)) + boot_list <- if (bootstrap) vector("list", K) else NULL + n_vec <- integer(K); names(n_vec) <- beh_names + + for (j in seq_along(q_set)) { + q <- q_set[j] + adopters <- !is.na(toa_obj[, q]) + n_vec[j] <- sum(adopters) + if (n_vec[j] < min_adopters) next + + toa_q <- toa_obj[adopters, q] + indeg_q <- indegree_agg[adopters] + outdeg_q <- outdegree_agg[adopters] + + corr_mat["indegree_toa", j] <- cor_safely(indeg_q, toa_q) + corr_mat["outdegree_toa", j] <- cor_safely(outdeg_q, toa_q) + + if (bootstrap) { + bd <- cbind(indeg_q, outdeg_q, toa_q) + bd <- bd[stats::complete.cases(bd), , drop = FALSE] + if (nrow(bd) >= min_adopters) boot_list[[j]] <- boot_one(bd) } } structure(list( - correlations = correlations, - bootstrap = bootstrap_results, - call = match.call(), - degree_strategy = degree_strategy, - sample_size = sum(adopters) + correlations = corr_mat, + bootstrap = boot_list, + call = match.call(), + degree_strategy = degree_strategy, + sample_size = n_vec, + combine = "none", + undirected = undirected_flag ), class = "degree_adoption_diagnostic") } # Internal: Coerce to diffnet from common graph inputs ------------------------ .ndd_coerce_to_diffnet <- function(graph, toa, t0 = NULL, t1 = NULL, name = "A diffusion network", - behavior = "Innovation", + behavior = NULL, valued = getOption("diffnet.valued", FALSE), ...) { - # infer periods + # infer periods from TOA when not provided if (is.null(t0)) t0 <- 1L if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) + Tlen_target <- max(1L, as.integer(t1 - t0 + 1L)) - # build list of adjacency matrices (length T) in dgCMatrix format - to_list_dgC <- function(g1) { - if (inherits(g1, "dgCMatrix")) { - G <- g1 - } else if (inherits(g1, "matrix")) { - G <- methods::as(g1, "dgCMatrix") - } else if (inherits(g1, "igraph")) { - G <- methods::as(igraph::as_adjacency_matrix(g1, sparse = TRUE), "dgCMatrix") - } else if (inherits(g1, "network")) { - tmp <- as_generic_graph.network(g1) - G <- tmp$graph[[1]] - } else if (inherits(g1, "list")) { - return(lapply(g1, to_list_dgC)) - } else if (inherits(g1, "array")) { - return(lapply(seq_len(dim(g1)[3]), function(i) methods::as(g1[, , i], "dgCMatrix"))) + # If behavior not provided, derive a sensible default + if (is.null(behavior)) { + if (is.matrix(toa)) { + behavior <- if (!is.null(colnames(toa))) colnames(toa) else paste0("behavior_", seq_len(ncol(toa))) } else { - stop("Unsupported graph class for coercion.") + behavior <- "Innovation" } + } + # Helper: coerce a single graph slice to dgCMatrix + as_slice_dgC <- function(g) { + if (inherits(g, "dgCMatrix")) { + G <- g + } else if (inherits(g, "matrix")) { + G <- methods::as(g, "dgCMatrix") + } else if (inherits(g, "igraph")) { + G <- methods::as(igraph::as_adjacency_matrix(g, sparse = TRUE), "dgCMatrix") + } else if (inherits(g, "network")) { + tmp <- as_generic_graph.network(g) + G <- tmp$graph[[1]] + } else { + stop("Unsupported graph slice class for coercion.") + } if (!valued) G@x <- rep(1, length(G@x)) - list(G) + G } - Glist <- if (inherits(graph, "list")) unlist(lapply(graph, to_list_dgC), recursive = FALSE) - else if (inherits(graph, "array")) lapply(seq_len(dim(graph)[3]), function(i) methods::as(graph[, , i], "dgCMatrix")) - else to_list_dgC(graph) + # Build list of time slices (each a dgCMatrix) + if (inherits(graph, "list")) { + Glist <- lapply(graph, as_slice_dgC) + } else if (inherits(graph, "array")) { + if (length(dim(graph)) != 3L || dim(graph)[1] != dim(graph)[2]) + stop("When 'graph' is an array it must be n x n x T.") + Glist <- lapply(seq_len(dim(graph)[3]), function(i) methods::as(graph[, , i], "dgCMatrix")) + if (!valued) Glist <- lapply(Glist, function(G) { G@x <- rep(1, length(G@x)); G }) + } else { + # Single static graph: make one slice + Glist <- list(as_slice_dgC(graph)) + } - # If only one slice provided, repeat across [t0, t1] - Tlen <- length(Glist) - if (Tlen == 1L && (t1 - t0 + 1L) > 1L) { - Glist <- rep(Glist, t1 - t0 + 1L) + # If only one slice provided but multiple periods implied by TOA, repeat it + if (length(Glist) == 1L && Tlen_target > 1L) { + Glist <- rep(Glist, Tlen_target) names(Glist) <- as.character(seq.int(t0, t1)) } - # Align vertex names (if any) with toa names + # Sanity check + if (!all(vapply(Glist, function(x) inherits(x, "dgCMatrix"), logical(1)))) + stop("All graph slices must be coercible to 'dgCMatrix'.") + + # Align vertex names with TOA (vector or matrix) rn <- rownames(Glist[[1]]) - if (!is.null(names(toa)) && length(rn) && !identical(names(toa), rn)) { - # try to reorder by names - ord <- match(rn, names(toa)) - if (anyNA(ord)) stop("Names in 'toa' do not match graph vertex names.") - toa <- toa[ord] + if (length(rn)) { + toa_names <- if (is.matrix(toa)) rownames(toa) else names(toa) + if (!is.null(toa_names) && !identical(toa_names, rn)) { + ord <- match(rn, toa_names) + if (anyNA(ord)) stop("Names in 'toa' do not match graph vertex names.") + if (is.matrix(toa)) toa <- toa[ord, , drop = FALSE] else toa <- toa[ord] + } } new_diffnet(graph = Glist, toa = toa, t0 = t0, t1 = t1, @@ -1639,61 +1828,26 @@ degree_adoption_diagnostic <- function( print.degree_adoption_diagnostic <- function(x, ...) { cat("Degree and Time of Adoption Diagnostic\n") cat("======================================\n\n") - + cat("Degree aggregation strategy:", x$degree_strategy, "\n") - cat("Sample size (adopters only):", x$sample_size, "\n\n") - - cat("Correlations:\n") - cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", x$correlations[["indegree_toa"]])) - cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", x$correlations[["outdegree_toa"]])) - - if (!is.null(x$bootstrap)) { - cat("\nBootstrap Results:\n") - cat(sprintf(" Replicates: %d\n", x$bootstrap$R)) - cat(sprintf(" Confidence level: %.1f%%\n\n", x$bootstrap$indegree$conf_level * 100)) - - if (!is.null(x$bootstrap$indegree$conf_int)) { - cat(" In-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) - cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) - cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", - x$bootstrap$indegree$conf_level * 100, - x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) - - cat(" Out-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) - cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) - cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", - x$bootstrap$outdegree$conf_level * 100, - x$bootstrap$outdegree$conf_int[1], x$bootstrap$outdegree$conf_int[2])) - } else { - cat(" Bootstrap estimates (CI unavailable):\n") - cat(sprintf(" In-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n", - x$bootstrap$indegree$correlation, - x$bootstrap$indegree$bias, - x$bootstrap$indegree$std_error)) - cat(sprintf(" Out-degree: Est=%6.3f, Bias=%6.3f, SE=%6.3f\n\n", - x$bootstrap$outdegree$correlation, - x$bootstrap$outdegree$bias, - x$bootstrap$outdegree$std_error)) + + # Sample size can be a scalar or a vector (per behavior when combine='none') + if (length(x$sample_size) == 1L) { + cat("Sample size (adopters only):", x$sample_size, "\n\n") + } else { + cat("Sample size (adopters only): total =", sum(x$sample_size, na.rm = TRUE), "\n") + cat(" By behavior:\n") + beh_names <- names(x$sample_size) + for (j in seq_along(x$sample_size)) { + cat(sprintf(" - %s: %d\n", if (length(beh_names)) beh_names[j] else paste0("B", j), x$sample_size[j])) } + cat("\n") } - # Concise CI-aware interpretation ------------------------------------------ - cat("Interpretation:\n") - thr <- 0.10 - indeg <- x$correlations[["indegree_toa"]] - outdeg <- x$correlations[["outdegree_toa"]] - - has_boot <- !is.null(x$bootstrap) - indeg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL - outdeg_ci <- if (has_boot && !is.null(x$bootstrap$outdegree$conf_int)) x$bootstrap$outdegree$conf_int else NULL - lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level*100 else NA_real_ + undirected <- isTRUE(x$undirected) - # helper to print a single line following the required phrasing - explain <- function(label, r, ci) { + # Helper used in both modes ------------------------------------------------ + explain <- function(label, r, ci, lvl_arg = NA_real_, thr = 0.10) { # Handle NA correlations gracefully if (is.na(r)) { if (is.null(ci)) { @@ -1702,8 +1856,7 @@ print.degree_adoption_diagnostic <- function(x, ...) { label )) } else { - # if CI exists but r is NA, we still treat as inconclusive text-wise - lvl_local <- if (!is.na(lvl)) lvl else 95 + lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) cat(sprintf( " %s: Weak relationship between centrality and adoption timing; %s statistically supported:\n r is NA; CI (%.1f%%) %s 0.\n", @@ -1722,17 +1875,17 @@ print.degree_adoption_diagnostic <- function(x, ...) { if (!abs_big) { cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \u2264 %.1f; no CI.\n", label, - if (label == "In-degree") "in-degree" else "in-degree", # text requested by user examples + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", thr)) } else if (r > 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", thr)) } else { cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers):\n |r| > %.1f; no CI.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", thr)) } return(invisible()) @@ -1740,39 +1893,213 @@ print.degree_adoption_diagnostic <- function(x, ...) { # CI available ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) - lvl_local <- if (!is.na(lvl)) lvl else 95 + lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 if (!abs_big && !ci_includes_zero) { cat(sprintf(" %s: Weak relationship between centrality and adoption timing; statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) excludes 0.\n", - label, thr, lvl_local)) + label, 0.10, lvl_local)) } else if (!abs_big && ci_includes_zero) { cat(sprintf(" %s: Weak relationship between centrality and adoption timing; NOT statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) includes 0.\n", - label, thr, lvl_local)) + label, 0.10, lvl_local)) } else if (abs_big && !ci_includes_zero && r > 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", - thr, lvl_local)) + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", + 0.10, lvl_local)) } else if (abs_big && !ci_includes_zero && r < 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", - thr, lvl_local)) + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", + 0.10, lvl_local)) } else if (abs_big && ci_includes_zero && r > 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", - thr, lvl_local)) + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", + 0.10, lvl_local)) } else if (abs_big && ci_includes_zero && r < 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt early (opposers); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", + cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", label, - if (label == "In-degree") "in-degree" else "out-degree", - thr, lvl_local)) + if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", + 0.10, lvl_local)) + } + } + + # Branch on combine mode --------------------------------------------------- + if (is.null(x$combine) || x$combine != "none") { + # -------- single behavior OR combined pooled/average/earliest ---------- + cat("\nCorrelations:\n") + if (undirected) { + rdeg <- x$correlations[["indegree_toa"]] # same as outdegree_toa + cat(sprintf(" Degree - Time of Adoption: %6.3f\n", rdeg)) + } else { + cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", x$correlations[["indegree_toa"]])) + cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", x$correlations[["outdegree_toa"]])) + } + + if (!is.null(x$bootstrap)) { + cat("\nBootstrap Results:\n") + cat(sprintf(" Replicates: %d\n", x$bootstrap$R)) + if (!is.null(x$bootstrap$indegree$conf_level)) + cat(sprintf(" Confidence level: %.1f%%\n\n", x$bootstrap$indegree$conf_level * 100)) + + if (!is.null(x$bootstrap$indegree$conf_int)) { + if (undirected) { + cat(" Degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) + if (!is.null(x$bootstrap$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) + if (!is.null(x$bootstrap$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", + x$bootstrap$indegree$conf_level * 100, + x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) + } else { + cat(" In-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) + if (!is.null(x$bootstrap$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) + if (!is.null(x$bootstrap$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", + x$bootstrap$indegree$conf_level * 100, + x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) + + if (!is.null(x$bootstrap$outdegree$conf_int)) { + cat(" Out-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) + if (!is.null(x$bootstrap$outdegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) + if (!is.null(x$bootstrap$outdegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", + x$bootstrap$outdegree$conf_level * 100, + x$bootstrap$outdegree$conf_int[1], x$bootstrap$outdegree$conf_int[2])) + } + } + } else { + if (undirected) { + cat(" Bootstrap estimates (CI unavailable):\n") + cat(sprintf(" Degree: Est=%6.3f%s%s\n", + x$bootstrap$indegree$correlation, + if (!is.null(x$bootstrap$indegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$indegree$bias)) else "", + if (!is.null(x$bootstrap$indegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$indegree$std_error)) else "")) + cat("\n") + } else { + cat(" Bootstrap estimates (CI unavailable):\n") + cat(sprintf(" In-degree: Est=%6.3f%s%s\n", + x$bootstrap$indegree$correlation, + if (!is.null(x$bootstrap$indegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$indegree$bias)) else "", + if (!is.null(x$bootstrap$indegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$indegree$std_error)) else "")) + cat(sprintf(" Out-degree: Est=%6.3f%s%s\n\n", + x$bootstrap$outdegree$correlation, + if (!is.null(x$bootstrap$outdegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$outdegree$bias)) else "", + if (!is.null(x$bootstrap$outdegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$outdegree$std_error)) else "")) + } + } + } + + cat("Interpretation:\n") + if (undirected) { + rdeg <- x$correlations[["indegree_toa"]] + has_boot <- !is.null(x$bootstrap) + deg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL + lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level * 100 else NA_real_ + explain("Degree", rdeg, deg_ci, lvl_arg = lvl) + } else { + indeg <- x$correlations[["indegree_toa"]] + outdeg <- x$correlations[["outdegree_toa"]] + has_boot <- !is.null(x$bootstrap) + indeg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL + outdeg_ci <- if (has_boot && !is.null(x$bootstrap$outdegree$conf_int)) x$bootstrap$outdegree$conf_int else NULL + lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level * 100 else NA_real_ + explain("In-degree", indeg, indeg_ci, lvl_arg = lvl) + explain("Out-degree", outdeg, outdeg_ci, lvl_arg = lvl) + } + invisible(x) + return(invisible(x)) + } + + # -------------------------- combine == "none" ----------------------------- + cat("Combination mode:", "none (per behavior)\n\n") + + # Correlations section + cat("Correlations (per behavior):\n") + beh_names <- colnames(x$correlations) + for (j in seq_len(ncol(x$correlations))) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + cat(sprintf(" [%s]\n", bname)) + if (undirected) { + rdeg <- x$correlations["indegree_toa", j] + cat(sprintf(" Degree - Time of Adoption: %6.3f\n", rdeg)) + } else { + r_in <- x$correlations["indegree_toa", j] + r_out <- x$correlations["outdegree_toa", j] + cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", r_in)) + cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", r_out)) + } + } + cat("\n") + + # Bootstrap section (if any) + if (!is.null(x$bootstrap)) { + any_boot <- any(vapply(x$bootstrap, function(b) !is.null(b), logical(1))) + if (any_boot) { + cat("Bootstrap Results (per behavior):\n") + for (j in seq_len(ncol(x$correlations))) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + b <- x$bootstrap[[j]] + if (is.null(b)) next + cat(sprintf(" [%s]\n", bname)) + if (!is.null(b$R)) cat(sprintf(" Replicates: %d\n", b$R)) + if (!is.null(b$indegree$conf_level)) cat(sprintf(" Confidence level: %.1f%%\n", b$indegree$conf_level * 100)) + if (!is.null(b$indegree$conf_int)) { + if (undirected) { + cat(" Degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", b$indegree$correlation)) + if (!is.null(b$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$indegree$bias)) + if (!is.null(b$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$indegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", + b$indegree$conf_level * 100, b$indegree$conf_int[1], b$indegree$conf_int[2])) + } else { + cat(" In-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", b$indegree$correlation)) + if (!is.null(b$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$indegree$bias)) + if (!is.null(b$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$indegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", + b$indegree$conf_level * 100, b$indegree$conf_int[1], b$indegree$conf_int[2])) + } + } + if (!undirected && !is.null(b$outdegree$conf_int)) { + cat(" Out-degree correlation:\n") + cat(sprintf(" Estimate: %6.3f\n", b$outdegree$correlation)) + if (!is.null(b$outdegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$outdegree$bias)) + if (!is.null(b$outdegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$outdegree$std_error)) + cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", + b$outdegree$conf_level * 100, b$outdegree$conf_int[1], b$outdegree$conf_int[2])) + } + } + cat("\n") + } + } + + # Interpretation (per behavior) + cat("Interpretation (per behavior):\n") + for (j in seq_len(ncol(x$correlations))) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + if (undirected) { + rdeg <- x$correlations["indegree_toa", j] + b <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL + deg_ci <- if (!is.null(b) && !is.null(b$indegree$conf_int)) b$indegree$conf_int else NULL + lvl <- if (!is.null(b) && !is.null(b$indegree$conf_level)) b$indegree$conf_level * 100 else NA_real_ + cat(sprintf(" [%s]\n", bname)) + explain("Degree", rdeg, deg_ci, lvl_arg = lvl) + } else { + r_in <- x$correlations["indegree_toa", j] + r_out <- x$correlations["outdegree_toa", j] + b <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL + indeg_ci <- if (!is.null(b) && !is.null(b$indegree$conf_int)) b$indegree$conf_int else NULL + outdeg_ci <- if (!is.null(b) && !is.null(b$outdegree$conf_int)) b$outdegree$conf_int else NULL + lvl <- if (!is.null(b) && !is.null(b$indegree$conf_level)) b$indegree$conf_level * 100 else NA_real_ + cat(sprintf(" [%s]\n", bname)) + explain("In-degree", r_in, indeg_ci, lvl_arg = lvl) + explain("Out-degree", r_out, outdeg_ci, lvl_arg = lvl) } } - explain("In-degree", indeg, indeg_ci) - explain("Out-degree", outdeg, outdeg_ci) invisible(x) } diff --git a/man/degree_adoption_diagnostic.Rd b/man/degree_adoption_diagnostic.Rd index 5f8c3dd..a553e4d 100644 --- a/man/degree_adoption_diagnostic.Rd +++ b/man/degree_adoption_diagnostic.Rd @@ -15,7 +15,9 @@ degree_adoption_diagnostic( t0 = NULL, t1 = NULL, name = "A diffusion network", - behavior = "Innovation", + behavior = NULL, + combine = c("none", "pooled", "average", "earliest"), + min_adopters = 3L, ... ) } @@ -35,25 +37,46 @@ time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}.} \item{valued}{Logical scalar. Whether to use edge weights in degree calculations.} -\item{toa}{Integer vector of length \eqn{n} with time of adoption. -Required when \code{graph} is not a \code{diffnet}.} +\item{toa}{Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} +matrix (multi-behavior) with times of adoption. Required when \code{graph} is not a \code{diffnet}.} \item{t0, t1}{Optional integer scalars defining the first and last observed periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 and \code{t1} to \code{max(toa, na.rm=TRUE)}.} -\item{name, behavior}{Optional character scalars used only when coercing +\item{name}{Optional character scalars used only when coercing inputs into a \code{diffnet} object (passed to \code{new_diffnet}).} +\item{behavior}{Which behaviors to include when \code{toa} is a matrix (multi-diffusion). +Can be \code{NULL} (all), a numeric index vector, or a character vector matching \code{colnames(toa)}.} + +\item{combine}{Character scalar. How to combine multiple behaviors when \code{toa} is a matrix: +\code{"none"} (analyze each behavior separately), \code{"pooled"} (stack rows across behaviors), +\code{"average"} (per-actor mean of TOA across selected behaviors), or +\code{"earliest"} (per-actor minimum TOA). Ignored for single-behavior.} + +\item{min_adopters}{Integer scalar. Minimum number of adopters required to compute correlations +for any analysis cell (default 3).} + \item{...}{Additional arguments passed on when coercing to \code{diffnet}.} } \value{ -A list with components: +When analyzing a single behavior (or when \code{combine!="none"}), a list with: \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} \item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} \item{call}{The matched call} \item{degree_strategy}{The degree aggregation strategy used} -\item{sample_size}{Number of nodes included in the analysis (excludes non-adopters)} +\item{sample_size}{Number of rows included in the analysis (adopter rows)} +\item{combine}{\code{NULL} for single-behavior; otherwise the combination rule used.} + +When \code{combine="none"} with multiple behaviors, returns the same structure, except: +\itemize{ + \item \code{correlations} is a \eqn{2\times Q^*}{2 x Q*} matrix with rows \code{c("indegree_toa","outdegree_toa")} + and one column per analyzed behavior. + \item \code{bootstrap} is a named list with one entry per behavior (each like the single-behavior case), or \code{NULL} if \code{bootstrap=FALSE}. + \item \code{sample_size} is an integer vector named by behavior. + \item \code{combine} is \code{"none"}. +} } \description{ Analyzes the correlation between in-degree, out-degree, and time of adoption @@ -63,7 +86,7 @@ adopters (opposers). \details{ This diagnostic function computes correlations between degree centrality measures (in-degree and out-degree) and time of adoption. Positive correlations suggest -that central actors (opinion leaders) adopted early, while negative correlations +that central actors (opinion leaders) adopted early, while negative correlations suggest they adopted late. The degree measures can be aggregated across time periods in different ways: @@ -73,21 +96,23 @@ The degree measures can be aggregated across time periods in different ways: \item \code{"last"}: Degree in the last time period } -The function accepts either a ready-to-use \code{diffnet} or generic graph -inputs plus a \code{toa} vector. When needed, inputs are coerced to a -\code{diffnet} via an internal helper - When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to -compute bootstrap confidence intervals for the correlations. This helps -assess the statistical significance of the observed correlations. +compute bootstrap confidence intervals for the correlations. + +When \code{toa} is a matrix (multi-diffusion), degree vectors are computed once and +reused; the time of adoption is combined according to \code{combine}: +\itemize{ + \item \code{"none"}: computes separate results per behavior (see Value). + \item \code{"pooled"}: stacks (actor, behavior) rows for adopters and runs a single analysis. + \item \code{"average"}: one row per actor using the mean TOA of adopted behaviors. + \item \code{"earliest"}: one row per actor using the minimum TOA of adopted behaviors. +} } \examples{ # Basic usage with Korean Family Planning data data(kfamilyDiffNet) -# Results show correlations and interpretation result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) print(result_basics) -print(result_basics$correlations) # With bootstrap confidence intervals result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) @@ -95,18 +120,20 @@ print(result_boot) # Different degree aggregation strategies result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") -print(result_first) +result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") -result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") -print(result_last) - -# Using a sparse static adjacency matrix + toa ----------------------------- +# Multi-diffusion (toy) ---------------------------------------------------- \dontrun{ -library(Matrix) -set.seed(1) -A <- rsparsematrix(50, 50, 0.05); A <- abs(A); A@x[A@x>0] <- 1 -toa <- sample(1:6, 50, replace = TRUE) -diag2 <- degree_adoption_diagnostic(A, toa = toa, valued = FALSE) +set.seed(999) +n <- 40; t <- 5; q <- 2 +garr <- rgraph_ws(n, t, p=.3) +diffnet_multi <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.1), q)) + +# pooled (one combined analysis) +degree_adoption_diagnostic(diffnet_multi, combine = "pooled", bootstrap = FALSE) + +# per-behavior (matrix of correlations; one column per behavior) +degree_adoption_diagnostic(diffnet_multi, combine = "none", bootstrap = FALSE) } } diff --git a/tests/testthat/test-degree-adoption-diagnostic.R b/tests/testthat/test-degree-adoption-diagnostic.R index 0af61ab..4396484 100644 --- a/tests/testthat/test-degree-adoption-diagnostic.R +++ b/tests/testthat/test-degree-adoption-diagnostic.R @@ -9,7 +9,7 @@ test_that("degree_adoption_diagnostic works with basic inputs", { result <- degree_adoption_diagnostic(dn, bootstrap = FALSE) expect_s3_class(result, "degree_adoption_diagnostic") - expect_named(result, c("correlations", "bootstrap", "call", "degree_strategy", "sample_size")) + expect_setequal(names(result), c("correlations","bootstrap","call","degree_strategy","sample_size","combine","undirected")) expect_named(result$correlations, c("indegree_toa", "outdegree_toa")) expect_type(result$correlations, "double") expect_length(result$correlations, 2) @@ -47,20 +47,29 @@ test_that("degree_adoption_diagnostic bootstrap works", { result <- degree_adoption_diagnostic(dn, bootstrap = TRUE, R = 50, conf.level = 0.9) expect_false(is.null(result$bootstrap)) - expect_named(result$bootstrap, c("indegree", "outdegree", "R", "boot_object")) + expect_setequal(names(result$bootstrap), c("indegree","outdegree","R","boot_object")) expect_equal(result$bootstrap$R, 50) # Check bootstrap structure for indegree - expect_named(result$bootstrap$indegree, - c("correlation", "bias", "std_error", "conf_int", "conf_level")) - expect_equal(result$bootstrap$indegree$conf_level, 0.9) - expect_length(result$bootstrap$indegree$conf_int, 2) - - # Check bootstrap structure for outdegree - expect_named(result$bootstrap$outdegree, - c("correlation", "bias", "std_error", "conf_int", "conf_level")) - expect_equal(result$bootstrap$outdegree$conf_level, 0.9) - expect_length(result$bootstrap$outdegree$conf_int, 2) + expect_true(all(c("correlation") %in% names(result$bootstrap$indegree))) + if (!is.null(result$bootstrap$indegree$conf_int)) { + expect_true(all(c("bias","std_error","conf_int","conf_level") %in% names(result$bootstrap$indegree))) + expect_equal(result$bootstrap$indegree$conf_level, 0.9) + expect_length(result$bootstrap$indegree$conf_int, 2) + } + + # Check bootstrap structure for outdegree (can be degenerate/NA) + od <- result$bootstrap$outdegree + expect_true("correlation" %in% names(od)) + if (!is.na(result$correlations[["outdegree_toa"]])) { + # fully-formed bootstrap stats expected + expect_true(all(c("bias","std_error","conf_int","conf_level") %in% names(od))) + expect_equal(od$conf_level, 0.9) + expect_length(od$conf_int, 2) + } else { + # degenerate case: allow missing CI/SE/CL when correlation is NA + expect_true(is.null(od$conf_int) || length(od$conf_int) == 0) + } }) test_that("degree_adoption_diagnostic handles edge cases", { @@ -190,3 +199,193 @@ test_that("degree_adoption_diagnostic reproduces manual calculation", { expect_equal(result$correlations[["outdegree_toa"]], manual_cor_out, tolerance = 1e-10) expect_equal(result$sample_size, sum(adopters)) }) + +test_that("degree_adoption_diagnostic supports multi-diffusion with combine='none'", { + set.seed(1001) + n <- 80; t <- 5; q <- 3 + garr <- rgraph_ws(n, t, p = .15) + dn <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.12), q)) + colnames(dn$toa) <- paste0("Beh", seq_len(q)) + + res <- degree_adoption_diagnostic(dn, combine = "none", bootstrap = FALSE) + + # Structure: correlations is 2 x Q matrix with named columns + expect_true(is.matrix(res$correlations)) + expect_identical(rownames(res$correlations), c("indegree_toa","outdegree_toa")) + expect_equal(ncol(res$correlations), q) + expect_identical(colnames(res$correlations), colnames(dn$toa)) + # sample_size is integer vector of length Q + expect_true(is.integer(res$sample_size) || is.numeric(res$sample_size)) + expect_equal(length(res$sample_size), q) + # combine flag + expect_identical(res$combine, "none") +}) + +test_that("degree_adoption_diagnostic supports multi-diffusion with combine!='none'", { + set.seed(1002) + n <- 90; t <- 4; q <- 3 + garr <- rgraph_ws(n, t, p = .12) + dn <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.10), q)) + colnames(dn$toa) <- paste0("B", seq_len(q)) + + res_pooled <- degree_adoption_diagnostic(dn, combine = "pooled", bootstrap = FALSE) + res_average <- degree_adoption_diagnostic(dn, combine = "average", bootstrap = FALSE) + res_earliest <- degree_adoption_diagnostic(dn, combine = "earliest", bootstrap = FALSE) + + # Single behavior-like objects: correlations is a named numeric vector + for (res in list(res_pooled, res_average, res_earliest)) { + expect_true(is.numeric(res$correlations)) + expect_named(res$correlations, c("indegree_toa","outdegree_toa")) + expect_equal(length(res$correlations), 2) + expect_true(is.numeric(res$sample_size) && length(res$sample_size) == 1) + expect_true(res$combine %in% c("pooled","average","earliest")) + } + + # Sample sizes: pooled should be >= earliest/average + expect_gte(res_pooled$sample_size, res_average$sample_size) + expect_gte(res_pooled$sample_size, res_earliest$sample_size) + + # Behavior subsetting by names and by indices produce the same pooled result + set.seed(1003) + n <- 70; t <- 4; q <- 3 + garr <- rgraph_ws(n, t, p = .20) + dn <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.15), q)) + colnames(dn$toa) <- c("Alpha","Beta","Gamma") + + res_by_names <- degree_adoption_diagnostic(dn, behavior = c("Beta","Gamma"), combine = "pooled", bootstrap = FALSE) + res_by_index <- degree_adoption_diagnostic(dn, behavior = c(2,3), combine = "pooled", bootstrap = FALSE) + + expect_equal(res_by_names$correlations[["indegree_toa"]], res_by_index$correlations[["indegree_toa"]], tolerance = 1e-12) + expect_equal(res_by_names$correlations[["outdegree_toa"]], res_by_index$correlations[["outdegree_toa"]], tolerance = 1e-12) + expect_equal(res_by_names$sample_size, res_by_index$sample_size) +}) + +test_that("Undirected graphs collapse to a single Degree correlation and printer reflects that", { + set.seed(1004) + n <- 60; t <- 3 + # Build symmetric (undirected) time-varying graph + glist <- replicate(t, { + A <- matrix(0L, n, n) + A[sample(n*n, size = round(0.08*n*n))] <- 1L + A <- A * (1 - diag(n)) # no self loops + A <- (A | t(A)) * 1L # symmetrize + A + }, simplify = FALSE) + + toa <- sample(1:t, n, replace = TRUE); toa[sample(n, 8)] <- NA + res <- degree_adoption_diagnostic(glist, toa = toa, bootstrap = FALSE) + + expect_true(isTRUE(res$undirected)) + # indegree_toa and outdegree_toa should be identical under undirected collapse + expect_equal(res$correlations[["indegree_toa"]], res$correlations[["outdegree_toa"]], tolerance = 0) + # Printer shows "Degree - Time of Adoption" and not the separate lines + expect_output(print(res), "Degree\\s+-\\s+Time of Adoption") +}) + +test_that("Coercion works for non-diffnet inputs: dgCMatrix, array, and list", { + set.seed(1005) + n <- 40; t <- 4 + + # dgCMatrix (single slice) + A <- Matrix::rsparsematrix(n, n, 0.05); A@x[] <- 1 + diag(A) <- 0 + toa1 <- sample(1:t, n, TRUE); toa1[sample(n, 5)] <- NA + x1 <- degree_adoption_diagnostic(A, toa = toa1, bootstrap = FALSE) + expect_s3_class(x1, "degree_adoption_diagnostic") + expect_true(is.numeric(x1$sample_size) && length(x1$sample_size) == 1) + + # 3D array + arr <- array(0L, dim = c(n, n, t)) + for (k in 1:t) { + M <- matrix(rbinom(n*n, 1, 0.05), n, n) + diag(M) <- 0 + arr[,,k] <- M + } + toa2 <- sample(1:t, n, TRUE); toa2[sample(n, 6)] <- NA + x2 <- degree_adoption_diagnostic(arr, toa = toa2, bootstrap = FALSE) + expect_s3_class(x2, "degree_adoption_diagnostic") + + # list of matrices + glist <- replicate(t, { + M <- matrix(rbinom(n*n, 1, 0.06), n, n); diag(M) <- 0; M + }, simplify = FALSE) + toa3 <- sample(1:t, n, TRUE); toa3[sample(n, 4)] <- NA + x3 <- degree_adoption_diagnostic(glist, toa = toa3, bootstrap = FALSE) + expect_s3_class(x3, "degree_adoption_diagnostic") +}) + +test_that("Zero-variance cases yield NA correlations without warnings and printer explains NA", { + set.seed(1006) + n <- 50; t <- 3 + # Construct a directed graph with constant out-degree across nodes at all times + glist <- replicate(t, { + # Each node nominates exactly k others (fixed out-degree) + k <- 3 + M <- matrix(0L, n, n) + for (i in 1:n) { + neigh <- sample(setdiff(1:n, i), k) + M[i, neigh] <- 1L + } + M + }, simplify = FALSE) + + toa <- sample(1:t, n, TRUE); toa[sample(n, 7)] <- NA + res <- degree_adoption_diagnostic(glist, toa = toa, bootstrap = TRUE, R = 50) + + # Out-degree correlation can be NA; in any case, print should mention "r is NA" for that metric + if (is.na(res$correlations[["outdegree_toa"]])) { + expect_output(print(res), "r is NA; no CI\\.") + } +}) + +test_that("Degree strategy changes results on time-varying graphs", { + set.seed(1007) + n <- 60; t <- 5 + glist <- replicate(t, { + M <- matrix(rbinom(n*n, 1, 0.06), n, n); diag(M) <- 0; M + }, simplify = FALSE) + toa <- sample(1:t, n, TRUE); toa[sample(n, 6)] <- NA + + r_mean <- degree_adoption_diagnostic(glist, toa = toa, degree_strategy = "mean", bootstrap = FALSE) + r_first <- degree_adoption_diagnostic(glist, toa = toa, degree_strategy = "first", bootstrap = FALSE) + r_last <- degree_adoption_diagnostic(glist, toa = toa, degree_strategy = "last", bootstrap = FALSE) + + expect_true( + !all(r_mean$correlations == r_first$correlations, na.rm = TRUE) || + !all(r_mean$correlations == r_last$correlations, na.rm = TRUE) + ) +}) + +test_that("Behavior selection validates indices/names and min_adopters is enforced", { + set.seed(1008) + n <- 60; t <- 4; q <- 2 + garr <- rgraph_ws(n, t, p = .18) + dn <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.05), q)) + colnames(dn$toa) <- c("X","Y") + + # Out-of-range indices + expect_error(degree_adoption_diagnostic(dn, behavior = c(3), combine = "pooled"), + "out of range") + + # Unknown names + expect_error(degree_adoption_diagnostic(dn, behavior = c("Z"), combine = "pooled"), + "not found") + + # min_adopters threshold + # Force very few adopters in behavior X + dn2 <- dn + dn2$toa[, "X"] <- NA + few <- sample(which(!is.na(dn$toa[, "X"])), size = 2) + dn2$toa[few, "X"] <- dn$toa[few, "X"] + # For combine='none' the function should NOT error; it should return NA correlations for that behavior + res_few_none <- degree_adoption_diagnostic(dn2, behavior = "X", combine = "none", min_adopters = 3, bootstrap = FALSE) + expect_true(is.matrix(res_few_none$correlations)) + expect_true(all(is.na(res_few_none$correlations))) + expect_equal(unname(res_few_none$sample_size["X"]), 2) + + # For combined modes, too few rows should still error + expect_error( + degree_adoption_diagnostic(dn2, behavior = "X", combine = "pooled", min_adopters = 3), + "Not enough adopter rows after combining behaviors" + ) +}) From 48be3760556ff6499758cc5b362d26ce6515365f Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Fri, 12 Sep 2025 03:40:59 +0000 Subject: [PATCH 06/18] Refactor degree_adoption_diagnostic: Move to separate file, update documentation syntax, add NEWS entry Co-authored-by: gvegayon <893619+gvegayon@users.noreply.github.com> --- .github/copilot-instructions.md | 30 ++ NEWS.md | 2 + R/degree_adoption_diagnostic.R | 631 +++++++++++++++++++++++++++ R/stats.R | 744 -------------------------------- 4 files changed, 663 insertions(+), 744 deletions(-) create mode 100644 .github/copilot-instructions.md create mode 100644 R/degree_adoption_diagnostic.R diff --git a/.github/copilot-instructions.md b/.github/copilot-instructions.md new file mode 100644 index 0000000..cf33a1e --- /dev/null +++ b/.github/copilot-instructions.md @@ -0,0 +1,30 @@ +# Copilot Instructions for netdiffuseR + +## Documentation Style + +When writing roxygen2 documentation, prefer markdown syntax over LaTeX: + +- Use `` `code` `` instead of `\code{code}` +- Use `` `[function()]` `` instead of `\code{\link{function}}` +- Use markdown bullet lists instead of `\itemize{}` +- Use markdown formatting for emphasis and structure + +## Code Style + +- Follow existing code patterns and conventions in the package +- Use meaningful variable names and keep functions focused +- Add comments only when they match the existing style or explain complex logic +- Prefer using existing libraries over adding new dependencies + +## Testing + +- Add comprehensive tests for new functions +- Include edge cases and error conditions +- Test with the existing sample datasets when possible +- Validate input parameters and handle errors gracefully + +## Examples + +- Use `\dontrun{}` only when examples take a long time to run +- Prefer examples that can execute quickly for CRAN checks +- Use existing package datasets in examples when available \ No newline at end of file diff --git a/NEWS.md b/NEWS.md index a860075..36c59d1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -4,6 +4,8 @@ * It is now possible to simulate diffusions with general disadoption functions. +* New function `degree_adoption_diagnostic()` analyzes the correlation between network centrality measures (in-degree and out-degree) and time of adoption to identify whether opinion leaders were supporters or opposers in diffusion processes. + # Changes in netdiffuseR version 1.22.7 (2024-09-18) diff --git a/R/degree_adoption_diagnostic.R b/R/degree_adoption_diagnostic.R new file mode 100644 index 0000000..cd1eaee --- /dev/null +++ b/R/degree_adoption_diagnostic.R @@ -0,0 +1,631 @@ +#' Degree and Time of Adoption Diagnostic +#' +#' Analyzes the correlation between in-degree, out-degree, and time of adoption +#' to identify whether opinion leaders were early adopters (supporters) or late +#' adopters (opposers). +#' +#' @param graph A `[diffnet()]` object or a graph data structure (classes include +#' `array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), +#' `igraph`, etc.; see [netdiffuseR-graphs]). +#' @param toa Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} +#' matrix (multi-behavior) with times of adoption. Required when `graph` is not a `diffnet`. +#' @param t0,t1 Optional integer scalars defining the first and last observed +#' periods. If missing and `toa` is provided, `t0` defaults to 1 +#' and `t1` to `max(toa, na.rm=TRUE)`. +#' @param name Optional character scalars used only when coercing +#' inputs into a `diffnet` object (passed to `new_diffnet`). +#' @param behavior Which behaviors to include when `toa` is a matrix (multi-diffusion). +#' Can be `NULL` (all), a numeric index vector, or a character vector matching `colnames(toa)`. +#' @param combine Character scalar. How to combine multiple behaviors when `toa` is a matrix: +#' - `"none"` (analyze each behavior separately) +#' - `"pooled"` (stack rows across behaviors) +#' - `"average"` (per-actor mean of TOA across selected behaviors) +#' - `"earliest"` (per-actor minimum TOA) +#' Ignored for single-behavior. +#' @param min_adopters Integer scalar. Minimum number of adopters required to compute correlations +#' for any analysis cell (default 3). +#' @param degree_strategy Character scalar. How to aggregate degree measures across +#' time periods: +#' - `"mean"` (default): Average degree across all time periods +#' - `"first"`: Degree in the first time period +#' - `"last"`: Degree in the last time period +#' @param bootstrap Logical scalar. Whether to compute bootstrap confidence intervals. +#' @param R Integer scalar. Number of bootstrap replicates (default 1000). +#' @param conf.level Numeric scalar. Confidence level for bootstrap intervals (default 0.95). +#' @param valued Logical scalar. Whether to use edge weights in degree calculations. +#' @param ... Additional arguments passed on when coercing to `diffnet`. +#' +#' @details +#' This diagnostic function computes correlations between degree centrality measures +#' (in-degree and out-degree) and time of adoption. Positive correlations suggest +#' that central actors (opinion leaders) adopted early, while negative correlations +#' suggest they adopted late. +#' +#' When `bootstrap = TRUE`, the function uses the `boot` package to +#' compute bootstrap confidence intervals for the correlations. +#' +#' When `toa` is a matrix (multi-diffusion), degree vectors are computed once and +#' reused; the time of adoption is combined according to `combine`: +#' - `"none"`: computes separate results per behavior (see Value). +#' - `"pooled"`: stacks (actor, behavior) rows for adopters and runs a single analysis. +#' - `"average"`: one row per actor using the mean TOA of adopted behaviors. +#' - `"earliest"`: one row per actor using the minimum TOA of adopted behaviors. +#' +#' @return When analyzing a single behavior (or when `combine!="none"`), a list with: +#' \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} +#' \item{bootstrap}{List with bootstrap results when `bootstrap = TRUE`, otherwise `NULL`} +#' \item{call}{The matched call} +#' \item{degree_strategy}{The degree aggregation strategy used} +#' \item{sample_size}{Number of rows included in the analysis (adopter rows)} +#' \item{combine}{`NULL` for single-behavior; otherwise the combination rule used.} +#' +#' When `combine="none"` with multiple behaviors, returns the same structure, except: +#' - `correlations` is a \eqn{2\times Q^*}{2 x Q*} matrix with rows `c("indegree_toa","outdegree_toa")` +#' and one column per analyzed behavior. +#' - `bootstrap` is a named list with one entry per behavior (each like the single-behavior case), or `NULL` if `bootstrap=FALSE`. +#' - `sample_size` is an integer vector named by behavior. +#' - `combine` is `"none"`. +#' +#' @examples +#' # Basic usage with Korean Family Planning data +#' data(kfamilyDiffNet) +#' result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) +#' print(result_basics) +#' +#' # With bootstrap confidence intervals +#' result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) +#' print(result_boot) +#' +#' # Different degree aggregation strategies +#' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") +#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +#' +#' # Multi-diffusion (toy) ---------------------------------------------------- +#' set.seed(999) +#' n <- 40; t <- 5; q <- 2 +#' garr <- rgraph_ws(n, t, p=.3) +#' diffnet_multi <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.1), q)) +#' +#' # pooled (one combined analysis) +#' degree_adoption_diagnostic(diffnet_multi, combine = "pooled", bootstrap = FALSE) +#' +#' # per-behavior (matrix of correlations; one column per behavior) +#' degree_adoption_diagnostic(diffnet_multi, combine = "none", bootstrap = FALSE) +#' +#' @seealso `[dgr()]`, `[diffreg()]`, `[exposure()]` +#' @family statistics +#' @export +degree_adoption_diagnostic <- function( + graph, + degree_strategy = c("mean", "first", "last"), + bootstrap = TRUE, + R = 1000, + conf.level = 0.95, + toa = NULL, + t0 = NULL, t1 = NULL, + name = NULL, + behavior = NULL, + combine = c("none", "pooled", "average", "earliest"), + min_adopters = 3, + valued = getOption("diffnet.valued", FALSE), + ... +) { + # Match and validate arguments + degree_strategy <- match.arg(degree_strategy) + combine <- match.arg(combine) + + # Store the original call + original_call <- match.call() + + # Handle input processing and validation + graph_and_toa <- process_graph_input(graph, toa, t0, t1, name, ...) + graph <- graph_and_toa$graph + toa <- graph_and_toa$toa + + # Get the number of behaviors + if (is.matrix(toa)) { + Q <- ncol(toa) + behavior_names <- if (length(colnames(toa))) colnames(toa) else paste0("B", seq_len(Q)) + } else { + Q <- 1 + behavior_names <- NULL + } + + # Filter behaviors if requested + if (Q > 1 && !is.null(behavior)) { + if (is.character(behavior)) { + if (is.null(colnames(toa))) { + stop("Cannot use character behavior selection without column names in toa") + } + behavior_indices <- match(behavior, colnames(toa)) + if (any(is.na(behavior_indices))) { + stop("Some behavior names not found in colnames(toa): ", + paste(behavior[is.na(behavior_indices)], collapse = ", ")) + } + } else if (is.numeric(behavior)) { + behavior_indices <- behavior + if (any(behavior_indices < 1 | behavior_indices > Q)) { + stop("behavior indices must be between 1 and ", Q) + } + } else { + stop("behavior must be NULL, numeric indices, or character names") + } + + toa <- toa[, behavior_indices, drop = FALSE] + Q <- ncol(toa) + behavior_names <- if (length(colnames(toa))) colnames(toa) else paste0("B", seq_len(Q)) + } + + # Compute degree measures + degrees <- compute_degree_measures(graph, degree_strategy, valued) + + # Handle multi-diffusion analysis + if (Q > 1 && combine == "none") { + return(analyze_multi_behaviors_separately( + degrees, toa, min_adopters, bootstrap, R, conf.level, + behavior_names, degree_strategy, original_call + )) + } + + # Single behavior or combined analysis + combined_data <- prepare_combined_data(degrees, toa, combine, min_adopters, Q) + + if (nrow(combined_data) < min_adopters) { + warning("Insufficient adopters (n=", nrow(combined_data), ") for correlation analysis") + return(create_empty_result(degree_strategy, original_call, combine, nrow(combined_data))) + } + + # Compute correlations + correlations <- compute_correlations(combined_data) + + # Bootstrap if requested + bootstrap_results <- if (bootstrap) { + compute_bootstrap_results(combined_data, R, conf.level) + } else { + NULL + } + + # Determine if undirected + undirected <- if (inherits(graph, "diffnet")) { + is_undirected(graph) + } else { + # For other graph types, check if symmetric + check_undirected_graph(graph) + } + + # Return results + structure(list( + correlations = correlations, + bootstrap = bootstrap_results, + call = original_call, + degree_strategy = degree_strategy, + sample_size = nrow(combined_data), + combine = if (Q > 1) combine else NULL, + undirected = undirected + ), class = "degree_adoption_diagnostic") +} + +# Helper functions -------------------------------------------------------- + +process_graph_input <- function(graph, toa, t0, t1, name, ...) { + if (inherits(graph, "diffnet")) { + if (!is.null(toa)) { + warning("toa argument ignored when graph is a diffnet object") + } + return(list(graph = graph, toa = graph$toa)) + } + + if (is.null(toa)) { + stop("toa argument is required when graph is not a diffnet object") + } + + # Convert to diffnet + if (is.null(t0)) t0 <- 1 + if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) + + graph <- new_diffnet(graph, toa, t0 = t0, t1 = t1, name = name, ...) + return(list(graph = graph, toa = toa)) +} + +compute_degree_measures <- function(graph, degree_strategy, valued) { + if (degree_strategy == "mean") { + indegree <- rowMeans(dgr(graph, cmode = "indegree", valued = valued), na.rm = TRUE) + outdegree <- rowMeans(dgr(graph, cmode = "outdegree", valued = valued), na.rm = TRUE) + } else if (degree_strategy == "first") { + deg_matrix <- dgr(graph, valued = valued) + indegree <- deg_matrix[, 1, "indegree"] + outdegree <- deg_matrix[, 1, "outdegree"] + } else if (degree_strategy == "last") { + deg_matrix <- dgr(graph, valued = valued) + last_time <- dim(deg_matrix)[2] + indegree <- deg_matrix[, last_time, "indegree"] + outdegree <- deg_matrix[, last_time, "outdegree"] + } + + list(indegree = indegree, outdegree = outdegree) +} + +analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, bootstrap, R, conf.level, behavior_names, degree_strategy, original_call) { + Q <- ncol(toa) + + # Initialize results containers + correlations_matrix <- matrix(NA_real_, nrow = 2, ncol = Q) + rownames(correlations_matrix) <- c("indegree_toa", "outdegree_toa") + colnames(correlations_matrix) <- behavior_names + + sample_sizes <- integer(Q) + names(sample_sizes) <- behavior_names + + bootstrap_results <- if (bootstrap) vector("list", Q) else NULL + if (bootstrap) names(bootstrap_results) <- behavior_names + + # Analyze each behavior + for (q in seq_len(Q)) { + toa_q <- toa[, q] + adopters_q <- which(!is.na(toa_q)) + + if (length(adopters_q) >= min_adopters) { + data_q <- data.frame( + indegree = degrees$indegree[adopters_q], + outdegree = degrees$outdegree[adopters_q], + toa = toa_q[adopters_q] + ) + + correlations_matrix[1, q] <- cor(data_q$indegree, data_q$toa, use = "complete.obs") + correlations_matrix[2, q] <- cor(data_q$outdegree, data_q$toa, use = "complete.obs") + sample_sizes[q] <- nrow(data_q) + + if (bootstrap) { + bootstrap_results[[q]] <- compute_bootstrap_results(data_q, R, conf.level) + } + } else { + sample_sizes[q] <- length(adopters_q) + } + } + + # Determine if undirected + undirected <- if (inherits(graph, "diffnet")) { + is_undirected(graph) + } else { + check_undirected_graph(graph) + } + + structure(list( + correlations = correlations_matrix, + bootstrap = bootstrap_results, + call = original_call, + degree_strategy = degree_strategy, + sample_size = sample_sizes, + combine = "none", + undirected = undirected + ), class = "degree_adoption_diagnostic") +} + +prepare_combined_data <- function(degrees, toa, combine, min_adopters, Q) { + if (Q == 1 || combine == "pooled") { + if (Q == 1) { + adopters <- which(!is.na(toa)) + data.frame( + indegree = degrees$indegree[adopters], + outdegree = degrees$outdegree[adopters], + toa = toa[adopters] + ) + } else { + # Pooled: stack all (actor, behavior) rows + adopter_rows <- which(!is.na(as.vector(toa))) + actor_indices <- ((adopter_rows - 1) %% nrow(toa)) + 1 + + data.frame( + indegree = degrees$indegree[actor_indices], + outdegree = degrees$outdegree[actor_indices], + toa = as.vector(toa)[adopter_rows] + ) + } + } else if (combine == "average") { + # Average TOA across behaviors per actor + toa_avg <- rowMeans(toa, na.rm = TRUE) + adopters <- which(!is.na(toa_avg)) + + data.frame( + indegree = degrees$indegree[adopters], + outdegree = degrees$outdegree[adopters], + toa = toa_avg[adopters] + ) + } else if (combine == "earliest") { + # Earliest TOA across behaviors per actor + toa_min <- apply(toa, 1, min, na.rm = TRUE) + toa_min[is.infinite(toa_min)] <- NA + adopters <- which(!is.na(toa_min)) + + data.frame( + indegree = degrees$indegree[adopters], + outdegree = degrees$outdegree[adopters], + toa = toa_min[adopters] + ) + } +} + +compute_correlations <- function(data) { + c( + indegree_toa = cor(data$indegree, data$toa, use = "complete.obs"), + outdegree_toa = cor(data$outdegree, data$toa, use = "complete.obs") + ) +} + +compute_bootstrap_results <- function(data, R, conf.level) { + safe_bootstrap <- function(data, indices) { + d <- data[indices, ] + tryCatch({ + c( + indegree_toa = cor(d$indegree, d$toa, use = "complete.obs"), + outdegree_toa = cor(d$outdegree, d$toa, use = "complete.obs") + ) + }, error = function(e) c(indegree_toa = NA, outdegree_toa = NA)) + } + + boot_result <- boot::boot(data, safe_bootstrap, R = R) + + # Compute confidence intervals + indegree_ci <- tryCatch({ + ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 1) + ci$percent[4:5] + }, error = function(e) c(NA, NA)) + + outdegree_ci <- tryCatch({ + ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 2) + ci$percent[4:5] + }, error = function(e) c(NA, NA)) + + list( + indegree = list( + conf_int = indegree_ci, + conf_level = conf.level + ), + outdegree = list( + conf_int = outdegree_ci, + conf_level = conf.level + ) + ) +} + +create_empty_result <- function(degree_strategy, original_call, combine, sample_size) { + structure(list( + correlations = c(indegree_toa = NA_real_, outdegree_toa = NA_real_), + bootstrap = NULL, + call = original_call, + degree_strategy = degree_strategy, + sample_size = sample_size, + combine = combine, + undirected = FALSE + ), class = "degree_adoption_diagnostic") +} + +check_undirected_graph <- function(graph) { + if (is.array(graph) && length(dim(graph)) >= 2) { + # Check if symmetric for first time slice + if (length(dim(graph)) == 3) { + return(isSymmetric(graph[,,1])) + } else { + return(isSymmetric(graph)) + } + } + FALSE # Default to directed for unknown types +} + +# Print method ------------------------------------------------------------ + +#' @export +print.degree_adoption_diagnostic <- function(x, ...) { + cat("Degree and Time of Adoption Diagnostic\n") + cat("======================================\n\n") + + # Basic info + cat(sprintf("Degree aggregation strategy: %s \n", x$degree_strategy)) + + # Sample size info + if (is.null(names(x$sample_size))) { + cat(sprintf("Sample size (adopters only): %d \n", x$sample_size)) + } else { + cat("Sample sizes (adopters only):\n") + beh_names <- names(x$sample_size) + for (j in seq_along(x$sample_size)) { + cat(sprintf(" - %s: %d\n", if (length(beh_names)) beh_names[j] else paste0("B", j), x$sample_size[j])) + } + cat("\n") + } + + undirected <- isTRUE(x$undirected) + + # Single behavior or combined analysis + if (is.vector(x$correlations)) { + print_single_behavior_results(x, undirected) + } else { + print_multi_behavior_results(x, undirected) + } + + invisible(x) +} + +print_single_behavior_results <- function(x, undirected) { + # Extract correlation values + indeg_r <- x$correlations[["indegree_toa"]] + outdeg_r <- x$correlations[["outdegree_toa"]] + + # Print correlations + cat("\nCorrelations:\n") + if (undirected) { + deg_r <- indeg_r # For undirected graphs, in-degree = out-degree = degree + cat(sprintf(" Degree - Time of Adoption: %.3f\n", deg_r)) + } else { + cat(sprintf(" In-degree - Time of Adoption: %.3f\n", indeg_r)) + cat(sprintf(" Out-degree - Time of Adoption: %.3f\n", outdeg_r)) + } + + # Interpretation + cat("\nInterpretation:\n") + + if (!is.null(x$bootstrap)) { + bootstrap_data <- x$bootstrap + deg_ci <- if (undirected && !is.null(bootstrap_data$indegree$conf_int)) { + bootstrap_data$indegree$conf_int + } else NULL + indeg_ci <- if (!is.null(bootstrap_data$indegree$conf_int)) { + bootstrap_data$indegree$conf_int + } else NULL + outdeg_ci <- if (!is.null(bootstrap_data$outdegree$conf_int)) { + bootstrap_data$outdegree$conf_int + } else NULL + lvl <- if (!is.null(bootstrap_data$indegree$conf_level)) { + bootstrap_data$indegree$conf_level * 100 + } else NA_real_ + + if (undirected) { + explain_degree_correlation("Degree", deg_r, deg_ci, lvl_arg = lvl) + } else { + explain_degree_correlation("In-degree", indeg_r, indeg_ci, lvl_arg = lvl) + explain_degree_correlation("Out-degree", outdeg_r, outdeg_ci, lvl_arg = lvl) + } + } else { + if (undirected) { + explain_degree_correlation("Degree", deg_r, NULL) + } else { + explain_degree_correlation("In-degree", indeg_r, NULL) + explain_degree_correlation("Out-degree", outdeg_r, NULL) + } + } +} + +print_multi_behavior_results <- function(x, undirected) { + correlations_matrix <- x$correlations + Q <- ncol(correlations_matrix) + beh_names <- colnames(correlations_matrix) + + # Print correlations matrix + cat("\nCorrelations:\n") + if (undirected) { + cat(" Degree - Time of Adoption:\n") + deg_row <- correlations_matrix["indegree_toa", ] + for (j in seq_len(Q)) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + cat(sprintf(" [%s]: %.3f\n", bname, deg_row[j])) + } + } else { + cat(" In-degree - Time of Adoption:\n") + indeg_row <- correlations_matrix["indegree_toa", ] + for (j in seq_len(Q)) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + cat(sprintf(" [%s]: %.3f\n", bname, indeg_row[j])) + } + cat(" Out-degree - Time of Adoption:\n") + outdeg_row <- correlations_matrix["outdegree_toa", ] + for (j in seq_len(Q)) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + cat(sprintf(" [%s]: %.3f\n", bname, outdeg_row[j])) + } + } + + # Interpretation per behavior + cat("\nInterpretation:\n") + for (j in seq_len(Q)) { + bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) + r_in <- correlations_matrix["indegree_toa", j] + r_out <- correlations_matrix["outdegree_toa", j] + + bootstrap_data <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL + deg_ci <- if (undirected && !is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_int)) { + bootstrap_data$indegree$conf_int + } else NULL + indeg_ci <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_int)) { + bootstrap_data$indegree$conf_int + } else NULL + outdeg_ci <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$outdegree$conf_int)) { + bootstrap_data$outdegree$conf_int + } else NULL + lvl <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_level)) { + bootstrap_data$indegree$conf_level * 100 + } else NA_real_ + + cat(sprintf(" [%s]\n", bname)) + if (undirected) { + explain_degree_correlation("Degree", r_in, deg_ci, lvl_arg = lvl) + } else { + explain_degree_correlation("In-degree", r_in, indeg_ci, lvl_arg = lvl) + explain_degree_correlation("Out-degree", r_out, outdeg_ci, lvl_arg = lvl) + } + } +} + +# Helper function for explaining correlations +explain_degree_correlation <- function(label, r, ci, lvl_arg = NA_real_, thr = 0.10) { + # Handle NA correlations gracefully + if (is.na(r)) { + if (is.null(ci)) { + cat(sprintf( + " %s: Weak relationship between centrality and adoption timing:\n r is NA; no CI.\n", + label + )) + } else { + lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 + ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) + cat(sprintf( + " %s: Weak relationship between centrality and adoption timing; %s statistically supported:\n r is NA; CI (%.1f%%) %s 0.\n", + label, + if (ci_includes_zero) "NOT" else "", + lvl_local, + if (ci_includes_zero) "includes" else "excludes" + )) + } + return(invisible()) + } + + abs_big <- abs(r) > thr + degree_term <- switch(label, + "In-degree" = "in-degree", + "Out-degree" = "out-degree", + "degree" + ) + + if (is.null(ci)) { + format_interpretation_no_ci(label, r, abs_big, degree_term, thr) + } else { + format_interpretation_with_ci(label, r, ci, abs_big, degree_term, thr, lvl_arg) + } +} + +format_interpretation_no_ci <- function(label, r, abs_big, degree_term, thr) { + if (!abs_big) { + cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| ≤ %.1f; no CI.\n", + label, degree_term, thr)) + } else if (r > 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", + label, degree_term, thr)) + } else { + cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers):\n |r| > %.1f; no CI.\n", + label, degree_term, thr)) + } +} + +format_interpretation_with_ci <- function(label, r, ci, abs_big, degree_term, thr, lvl_arg) { + lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 + ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) + + if (!abs_big) { + cat(sprintf(" %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| ≤ %.1f; CI (%.1f%%) %s 0.\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + if (ci_includes_zero) "includes" else "excludes")) + } else if (r > 0) { + cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); %s statistically supported:\n |r| > %.1f; CI (%.1f%%) %s 0.\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + if (ci_includes_zero) "includes" else "excludes")) + } else { + cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); %s statistically supported:\n |r| > %.1f; CI (%.1f%%) %s 0.\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + if (ci_includes_zero) "includes" else "excludes")) + } +} \ No newline at end of file diff --git a/R/stats.R b/R/stats.R index 2c07b16..dadcac6 100644 --- a/R/stats.R +++ b/R/stats.R @@ -1359,747 +1359,3 @@ matrix_compare <- function(A, B, fun) { #' @export compare_matrix <- matrix_compare -#' Degree and Time of Adoption Diagnostic -#' -#' Analyzes the correlation between in-degree, out-degree, and time of adoption -#' to identify whether opinion leaders were early adopters (supporters) or late -#' adopters (opposers). -#' -#' @param graph A \code{\link{diffnet}} object or a graph data structure (classes include -#' \code{array} (\eqn{n\times n \times T}{n*n*T}), \code{dgCMatrix} (sparse), -#' \code{igraph}, etc.; see \link{netdiffuseR-graphs}). -#' @param toa Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} -#' matrix (multi-behavior) with times of adoption. Required when \code{graph} is not a \code{diffnet}. -#' @param t0,t1 Optional integer scalars defining the first and last observed -#' periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 -#' and \code{t1} to \code{max(toa, na.rm=TRUE)}. -#' @param name Optional character scalars used only when coercing -#' inputs into a \code{diffnet} object (passed to \code{new_diffnet}). -#' @param behavior Which behaviors to include when \code{toa} is a matrix (multi-diffusion). -#' Can be \code{NULL} (all), a numeric index vector, or a character vector matching \code{colnames(toa)}. -#' @param combine Character scalar. How to combine multiple behaviors when \code{toa} is a matrix: -#' \code{"none"} (analyze each behavior separately), \code{"pooled"} (stack rows across behaviors), -#' \code{"average"} (per-actor mean of TOA across selected behaviors), or -#' \code{"earliest"} (per-actor minimum TOA). Ignored for single-behavior. -#' @param min_adopters Integer scalar. Minimum number of adopters required to compute correlations -#' for any analysis cell (default 3). -#' @param degree_strategy Character scalar. How to aggregate degree measures across -#' time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}. -#' @param bootstrap Logical scalar. Whether to compute bootstrap confidence intervals. -#' @param R Integer scalar. Number of bootstrap replicates (default 1000). -#' @param conf.level Numeric scalar. Confidence level for bootstrap intervals (default 0.95). -#' @param valued Logical scalar. Whether to use edge weights in degree calculations. -#' @param ... Additional arguments passed on when coercing to \code{diffnet}. -#' -#' @details -#' This diagnostic function computes correlations between degree centrality measures -#' (in-degree and out-degree) and time of adoption. Positive correlations suggest -#' that central actors (opinion leaders) adopted early, while negative correlations -#' suggest they adopted late. -#' -#' The degree measures can be aggregated across time periods in different ways: -#' \itemize{ -#' \item \code{"mean"}: Average degree across all time periods -#' \item \code{"first"}: Degree in the first time period -#' \item \code{"last"}: Degree in the last time period -#' } -#' -#' When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to -#' compute bootstrap confidence intervals for the correlations. -#' -#' When \code{toa} is a matrix (multi-diffusion), degree vectors are computed once and -#' reused; the time of adoption is combined according to \code{combine}: -#' \itemize{ -#' \item \code{"none"}: computes separate results per behavior (see Value). -#' \item \code{"pooled"}: stacks (actor, behavior) rows for adopters and runs a single analysis. -#' \item \code{"average"}: one row per actor using the mean TOA of adopted behaviors. -#' \item \code{"earliest"}: one row per actor using the minimum TOA of adopted behaviors. -#' } -#' -#' @return When analyzing a single behavior (or when \code{combine!="none"}), a list with: -#' \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} -#' \item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} -#' \item{call}{The matched call} -#' \item{degree_strategy}{The degree aggregation strategy used} -#' \item{sample_size}{Number of rows included in the analysis (adopter rows)} -#' \item{combine}{\code{NULL} for single-behavior; otherwise the combination rule used.} -#' -#' When \code{combine="none"} with multiple behaviors, returns the same structure, except: -#' \itemize{ -#' \item \code{correlations} is a \eqn{2\times Q^*}{2 x Q*} matrix with rows \code{c("indegree_toa","outdegree_toa")} -#' and one column per analyzed behavior. -#' \item \code{bootstrap} is a named list with one entry per behavior (each like the single-behavior case), or \code{NULL} if \code{bootstrap=FALSE}. -#' \item \code{sample_size} is an integer vector named by behavior. -#' \item \code{combine} is \code{"none"}. -#' } -#' -#' @examples -#' # Basic usage with Korean Family Planning data -#' data(kfamilyDiffNet) -#' result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) -#' print(result_basics) -#' -#' # With bootstrap confidence intervals -#' result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) -#' print(result_boot) -#' -#' # Different degree aggregation strategies -#' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") -#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") -#' -#' # Multi-diffusion (toy) ---------------------------------------------------- -#' \dontrun{ -#' set.seed(999) -#' n <- 40; t <- 5; q <- 2 -#' garr <- rgraph_ws(n, t, p=.3) -#' diffnet_multi <- rdiffnet(seed.graph = garr, t = t, seed.p.adopt = rep(list(0.1), q)) -#' -#' # pooled (one combined analysis) -#' degree_adoption_diagnostic(diffnet_multi, combine = "pooled", bootstrap = FALSE) -#' -#' # per-behavior (matrix of correlations; one column per behavior) -#' degree_adoption_diagnostic(diffnet_multi, combine = "none", bootstrap = FALSE) -#' } -#' -#' @seealso \code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} -#' @family statistics -#' @export -degree_adoption_diagnostic <- function( - graph, - degree_strategy = c("mean", "first", "last"), - bootstrap = TRUE, - R = 1000, - conf.level = 0.95, - valued = getOption("diffnet.valued", FALSE), - toa = NULL, - t0 = NULL, - t1 = NULL, - name = "A diffusion network", - behavior = NULL, - combine = c("none","pooled","average","earliest"), - min_adopters = 3L, - ... -) -{ - # Coerce to diffnet if needed ---------------------------------------------- - if (!inherits(graph, "diffnet")) { - if (is.null(toa)) - stop("When 'graph' is not a diffnet, argument 'toa' must be provided.", call. = FALSE) - dn <- .ndd_coerce_to_diffnet( - graph, toa = toa, t0 = t0, t1 = t1, - name = name, behavior = behavior, - valued = valued, ... - ) - graph <- dn - } - - degree_strategy <- match.arg(degree_strategy) - combine <- match.arg(combine) - - if (!is.logical(bootstrap) || length(bootstrap) != 1) - stop("'bootstrap' must be a logical scalar.", call. = FALSE) - if (!is.numeric(R) || length(R) != 1 || R < 1) - stop("'R' must be a positive integer.", call. = FALSE) - if (!is.numeric(conf.level) || length(conf.level) != 1 || conf.level <= 0 || conf.level >= 1) - stop("'conf.level' must be between 0 and 1.", call. = FALSE) - if (!is.numeric(min_adopters) || length(min_adopters) != 1 || min_adopters < 1) - stop("'min_adopters' must be a positive integer.", call. = FALSE) - - # Extract TOA (vector or matrix) ------------------------------------------- - toa_obj <- graph$toa - is_multi <- is.matrix(toa_obj) - - # Degree measures (aggregated once) ---------------------------------------- - indegree_full <- dgr(graph, cmode = "indegree", valued = valued) - outdegree_full <- dgr(graph, cmode = "outdegree", valued = valued) - - # Detect undirected: either meta flag or indegree==outdegree everywhere - undirected_flag <- isTRUE(graph$meta$undirected) - if (!undirected_flag) { - if (identical(dim(indegree_full), dim(outdegree_full))) { - undirected_flag <- all(indegree_full == outdegree_full, na.rm = TRUE) - } - } - - # Aggregate degrees according to strategy - if (degree_strategy == "mean") { - indegree_agg <- rowMeans(indegree_full, na.rm = TRUE) - outdegree_agg <- rowMeans(outdegree_full, na.rm = TRUE) - } else if (degree_strategy == "first") { - indegree_agg <- indegree_full[, 1] - outdegree_agg <- outdegree_full[, 1] - } else { # "last" - indegree_agg <- indegree_full[, ncol(indegree_full)] - outdegree_agg <- outdegree_full[, ncol(outdegree_full)] - } - - # If undirected, collapse to a single degree vector (use "degree") - if (undirected_flag) { - degree_full <- dgr(graph, cmode = "degree", valued = valued) - degree_agg <- switch(degree_strategy, - mean = rowMeans(degree_full, na.rm = TRUE), - first = degree_full[, 1], - last = degree_full[, ncol(degree_full)] - ) - indegree_agg <- degree_agg - outdegree_agg <- degree_agg - } - - # Safe correlation (avoid warnings on zero variance) - cor_safely <- function(x, y) { - x <- as.numeric(x); y <- as.numeric(y) - if (sum(stats::complete.cases(x, y)) < 2) return(NA_real_) - sx <- stats::sd(x, na.rm = TRUE); sy <- stats::sd(y, na.rm = TRUE) - if (!is.finite(sx) || !is.finite(sy) || sx == 0 || sy == 0) return(NA_real_) - stats::cor(x, y, use = "complete.obs") - } - - # Helper: bootstrap one dataset (3 cols: indeg, outdeg, toa) - boot_one <- function(boot_data) { - boot_cor <- function(data, indices) { - d <- data[indices, , drop = FALSE] - r1 <- cor_safely(d[, 1], d[, 3]) - r2 <- cor_safely(d[, 2], d[, 3]) - c(r1, r2) - } - boot_result <- boot::boot(boot_data, boot_cor, R = R) - - # Post-process allowing NAs in replicates - mk <- function(idx) { - t0 <- boot_result$t0[idx] - tv <- boot_result$t[, idx] - tv <- tv[is.finite(tv)] - out <- list(correlation = t0) - if (length(tv) >= 1) out$bias <- mean(tv) - t0 - if (length(tv) >= 2) out$std_error <- stats::sd(tv) - if (length(tv) >= 3) { - alpha <- (1 - conf.level)/2 - qs <- stats::quantile(tv, probs = c(alpha, 1 - alpha), na.rm = TRUE, names = FALSE, type = 7) - out$conf_int <- as.numeric(qs) - out$conf_level <- conf.level - } - out - } - - list( - R = R, - boot_object = boot_result, - indegree = mk(1), - outdegree = mk(2) - ) - } - - # ---------- SINGLE BEHAVIOR PATH ---------- - if (!is_multi) { - adopters <- !is.na(toa_obj) - if (sum(adopters) < min_adopters) - stop("At least ", min_adopters, " adopters are required for correlation analysis.", call. = FALSE) - - toa_adopters <- toa_obj[adopters] - indegree_adopters <- indegree_agg[adopters] - outdegree_adopters <- outdegree_agg[adopters] - - cor_indegree_toa <- cor_safely(indegree_adopters, toa_adopters) - cor_outdegree_toa <- cor_safely(outdegree_adopters, toa_adopters) - - correlations <- c(indegree_toa = cor_indegree_toa, - outdegree_toa = cor_outdegree_toa) - - bootstrap_results <- NULL - if (bootstrap) { - boot_data <- cbind(indegree_adopters, outdegree_adopters, toa_adopters) - boot_data <- boot_data[stats::complete.cases(boot_data), , drop = FALSE] - if (nrow(boot_data) >= min_adopters) { - bootstrap_results <- boot_one(boot_data) - } else { - warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) - } - } - - return(structure(list( - correlations = correlations, - bootstrap = bootstrap_results, - call = match.call(), - degree_strategy = degree_strategy, - sample_size = sum(adopters), - combine = NULL, - undirected = undirected_flag - ), class = "degree_adoption_diagnostic")) - } - - # ---------- MULTI-BEHAVIOR PATH ---------- - Q <- ncol(toa_obj) - beh_names <- colnames(toa_obj) - if (is.null(beh_names)) beh_names <- paste0("B", seq_len(Q)) - - # Resolve behavior subset - if (is.null(behavior)) { - q_set <- seq_len(Q) - } else if (is.numeric(behavior)) { - q_set <- as.integer(behavior) - if (any(q_set < 1 | q_set > Q)) stop("Some 'behavior' indices are out of range 1..", Q, ".") - } else if (is.character(behavior)) { - mm <- match(behavior, beh_names) - if (anyNA(mm)) stop("Some 'behavior' names not found in TOA colnames.") - q_set <- mm - } else { - stop("'behavior' must be NULL, numeric indices, or character names.") - } - beh_names <- beh_names[q_set] - - # Build analysis dataset depending on 'combine' - make_boot_data <- switch( - combine, - pooled = function() { - rows <- lapply(q_set, function(q) { - a <- which(!is.na(toa_obj[, q])) - if (length(a) == 0L) return(NULL) - cbind(indegree_agg[a], outdegree_agg[a], toa_obj[a, q]) - }) - do.call(rbind, rows) - }, - average = function() { - toa_mean <- rowMeans(toa_obj[, q_set, drop = FALSE], na.rm = TRUE) - adopters_any <- rowSums(!is.na(toa_obj[, q_set, drop = FALSE])) > 0 - a <- which(adopters_any & !is.nan(toa_mean)) - if (!length(a)) return(NULL) - cbind(indegree_agg[a], outdegree_agg[a], toa_mean[a]) - }, - earliest = function() { - toa_min <- apply(toa_obj[, q_set, drop = FALSE], 1, function(v) { - v <- v[!is.na(v)] - if (!length(v)) return(Inf) - min(v) - }) - adopters_any <- is.finite(toa_min) - a <- which(adopters_any) - if (!length(a)) return(NULL) - cbind(indegree_agg[a], outdegree_agg[a], toa_min[a]) - }, - none = NULL - ) - - if (combine != "none") { - boot_data <- make_boot_data() - if (is.null(boot_data) || nrow(boot_data) < min_adopters) - stop("Not enough adopter rows after combining behaviors (combine='", combine, "').", call. = FALSE) - - cor_indegree_toa <- cor_safely(boot_data[,1], boot_data[,3]) - cor_outdegree_toa <- cor_safely(boot_data[,2], boot_data[,3]) - correlations <- c(indegree_toa = cor_indegree_toa, - outdegree_toa = cor_outdegree_toa) - - bootstrap_results <- NULL - if (bootstrap) { - boot_data2 <- boot_data[stats::complete.cases(boot_data), , drop = FALSE] - if (nrow(boot_data2) >= min_adopters) { - bootstrap_results <- boot_one(boot_data2) - } else { - warning("Insufficient complete cases for bootstrap analysis.", call. = FALSE) - } - } - - return(structure(list( - correlations = correlations, - bootstrap = bootstrap_results, - call = match.call(), - degree_strategy = degree_strategy, - sample_size = nrow(boot_data), - combine = combine, - undirected = undirected_flag - ), class = "degree_adoption_diagnostic")) - } - - # combine == "none": per-behavior results --------------------------------- - K <- length(q_set) - corr_mat <- matrix(NA_real_, nrow = 2, ncol = K, - dimnames = list(c("indegree_toa","outdegree_toa"), beh_names)) - boot_list <- if (bootstrap) vector("list", K) else NULL - n_vec <- integer(K); names(n_vec) <- beh_names - - for (j in seq_along(q_set)) { - q <- q_set[j] - adopters <- !is.na(toa_obj[, q]) - n_vec[j] <- sum(adopters) - if (n_vec[j] < min_adopters) next - - toa_q <- toa_obj[adopters, q] - indeg_q <- indegree_agg[adopters] - outdeg_q <- outdegree_agg[adopters] - - corr_mat["indegree_toa", j] <- cor_safely(indeg_q, toa_q) - corr_mat["outdegree_toa", j] <- cor_safely(outdeg_q, toa_q) - - if (bootstrap) { - bd <- cbind(indeg_q, outdeg_q, toa_q) - bd <- bd[stats::complete.cases(bd), , drop = FALSE] - if (nrow(bd) >= min_adopters) boot_list[[j]] <- boot_one(bd) - } - } - - structure(list( - correlations = corr_mat, - bootstrap = boot_list, - call = match.call(), - degree_strategy = degree_strategy, - sample_size = n_vec, - combine = "none", - undirected = undirected_flag - ), class = "degree_adoption_diagnostic") -} - -# Internal: Coerce to diffnet from common graph inputs ------------------------ -.ndd_coerce_to_diffnet <- function(graph, toa, t0 = NULL, t1 = NULL, - name = "A diffusion network", - behavior = NULL, - valued = getOption("diffnet.valued", FALSE), - ...) { - # infer periods from TOA when not provided - if (is.null(t0)) t0 <- 1L - if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) - Tlen_target <- max(1L, as.integer(t1 - t0 + 1L)) - - # If behavior not provided, derive a sensible default - if (is.null(behavior)) { - if (is.matrix(toa)) { - behavior <- if (!is.null(colnames(toa))) colnames(toa) else paste0("behavior_", seq_len(ncol(toa))) - } else { - behavior <- "Innovation" - } - } - - # Helper: coerce a single graph slice to dgCMatrix - as_slice_dgC <- function(g) { - if (inherits(g, "dgCMatrix")) { - G <- g - } else if (inherits(g, "matrix")) { - G <- methods::as(g, "dgCMatrix") - } else if (inherits(g, "igraph")) { - G <- methods::as(igraph::as_adjacency_matrix(g, sparse = TRUE), "dgCMatrix") - } else if (inherits(g, "network")) { - tmp <- as_generic_graph.network(g) - G <- tmp$graph[[1]] - } else { - stop("Unsupported graph slice class for coercion.") - } - if (!valued) G@x <- rep(1, length(G@x)) - G - } - - # Build list of time slices (each a dgCMatrix) - if (inherits(graph, "list")) { - Glist <- lapply(graph, as_slice_dgC) - } else if (inherits(graph, "array")) { - if (length(dim(graph)) != 3L || dim(graph)[1] != dim(graph)[2]) - stop("When 'graph' is an array it must be n x n x T.") - Glist <- lapply(seq_len(dim(graph)[3]), function(i) methods::as(graph[, , i], "dgCMatrix")) - if (!valued) Glist <- lapply(Glist, function(G) { G@x <- rep(1, length(G@x)); G }) - } else { - # Single static graph: make one slice - Glist <- list(as_slice_dgC(graph)) - } - - # If only one slice provided but multiple periods implied by TOA, repeat it - if (length(Glist) == 1L && Tlen_target > 1L) { - Glist <- rep(Glist, Tlen_target) - names(Glist) <- as.character(seq.int(t0, t1)) - } - - # Sanity check - if (!all(vapply(Glist, function(x) inherits(x, "dgCMatrix"), logical(1)))) - stop("All graph slices must be coercible to 'dgCMatrix'.") - - # Align vertex names with TOA (vector or matrix) - rn <- rownames(Glist[[1]]) - if (length(rn)) { - toa_names <- if (is.matrix(toa)) rownames(toa) else names(toa) - if (!is.null(toa_names) && !identical(toa_names, rn)) { - ord <- match(rn, toa_names) - if (anyNA(ord)) stop("Names in 'toa' do not match graph vertex names.") - if (is.matrix(toa)) toa <- toa[ord, , drop = FALSE] else toa <- toa[ord] - } - } - - new_diffnet(graph = Glist, toa = toa, t0 = t0, t1 = t1, - name = name, behavior = behavior) -} - -#' @export -print.degree_adoption_diagnostic <- function(x, ...) { - cat("Degree and Time of Adoption Diagnostic\n") - cat("======================================\n\n") - - cat("Degree aggregation strategy:", x$degree_strategy, "\n") - - # Sample size can be a scalar or a vector (per behavior when combine='none') - if (length(x$sample_size) == 1L) { - cat("Sample size (adopters only):", x$sample_size, "\n\n") - } else { - cat("Sample size (adopters only): total =", sum(x$sample_size, na.rm = TRUE), "\n") - cat(" By behavior:\n") - beh_names <- names(x$sample_size) - for (j in seq_along(x$sample_size)) { - cat(sprintf(" - %s: %d\n", if (length(beh_names)) beh_names[j] else paste0("B", j), x$sample_size[j])) - } - cat("\n") - } - - undirected <- isTRUE(x$undirected) - - # Helper used in both modes ------------------------------------------------ - explain <- function(label, r, ci, lvl_arg = NA_real_, thr = 0.10) { - # Handle NA correlations gracefully - if (is.na(r)) { - if (is.null(ci)) { - cat(sprintf( - " %s: Weak relationship between centrality and adoption timing:\n r is NA; no CI.\n", - label - )) - } else { - lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 - ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) - cat(sprintf( - " %s: Weak relationship between centrality and adoption timing; %s statistically supported:\n r is NA; CI (%.1f%%) %s 0.\n", - label, - if (ci_includes_zero) "NOT" else "", - lvl_local, - if (ci_includes_zero) "includes" else "excludes" - )) - } - return(invisible()) - } - - abs_big <- abs(r) > thr - - if (is.null(ci)) { - if (!abs_big) { - cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \u2264 %.1f; no CI.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - thr)) - } else if (r > 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - thr)) - } else { - cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers):\n |r| > %.1f; no CI.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - thr)) - } - return(invisible()) - } - - # CI available - ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) - lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 - - if (!abs_big && !ci_includes_zero) { - cat(sprintf(" %s: Weak relationship between centrality and adoption timing; statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) excludes 0.\n", - label, 0.10, lvl_local)) - } else if (!abs_big && ci_includes_zero) { - cat(sprintf(" %s: Weak relationship between centrality and adoption timing; NOT statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) includes 0.\n", - label, 0.10, lvl_local)) - } else if (abs_big && !ci_includes_zero && r > 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - 0.10, lvl_local)) - } else if (abs_big && !ci_includes_zero && r < 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); statistically supported:\n |r| > %.1f; CI (%.1f%%) excludes 0.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - 0.10, lvl_local)) - } else if (abs_big && ci_includes_zero && r > 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - 0.10, lvl_local)) - } else if (abs_big && ci_includes_zero && r < 0) { - cat(sprintf(" %s: Central actors (high %s) tended to adopt late (opposers); NOT statistically supported:\n |r| > %.1f; CI (%.1f%%) includes 0.\n", - label, - if (label == "In-degree") "in-degree" else if (label == "Out-degree") "out-degree" else "degree", - 0.10, lvl_local)) - } - } - - # Branch on combine mode --------------------------------------------------- - if (is.null(x$combine) || x$combine != "none") { - # -------- single behavior OR combined pooled/average/earliest ---------- - cat("\nCorrelations:\n") - if (undirected) { - rdeg <- x$correlations[["indegree_toa"]] # same as outdegree_toa - cat(sprintf(" Degree - Time of Adoption: %6.3f\n", rdeg)) - } else { - cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", x$correlations[["indegree_toa"]])) - cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", x$correlations[["outdegree_toa"]])) - } - - if (!is.null(x$bootstrap)) { - cat("\nBootstrap Results:\n") - cat(sprintf(" Replicates: %d\n", x$bootstrap$R)) - if (!is.null(x$bootstrap$indegree$conf_level)) - cat(sprintf(" Confidence level: %.1f%%\n\n", x$bootstrap$indegree$conf_level * 100)) - - if (!is.null(x$bootstrap$indegree$conf_int)) { - if (undirected) { - cat(" Degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) - if (!is.null(x$bootstrap$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) - if (!is.null(x$bootstrap$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", - x$bootstrap$indegree$conf_level * 100, - x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) - } else { - cat(" In-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$indegree$correlation)) - if (!is.null(x$bootstrap$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$indegree$bias)) - if (!is.null(x$bootstrap$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", - x$bootstrap$indegree$conf_level * 100, - x$bootstrap$indegree$conf_int[1], x$bootstrap$indegree$conf_int[2])) - - if (!is.null(x$bootstrap$outdegree$conf_int)) { - cat(" Out-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", x$bootstrap$outdegree$correlation)) - if (!is.null(x$bootstrap$outdegree$bias)) cat(sprintf(" Bias: %6.3f\n", x$bootstrap$outdegree$bias)) - if (!is.null(x$bootstrap$outdegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", x$bootstrap$outdegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n\n", - x$bootstrap$outdegree$conf_level * 100, - x$bootstrap$outdegree$conf_int[1], x$bootstrap$outdegree$conf_int[2])) - } - } - } else { - if (undirected) { - cat(" Bootstrap estimates (CI unavailable):\n") - cat(sprintf(" Degree: Est=%6.3f%s%s\n", - x$bootstrap$indegree$correlation, - if (!is.null(x$bootstrap$indegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$indegree$bias)) else "", - if (!is.null(x$bootstrap$indegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$indegree$std_error)) else "")) - cat("\n") - } else { - cat(" Bootstrap estimates (CI unavailable):\n") - cat(sprintf(" In-degree: Est=%6.3f%s%s\n", - x$bootstrap$indegree$correlation, - if (!is.null(x$bootstrap$indegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$indegree$bias)) else "", - if (!is.null(x$bootstrap$indegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$indegree$std_error)) else "")) - cat(sprintf(" Out-degree: Est=%6.3f%s%s\n\n", - x$bootstrap$outdegree$correlation, - if (!is.null(x$bootstrap$outdegree$bias)) paste0(", Bias=", sprintf("%6.3f", x$bootstrap$outdegree$bias)) else "", - if (!is.null(x$bootstrap$outdegree$std_error)) paste0(", SE=", sprintf("%6.3f", x$bootstrap$outdegree$std_error)) else "")) - } - } - } - - cat("Interpretation:\n") - if (undirected) { - rdeg <- x$correlations[["indegree_toa"]] - has_boot <- !is.null(x$bootstrap) - deg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL - lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level * 100 else NA_real_ - explain("Degree", rdeg, deg_ci, lvl_arg = lvl) - } else { - indeg <- x$correlations[["indegree_toa"]] - outdeg <- x$correlations[["outdegree_toa"]] - has_boot <- !is.null(x$bootstrap) - indeg_ci <- if (has_boot && !is.null(x$bootstrap$indegree$conf_int)) x$bootstrap$indegree$conf_int else NULL - outdeg_ci <- if (has_boot && !is.null(x$bootstrap$outdegree$conf_int)) x$bootstrap$outdegree$conf_int else NULL - lvl <- if (has_boot && !is.null(x$bootstrap$indegree$conf_level)) x$bootstrap$indegree$conf_level * 100 else NA_real_ - explain("In-degree", indeg, indeg_ci, lvl_arg = lvl) - explain("Out-degree", outdeg, outdeg_ci, lvl_arg = lvl) - } - invisible(x) - return(invisible(x)) - } - - # -------------------------- combine == "none" ----------------------------- - cat("Combination mode:", "none (per behavior)\n\n") - - # Correlations section - cat("Correlations (per behavior):\n") - beh_names <- colnames(x$correlations) - for (j in seq_len(ncol(x$correlations))) { - bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) - cat(sprintf(" [%s]\n", bname)) - if (undirected) { - rdeg <- x$correlations["indegree_toa", j] - cat(sprintf(" Degree - Time of Adoption: %6.3f\n", rdeg)) - } else { - r_in <- x$correlations["indegree_toa", j] - r_out <- x$correlations["outdegree_toa", j] - cat(sprintf(" In-degree - Time of Adoption: %6.3f\n", r_in)) - cat(sprintf(" Out-degree - Time of Adoption: %6.3f\n", r_out)) - } - } - cat("\n") - - # Bootstrap section (if any) - if (!is.null(x$bootstrap)) { - any_boot <- any(vapply(x$bootstrap, function(b) !is.null(b), logical(1))) - if (any_boot) { - cat("Bootstrap Results (per behavior):\n") - for (j in seq_len(ncol(x$correlations))) { - bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) - b <- x$bootstrap[[j]] - if (is.null(b)) next - cat(sprintf(" [%s]\n", bname)) - if (!is.null(b$R)) cat(sprintf(" Replicates: %d\n", b$R)) - if (!is.null(b$indegree$conf_level)) cat(sprintf(" Confidence level: %.1f%%\n", b$indegree$conf_level * 100)) - if (!is.null(b$indegree$conf_int)) { - if (undirected) { - cat(" Degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", b$indegree$correlation)) - if (!is.null(b$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$indegree$bias)) - if (!is.null(b$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", - b$indegree$conf_level * 100, b$indegree$conf_int[1], b$indegree$conf_int[2])) - } else { - cat(" In-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", b$indegree$correlation)) - if (!is.null(b$indegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$indegree$bias)) - if (!is.null(b$indegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$indegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", - b$indegree$conf_level * 100, b$indegree$conf_int[1], b$indegree$conf_int[2])) - } - } - if (!undirected && !is.null(b$outdegree$conf_int)) { - cat(" Out-degree correlation:\n") - cat(sprintf(" Estimate: %6.3f\n", b$outdegree$correlation)) - if (!is.null(b$outdegree$bias)) cat(sprintf(" Bias: %6.3f\n", b$outdegree$bias)) - if (!is.null(b$outdegree$std_error)) cat(sprintf(" Std. Error: %6.3f\n", b$outdegree$std_error)) - cat(sprintf(" %.1f%% CI: [%6.3f, %6.3f]\n", - b$outdegree$conf_level * 100, b$outdegree$conf_int[1], b$outdegree$conf_int[2])) - } - } - cat("\n") - } - } - - # Interpretation (per behavior) - cat("Interpretation (per behavior):\n") - for (j in seq_len(ncol(x$correlations))) { - bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) - if (undirected) { - rdeg <- x$correlations["indegree_toa", j] - b <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL - deg_ci <- if (!is.null(b) && !is.null(b$indegree$conf_int)) b$indegree$conf_int else NULL - lvl <- if (!is.null(b) && !is.null(b$indegree$conf_level)) b$indegree$conf_level * 100 else NA_real_ - cat(sprintf(" [%s]\n", bname)) - explain("Degree", rdeg, deg_ci, lvl_arg = lvl) - } else { - r_in <- x$correlations["indegree_toa", j] - r_out <- x$correlations["outdegree_toa", j] - b <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL - indeg_ci <- if (!is.null(b) && !is.null(b$indegree$conf_int)) b$indegree$conf_int else NULL - outdeg_ci <- if (!is.null(b) && !is.null(b$outdegree$conf_int)) b$outdegree$conf_int else NULL - lvl <- if (!is.null(b) && !is.null(b$indegree$conf_level)) b$indegree$conf_level * 100 else NA_real_ - cat(sprintf(" [%s]\n", bname)) - explain("In-degree", r_in, indeg_ci, lvl_arg = lvl) - explain("Out-degree", r_out, outdeg_ci, lvl_arg = lvl) - } - } - - invisible(x) -} - From 72234a289ca8ec12c3ca4e8766d278551837978c Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 12 Sep 2025 04:58:00 +0000 Subject: [PATCH 07/18] Updating docs --- .devcontainer/Dockerfile | 12 ++-- DESCRIPTION | 1 + man/degree_adoption_diagnostic.Rd | 96 ++++++++++++++----------------- 3 files changed, 51 insertions(+), 58 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index 39a03aa..77347cb 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,16 +1,16 @@ -FROM rocker/tidyverse:4.4.0 +FROM rocker/tidyverse:4.5.0 # RUN \ # echo 'options(repos=c(CRAN="https://packagemanager.posit.co/cran/__linux__/bookworm/latest"))' >> ~/.Rprofile && \ # Rscript --vanilla -e 'getOption("repos")' -# Adding R packages -RUN install2.r Rcpp sna network networkDynamic Matrix MASS MatchIt SparseM igraph \ - viridisLite covr testthat knitr rmarkdown ape RSiena survival RcppArmadillo - -RUN install2.r languageserver httpgd +RUN install2.r --error languageserver && installGithub.r nx10/httpgd RUN apt-get update && apt-get install --no-install-recommends -y valgrind gdb \ libglpk-dev +# Adding R packages +RUN install2.r --error Rcpp sna network networkDynamic Matrix MASS MatchIt SparseM igraph \ + viridisLite covr testthat knitr rmarkdown ape RSiena survival RcppArmadillo + CMD ["bash"] diff --git a/DESCRIPTION b/DESCRIPTION index be64f9c..041f783 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -66,6 +66,7 @@ Collate: 'bootnet.r' 'citer_environment.R' 'data.r' + 'degree_adoption_diagnostic.R' 'diffnet-c.R' 'diffnet-class.r' 'diffnet-indexing.r' diff --git a/man/degree_adoption_diagnostic.Rd b/man/degree_adoption_diagnostic.Rd index a553e4d..591da4c 100644 --- a/man/degree_adoption_diagnostic.Rd +++ b/man/degree_adoption_diagnostic.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/stats.R +% Please edit documentation in R/degree_adoption_diagnostic.R \name{degree_adoption_diagnostic} \alias{degree_adoption_diagnostic} \title{Degree and Time of Adoption Diagnostic} @@ -10,24 +10,27 @@ degree_adoption_diagnostic( bootstrap = TRUE, R = 1000, conf.level = 0.95, - valued = getOption("diffnet.valued", FALSE), toa = NULL, t0 = NULL, t1 = NULL, - name = "A diffusion network", + name = NULL, behavior = NULL, combine = c("none", "pooled", "average", "earliest"), - min_adopters = 3L, + min_adopters = 3, + valued = getOption("diffnet.valued", FALSE), ... ) } \arguments{ -\item{graph}{A \code{\link{diffnet}} object or a graph data structure (classes include -\code{array} (\eqn{n\times n \times T}{n*n*T}), \code{dgCMatrix} (sparse), -\code{igraph}, etc.; see \link{netdiffuseR-graphs}).} +\item{graph}{A `[diffnet()]` object or a graph data structure (classes include +`array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), +`igraph`, etc.; see [netdiffuseR-graphs]).} \item{degree_strategy}{Character scalar. How to aggregate degree measures across -time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}.} +time periods: +- `"mean"` (default): Average degree across all time periods +- `"first"`: Degree in the first time period +- `"last"`: Degree in the last time period} \item{bootstrap}{Logical scalar. Whether to compute bootstrap confidence intervals.} @@ -35,48 +38,48 @@ time periods: \code{"mean"} (default), \code{"first"}, or \code{"last"}.} \item{conf.level}{Numeric scalar. Confidence level for bootstrap intervals (default 0.95).} -\item{valued}{Logical scalar. Whether to use edge weights in degree calculations.} - \item{toa}{Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} -matrix (multi-behavior) with times of adoption. Required when \code{graph} is not a \code{diffnet}.} +matrix (multi-behavior) with times of adoption. Required when `graph` is not a `diffnet`.} \item{t0, t1}{Optional integer scalars defining the first and last observed -periods. If missing and \code{toa} is provided, \code{t0} defaults to 1 -and \code{t1} to \code{max(toa, na.rm=TRUE)}.} +periods. If missing and `toa` is provided, `t0` defaults to 1 +and `t1` to `max(toa, na.rm=TRUE)`.} \item{name}{Optional character scalars used only when coercing -inputs into a \code{diffnet} object (passed to \code{new_diffnet}).} +inputs into a `diffnet` object (passed to `new_diffnet`).} -\item{behavior}{Which behaviors to include when \code{toa} is a matrix (multi-diffusion). -Can be \code{NULL} (all), a numeric index vector, or a character vector matching \code{colnames(toa)}.} +\item{behavior}{Which behaviors to include when `toa` is a matrix (multi-diffusion). +Can be `NULL` (all), a numeric index vector, or a character vector matching `colnames(toa)`.} -\item{combine}{Character scalar. How to combine multiple behaviors when \code{toa} is a matrix: -\code{"none"} (analyze each behavior separately), \code{"pooled"} (stack rows across behaviors), -\code{"average"} (per-actor mean of TOA across selected behaviors), or -\code{"earliest"} (per-actor minimum TOA). Ignored for single-behavior.} +\item{combine}{Character scalar. How to combine multiple behaviors when `toa` is a matrix: +- `"none"` (analyze each behavior separately) +- `"pooled"` (stack rows across behaviors) +- `"average"` (per-actor mean of TOA across selected behaviors) +- `"earliest"` (per-actor minimum TOA) +Ignored for single-behavior.} \item{min_adopters}{Integer scalar. Minimum number of adopters required to compute correlations for any analysis cell (default 3).} -\item{...}{Additional arguments passed on when coercing to \code{diffnet}.} +\item{valued}{Logical scalar. Whether to use edge weights in degree calculations.} + +\item{...}{Additional arguments passed on when coercing to `diffnet`.} } \value{ -When analyzing a single behavior (or when \code{combine!="none"}), a list with: +When analyzing a single behavior (or when `combine!="none"`), a list with: \item{correlations}{Named numeric vector with correlations between in-degree/out-degree and time of adoption} -\item{bootstrap}{List with bootstrap results when \code{bootstrap = TRUE}, otherwise \code{NULL}} +\item{bootstrap}{List with bootstrap results when `bootstrap = TRUE`, otherwise `NULL`} \item{call}{The matched call} \item{degree_strategy}{The degree aggregation strategy used} \item{sample_size}{Number of rows included in the analysis (adopter rows)} -\item{combine}{\code{NULL} for single-behavior; otherwise the combination rule used.} - -When \code{combine="none"} with multiple behaviors, returns the same structure, except: -\itemize{ - \item \code{correlations} is a \eqn{2\times Q^*}{2 x Q*} matrix with rows \code{c("indegree_toa","outdegree_toa")} - and one column per analyzed behavior. - \item \code{bootstrap} is a named list with one entry per behavior (each like the single-behavior case), or \code{NULL} if \code{bootstrap=FALSE}. - \item \code{sample_size} is an integer vector named by behavior. - \item \code{combine} is \code{"none"}. -} +\item{combine}{`NULL` for single-behavior; otherwise the combination rule used.} + +When `combine="none"` with multiple behaviors, returns the same structure, except: +- `correlations` is a \eqn{2\times Q^*}{2 x Q*} matrix with rows `c("indegree_toa","outdegree_toa")` + and one column per analyzed behavior. +- `bootstrap` is a named list with one entry per behavior (each like the single-behavior case), or `NULL` if `bootstrap=FALSE`. +- `sample_size` is an integer vector named by behavior. +- `combine` is `"none"`. } \description{ Analyzes the correlation between in-degree, out-degree, and time of adoption @@ -89,24 +92,15 @@ This diagnostic function computes correlations between degree centrality measure that central actors (opinion leaders) adopted early, while negative correlations suggest they adopted late. -The degree measures can be aggregated across time periods in different ways: -\itemize{ - \item \code{"mean"}: Average degree across all time periods - \item \code{"first"}: Degree in the first time period - \item \code{"last"}: Degree in the last time period -} - -When \code{bootstrap = TRUE}, the function uses the \pkg{boot} package to +When `bootstrap = TRUE`, the function uses the `boot` package to compute bootstrap confidence intervals for the correlations. -When \code{toa} is a matrix (multi-diffusion), degree vectors are computed once and -reused; the time of adoption is combined according to \code{combine}: -\itemize{ - \item \code{"none"}: computes separate results per behavior (see Value). - \item \code{"pooled"}: stacks (actor, behavior) rows for adopters and runs a single analysis. - \item \code{"average"}: one row per actor using the mean TOA of adopted behaviors. - \item \code{"earliest"}: one row per actor using the minimum TOA of adopted behaviors. -} +When `toa` is a matrix (multi-diffusion), degree vectors are computed once and +reused; the time of adoption is combined according to `combine`: +- `"none"`: computes separate results per behavior (see Value). +- `"pooled"`: stacks (actor, behavior) rows for adopters and runs a single analysis. +- `"average"`: one row per actor using the mean TOA of adopted behaviors. +- `"earliest"`: one row per actor using the minimum TOA of adopted behaviors. } \examples{ # Basic usage with Korean Family Planning data @@ -123,7 +117,6 @@ result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "fi result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") # Multi-diffusion (toy) ---------------------------------------------------- -\dontrun{ set.seed(999) n <- 40; t <- 5; q <- 2 garr <- rgraph_ws(n, t, p=.3) @@ -134,11 +127,10 @@ degree_adoption_diagnostic(diffnet_multi, combine = "pooled", bootstrap = FALSE) # per-behavior (matrix of correlations; one column per behavior) degree_adoption_diagnostic(diffnet_multi, combine = "none", bootstrap = FALSE) -} } \seealso{ -\code{\link{dgr}}, \code{\link{diffreg}}, \code{\link{exposure}} +`[dgr()]`, `[diffreg()]`, `[exposure()]` Other statistics: \code{\link{bass}}, From d193bec583f2a3b7f7ae4ba337113f9dde4b88d8 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 12 Sep 2025 20:58:06 +0000 Subject: [PATCH 08/18] Adding new workflow and working on R CMD check --- .devcontainer/devcontainer.json | 5 +- .github/workflows/build-targz.yml | 38 ++++++++++++++ .github/workflows/pkgdown.yml | 2 +- .github/workflows/r.yml | 87 +++++++------------------------ 4 files changed, 61 insertions(+), 71 deletions(-) create mode 100644 .github/workflows/build-targz.yml diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 8b58911..4ec8930 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -1,7 +1,7 @@ // For format details, see https://aka.ms/devcontainer.json. For config options, see the // README at: https://github.com/devcontainers/templates/tree/main/src/cpp { - "name": "epiworldR", + "name": "netdiffuseR", "build": { "dockerfile": "Dockerfile" }, @@ -11,7 +11,8 @@ "reditorsupport.r", "rdebugger.r-debugger", "quarto.quarto", - "tianyishi.rmarkdown" + "tianyishi.rmarkdown", + "github.vscode-github-actions" ] } }, diff --git a/.github/workflows/build-targz.yml b/.github/workflows/build-targz.yml new file mode 100644 index 0000000..936ad5a --- /dev/null +++ b/.github/workflows/build-targz.yml @@ -0,0 +1,38 @@ +# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag. +# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions +on: + push: + branches: + - main + - master + pull_request: + branches: + - main + - master + workflow_dispatch: + +name: Build and upload the tar.gz + +jobs: + R-build: + runs-on: ubuntu-latest + name: Build the package + steps: + - uses: actions/checkout@v4 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: 'release' + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r-dependencies@v2 + + - name: Build the package + run: R CMD build . + + - name: Upload the artifact + uses: actions/upload-artifact@v5 + with: + name: package + path: "*.tar.gz" diff --git a/.github/workflows/pkgdown.yml b/.github/workflows/pkgdown.yml index a0d1eba..23bad37 100644 --- a/.github/workflows/pkgdown.yml +++ b/.github/workflows/pkgdown.yml @@ -44,7 +44,7 @@ jobs: shell: Rscript {0} - name: Upload artifact for GH pages deployment - uses: actions/upload-pages-artifact@v3 + uses: actions/upload-pages-artifact@v5 with: path: "docs/" diff --git a/.github/workflows/r.yml b/.github/workflows/r.yml index 8472306..f43258c 100644 --- a/.github/workflows/r.yml +++ b/.github/workflows/r.yml @@ -22,11 +22,12 @@ jobs: fail-fast: false matrix: config: - - {os: windows-latest, r: 'release'} - - {os: macOS-latest, r: 'release'} - - {os: ubuntu-latest, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} - - {os: ubuntu-latest, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} - - {os: ubuntu-latest, r: 'devel', valgrind: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"} + - {os: windows-latest, r: 'release', valgrind: false} + - {os: macOS-latest, r: 'release', valgrind: false} + - {os: ubuntu-latest, r: 'release', valgrind: false} + - {os: ubuntu-latest, r: 'devel', valgrind: false} + - {os: ubuntu-latest, r: 'devel', valgrind: true} + - {os: macos-26, r: 'release', valgrind: false} env: R_REMOTES_NO_ERRORS_FROM_WARNINGS: true @@ -42,73 +43,23 @@ jobs: - uses: r-lib/actions/setup-pandoc@v2 - - name: Query dependencies - run: | - install.packages('remotes') - saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2) - writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version") - shell: Rscript {0} - - - name: Restore R package cache - if: runner.os != 'Windows' - uses: actions/cache@v4 - with: - path: ${{ env.R_LIBS_USER }} - key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }} - restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1- - - - name: Install system dependencies - if: runner.os == 'Linux' - run: | - while read -r cmd - do - eval sudo $cmd - done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))') - - name: Install dependencies - run: | - remotes::install_deps(dependencies = TRUE) - remotes::install_cran("rcmdcheck") - shell: Rscript {0} - - - name: Check - if: ${{ matrix.config.valgrind != true }} - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: | - options(crayon.enabled = TRUE) - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "error", check_dir = "check") - shell: Rscript {0} + - uses: r-lib/actions/setup-r-dependencies@v2 - - name: Setup valgrind - if: ${{ matrix.config.valgrind == true }} + - name: Install valgrind + if: ${{ matrix.config.valgrind }} run: | sudo apt-get update - sudo apt-get install -y valgrind --no-install-recommends - shell: bash + sudo apt-get install -y --no-install-recommends valgrind - - name: Check with Valgrind - if: ${{ matrix.config.valgrind == true }} - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: | - options(crayon.enabled = TRUE) - rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran", "--use-valgrind"), error_on = "error", check_dir = "check") - shell: Rscript {0} - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@main + - uses: r-lib/actions/check-r-package@v2 + if: ${{ matrix.config.valgrind }} with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check + args: 'c("--as-cran", "--use-valgrind")' + + - uses: r-lib/actions/check-r-package@v2 + if: ${{ !matrix.config.valgrind }} + + + - - name: Build the package - if: ${{ matrix.config.os == 'ubuntu-latest' && (matrix.config.r == 'release' || matrix.config.r == 'devel') && (matrix.config.valgrind != true) }} - run: R CMD build . - - uses: actions/upload-artifact@v4 - if: ${{ matrix.config.os == 'ubuntu-latest' && (matrix.config.r == 'release' || matrix.config.r == 'devel') && (matrix.config.valgrind != true) }} - with: - name: netdiffuseR-built-package-${{ matrix.config.os }}-${{ matrix.config.r }} - path: netdiffuseR_*.tar.gz - retention-days: 7 From 30818c01585f0f7742868034e1b87a97cff1c15a Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 12 Sep 2025 21:13:04 +0000 Subject: [PATCH 09/18] Fixing actions --- .github/workflows/build-targz.yml | 2 +- .github/workflows/r.yml | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build-targz.yml b/.github/workflows/build-targz.yml index 936ad5a..a23a0cf 100644 --- a/.github/workflows/build-targz.yml +++ b/.github/workflows/build-targz.yml @@ -32,7 +32,7 @@ jobs: run: R CMD build . - name: Upload the artifact - uses: actions/upload-artifact@v5 + uses: actions/upload-artifact@v4 with: name: package path: "*.tar.gz" diff --git a/.github/workflows/r.yml b/.github/workflows/r.yml index f43258c..f18049b 100644 --- a/.github/workflows/r.yml +++ b/.github/workflows/r.yml @@ -44,6 +44,10 @@ jobs: - uses: r-lib/actions/setup-pandoc@v2 - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: | + any::rcmdcheck + needs: check - name: Install valgrind if: ${{ matrix.config.valgrind }} From 5a90114d3b41114b38e74bc28cba6b3034221546 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 12 Sep 2025 23:10:48 +0000 Subject: [PATCH 10/18] Updating readme --- .github/workflows/r.yml | 2 +- README.md | 25 +++++++++++++++++-------- 2 files changed, 18 insertions(+), 9 deletions(-) diff --git a/.github/workflows/r.yml b/.github/workflows/r.yml index f18049b..bdfe509 100644 --- a/.github/workflows/r.yml +++ b/.github/workflows/r.yml @@ -16,7 +16,7 @@ jobs: R-CMD-check: runs-on: ${{ matrix.config.os }} - name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) (valgrind: ${{ matrix.config.valgrind }}) strategy: fail-fast: false diff --git a/README.md b/README.md index b92c041..f9a223c 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,10 @@ This example has been taken from the package’s vignettes: library(netdiffuseR) ``` + Thank you for using netdiffuseR! Please consider citing it in your work. + You can find the citation information by running + citation("netdiffuseR") + Attaching package: 'netdiffuseR' @@ -390,18 +394,23 @@ sessionInfo() ``` R version 4.5.0 (2025-04-11) - Platform: aarch64-apple-darwin24.2.0 - Running under: macOS Sequoia 15.0.1 + Platform: x86_64-pc-linux-gnu + Running under: Ubuntu 24.04.2 LTS Matrix products: default - BLAS: /opt/homebrew/Cellar/openblas/0.3.29/lib/libopenblasp-r0.3.29.dylib - LAPACK: /opt/homebrew/Cellar/r/4.5.0/lib/R/lib/libRlapack.dylib; LAPACK version 3.12.1 + BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 + LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0 locale: - [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 + [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C + [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 + [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 + [7] LC_PAPER=en_US.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C + [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C - time zone: America/Denver - tzcode source: internal + time zone: Etc/UTC + tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base @@ -415,7 +424,7 @@ sessionInfo() [7] networkLite_1.1.0 boot_1.3-31 yaml_2.3.10 [10] fastmap_1.2.0 lattice_0.22-6 coda_0.19-4.1 [13] R6_2.6.1 generics_0.1.4 MatchIt_4.7.2 - [16] igraph_2.1.4.9046 knitr_1.50 MASS_7.3-65 + [16] igraph_2.1.4 knitr_1.50 MASS_7.3-65 [19] backports_1.5.0 tibble_3.3.0 statnet.common_4.12.0 [22] pillar_1.10.2 rlang_1.1.6 xfun_0.52 [25] viridisLite_0.4.2 cli_3.6.5 magrittr_2.0.3 From f7eb8fe240aea72c9210168c9c242d488353d4f2 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Fri, 12 Sep 2025 17:15:32 -0600 Subject: [PATCH 11/18] Fix formatting of valgrind variable in workflow name --- .github/workflows/r.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/r.yml b/.github/workflows/r.yml index bdfe509..4be348a 100644 --- a/.github/workflows/r.yml +++ b/.github/workflows/r.yml @@ -16,7 +16,7 @@ jobs: R-CMD-check: runs-on: ${{ matrix.config.os }} - name: ${{ matrix.config.os }} (${{ matrix.config.r }}) (valgrind: ${{ matrix.config.valgrind }}) + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) (valgrind:${{ matrix.config.valgrind }}) strategy: fail-fast: false From 5c83647b8c823aaba6f2eb02fd00dc4d1709a813 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 16 Sep 2025 23:05:11 -0300 Subject: [PATCH 12/18] Addressing errors and warnings --- R/degree_adoption_diagnostic.R | 310 +++++++++++------- man/degree_adoption_diagnostic.Rd | 8 +- .../test-degree-adoption-diagnostic.R | 10 +- 3 files changed, 203 insertions(+), 125 deletions(-) diff --git a/R/degree_adoption_diagnostic.R b/R/degree_adoption_diagnostic.R index cd1eaee..d135947 100644 --- a/R/degree_adoption_diagnostic.R +++ b/R/degree_adoption_diagnostic.R @@ -4,9 +4,9 @@ #' to identify whether opinion leaders were early adopters (supporters) or late #' adopters (opposers). #' -#' @param graph A `[diffnet()]` object or a graph data structure (classes include -#' `array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), -#' `igraph`, etc.; see [netdiffuseR-graphs]). +#' @param graph A `[diffnet()]` object or a graph data structure (classes include +#' `array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), +#' `igraph`, etc.; see [netdiffuseR-graphs]). #' @param toa Integer vector of length \eqn{n} (single behavior) or an \eqn{n\times Q}{n*Q} #' matrix (multi-behavior) with times of adoption. Required when `graph` is not a `diffnet`. #' @param t0,t1 Optional integer scalars defining the first and last observed @@ -25,7 +25,7 @@ #' @param min_adopters Integer scalar. Minimum number of adopters required to compute correlations #' for any analysis cell (default 3). #' @param degree_strategy Character scalar. How to aggregate degree measures across -#' time periods: +#' time periods: #' - `"mean"` (default): Average degree across all time periods #' - `"first"`: Degree in the first time period #' - `"last"`: Degree in the last time period @@ -38,12 +38,12 @@ #' @details #' This diagnostic function computes correlations between degree centrality measures #' (in-degree and out-degree) and time of adoption. Positive correlations suggest -#' that central actors (opinion leaders) adopted early, while negative correlations +#' that central actors (opinion leaders) adopted early, while negative correlations #' suggest they adopted late. -#' +#' #' When `bootstrap = TRUE`, the function uses the `boot` package to #' compute bootstrap confidence intervals for the correlations. -#' +#' #' When `toa` is a matrix (multi-diffusion), degree vectors are computed once and #' reused; the time of adoption is combined according to `combine`: #' - `"none"`: computes separate results per behavior (see Value). @@ -71,15 +71,15 @@ #' data(kfamilyDiffNet) #' result_basics <- degree_adoption_diagnostic(kfamilyDiffNet, bootstrap = FALSE) #' print(result_basics) -#' +#' #' # With bootstrap confidence intervals #' result_boot <- degree_adoption_diagnostic(kfamilyDiffNet) #' print(result_boot) -#' +#' #' # Different degree aggregation strategies #' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") #' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") -#' +#' #' # Multi-diffusion (toy) ---------------------------------------------------- #' set.seed(999) #' n <- 40; t <- 5; q <- 2 @@ -110,18 +110,33 @@ degree_adoption_diagnostic <- function( valued = getOption("diffnet.valued", FALSE), ... ) { + # Check that bootstrap is a logical scalar + if (!is.logical(bootstrap) || length(bootstrap) != 1 || is.na(bootstrap)) { + stop("'bootstrap' must be a logical scalar") + } + + # Check that R is a positive integer + if (!is.numeric(R) || length(R) != 1 || is.na(R) || R < 1 || R != as.integer(R)) { + stop("'R' must be a positive integer") + } + + # Check that conf.level is a numeric scalar between 0 and 1 + if (!is.numeric(conf.level) || length(conf.level) != 1 || is.na(conf.level) || conf.level <= 0 || conf.level >= 1) { + stop("'conf.level' must be between 0 and 1") + } + # Match and validate arguments degree_strategy <- match.arg(degree_strategy) combine <- match.arg(combine) - + # Store the original call original_call <- match.call() - + # Handle input processing and validation graph_and_toa <- process_graph_input(graph, toa, t0, t1, name, ...) graph <- graph_and_toa$graph toa <- graph_and_toa$toa - + # Get the number of behaviors if (is.matrix(toa)) { Q <- ncol(toa) @@ -130,7 +145,7 @@ degree_adoption_diagnostic <- function( Q <- 1 behavior_names <- NULL } - + # Filter behaviors if requested if (Q > 1 && !is.null(behavior)) { if (is.character(behavior)) { @@ -139,60 +154,55 @@ degree_adoption_diagnostic <- function( } behavior_indices <- match(behavior, colnames(toa)) if (any(is.na(behavior_indices))) { - stop("Some behavior names not found in colnames(toa): ", + stop("Some behavior names not found in colnames(toa): ", paste(behavior[is.na(behavior_indices)], collapse = ", ")) } } else if (is.numeric(behavior)) { behavior_indices <- behavior if (any(behavior_indices < 1 | behavior_indices > Q)) { - stop("behavior indices must be between 1 and ", Q) + stop("behavior index out of range: must be between 1 and ", Q) } } else { stop("behavior must be NULL, numeric indices, or character names") } - + toa <- toa[, behavior_indices, drop = FALSE] Q <- ncol(toa) behavior_names <- if (length(colnames(toa))) colnames(toa) else paste0("B", seq_len(Q)) } - + # Compute degree measures degrees <- compute_degree_measures(graph, degree_strategy, valued) - - # Handle multi-diffusion analysis - if (Q > 1 && combine == "none") { + + # Handle multi-diffusion analysis: for combine = "none", always analyze per behavior and do not error for too few adopters + if (combine == "none" && is.matrix(toa)) { return(analyze_multi_behaviors_separately( - degrees, toa, min_adopters, bootstrap, R, conf.level, - behavior_names, degree_strategy, original_call + degrees, toa, min_adopters, bootstrap, R, conf.level, + behavior_names, degree_strategy, original_call, graph )) } - + # Single behavior or combined analysis combined_data <- prepare_combined_data(degrees, toa, combine, min_adopters, Q) - + if (nrow(combined_data) < min_adopters) { - warning("Insufficient adopters (n=", nrow(combined_data), ") for correlation analysis") - return(create_empty_result(degree_strategy, original_call, combine, nrow(combined_data))) + stop("Insufficient adopters for correlation analysis. (n=", nrow(combined_data), + ", minimum = ", min_adopters, ").") } - + # Compute correlations correlations <- compute_correlations(combined_data) - + # Bootstrap if requested bootstrap_results <- if (bootstrap) { compute_bootstrap_results(combined_data, R, conf.level) } else { NULL } - - # Determine if undirected - undirected <- if (inherits(graph, "diffnet")) { - is_undirected(graph) - } else { - # For other graph types, check if symmetric - check_undirected_graph(graph) - } - + + # Determine if undirected (graph is always a diffnet here) + undirected <- isTRUE(is_undirected(graph)) + # Return results structure(list( correlations = correlations, @@ -214,16 +224,45 @@ process_graph_input <- function(graph, toa, t0, t1, name, ...) { } return(list(graph = graph, toa = graph$toa)) } - + if (is.null(toa)) { stop("toa argument is required when graph is not a diffnet object") } - + + # If graph is a list, ensure all elements are dgCMatrix + if (is.list(graph)) { + graph <- lapply(graph, function(g) { + if (inherits(g, "dgCMatrix")) return(g) + if (is.matrix(g)) return(as(Matrix::Matrix(g, sparse = TRUE), "dgCMatrix")) + stop("All elements of the graph list must be matrices or dgCMatrix.") + }) + } + + # If graph is a single static adjacency (matrix/dgCMatrix), expand it to T layers to avoid recycling warnings in new_diffnet + if (inherits(graph, "dgCMatrix") || is.matrix(graph)) { + if (is.null(t0)) t0 <- 1 + if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) + Tlen <- as.integer(t1 - t0 + 1L) + if (is.finite(Tlen) && Tlen > 1L) { + gmat <- if (inherits(graph, "dgCMatrix")) graph else as(Matrix::Matrix(graph, sparse = TRUE), "dgCMatrix") + graph <- replicate(Tlen, gmat, simplify = FALSE) + } + } + + # Detect undirectedness on the raw input before building diffnet + undirected_flag <- check_undirected_graph(graph) + # Convert to diffnet if (is.null(t0)) t0 <- 1 if (is.null(t1)) t1 <- max(toa, na.rm = TRUE) - - graph <- new_diffnet(graph, toa, t0 = t0, t1 = t1, name = name, ...) + + graph <- new_diffnet( + graph, toa, + t0 = t0, t1 = t1, + name = name, + undirected = undirected_flag, + ... + ) return(list(graph = graph, toa = toa)) } @@ -231,50 +270,68 @@ compute_degree_measures <- function(graph, degree_strategy, valued) { if (degree_strategy == "mean") { indegree <- rowMeans(dgr(graph, cmode = "indegree", valued = valued), na.rm = TRUE) outdegree <- rowMeans(dgr(graph, cmode = "outdegree", valued = valued), na.rm = TRUE) - } else if (degree_strategy == "first") { - deg_matrix <- dgr(graph, valued = valued) - indegree <- deg_matrix[, 1, "indegree"] - outdegree <- deg_matrix[, 1, "outdegree"] - } else if (degree_strategy == "last") { + } else { deg_matrix <- dgr(graph, valued = valued) - last_time <- dim(deg_matrix)[2] - indegree <- deg_matrix[, last_time, "indegree"] - outdegree <- deg_matrix[, last_time, "outdegree"] + if (length(dim(deg_matrix)) == 3) { + # Dynamic case + if (degree_strategy == "first") { + indegree <- deg_matrix[, 1, "indegree"] + outdegree <- deg_matrix[, 1, "outdegree"] + } else if (degree_strategy == "last") { + last_time <- dim(deg_matrix)[2] + indegree <- deg_matrix[, last_time, "indegree"] + outdegree <- deg_matrix[, last_time, "outdegree"] + } + } else if (length(dim(deg_matrix)) == 2) { + # Static case: check for column names, else use position + cn <- colnames(deg_matrix) + if (!is.null(cn) && all(c("indegree", "outdegree") %in% cn)) { + indegree <- deg_matrix[, "indegree"] + outdegree <- deg_matrix[, "outdegree"] + } else if (ncol(deg_matrix) >= 2) { + indegree <- deg_matrix[, 1] + outdegree <- deg_matrix[, 2] + } else { + stop("Degree matrix does not have expected columns for static graph.") + } + } else { + stop("Unexpected degree matrix dimensions in compute_degree_measures.") + } } - + list(indegree = indegree, outdegree = outdegree) } -analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, bootstrap, R, conf.level, behavior_names, degree_strategy, original_call) { +analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, bootstrap, R, conf.level, behavior_names, degree_strategy, original_call, graph) { Q <- ncol(toa) - + # Initialize results containers correlations_matrix <- matrix(NA_real_, nrow = 2, ncol = Q) rownames(correlations_matrix) <- c("indegree_toa", "outdegree_toa") colnames(correlations_matrix) <- behavior_names - + sample_sizes <- integer(Q) names(sample_sizes) <- behavior_names - + bootstrap_results <- if (bootstrap) vector("list", Q) else NULL if (bootstrap) names(bootstrap_results) <- behavior_names - + # Analyze each behavior for (q in seq_len(Q)) { toa_q <- toa[, q] adopters_q <- which(!is.na(toa_q)) - + if (length(adopters_q) >= min_adopters) { data_q <- data.frame( indegree = degrees$indegree[adopters_q], outdegree = degrees$outdegree[adopters_q], toa = toa_q[adopters_q] ) - - correlations_matrix[1, q] <- cor(data_q$indegree, data_q$toa, use = "complete.obs") - correlations_matrix[2, q] <- cor(data_q$outdegree, data_q$toa, use = "complete.obs") + + correlations_matrix[1, q] <- cor_safe(data_q$indegree, data_q$toa ) + correlations_matrix[2, q] <- cor_safe(data_q$outdegree, data_q$toa ) sample_sizes[q] <- nrow(data_q) - + if (bootstrap) { bootstrap_results[[q]] <- compute_bootstrap_results(data_q, R, conf.level) } @@ -282,14 +339,14 @@ analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, boots sample_sizes[q] <- length(adopters_q) } } - + # Determine if undirected undirected <- if (inherits(graph, "diffnet")) { is_undirected(graph) } else { check_undirected_graph(graph) } - + structure(list( correlations = correlations_matrix, bootstrap = bootstrap_results, @@ -314,7 +371,7 @@ prepare_combined_data <- function(degrees, toa, combine, min_adopters, Q) { # Pooled: stack all (actor, behavior) rows adopter_rows <- which(!is.na(as.vector(toa))) actor_indices <- ((adopter_rows - 1) %% nrow(toa)) + 1 - + data.frame( indegree = degrees$indegree[actor_indices], outdegree = degrees$outdegree[actor_indices], @@ -325,7 +382,7 @@ prepare_combined_data <- function(degrees, toa, combine, min_adopters, Q) { # Average TOA across behaviors per actor toa_avg <- rowMeans(toa, na.rm = TRUE) adopters <- which(!is.na(toa_avg)) - + data.frame( indegree = degrees$indegree[adopters], outdegree = degrees$outdegree[adopters], @@ -333,10 +390,13 @@ prepare_combined_data <- function(degrees, toa, combine, min_adopters, Q) { ) } else if (combine == "earliest") { # Earliest TOA across behaviors per actor - toa_min <- apply(toa, 1, min, na.rm = TRUE) + toa_min <- apply(toa, 1, function(row) { + if (all(is.na(row))) return(NA_real_) + min(row, na.rm = TRUE) + }) toa_min[is.infinite(toa_min)] <- NA adopters <- which(!is.na(toa_min)) - + data.frame( indegree = degrees$indegree[adopters], outdegree = degrees$outdegree[adopters], @@ -347,8 +407,8 @@ prepare_combined_data <- function(degrees, toa, combine, min_adopters, Q) { compute_correlations <- function(data) { c( - indegree_toa = cor(data$indegree, data$toa, use = "complete.obs"), - outdegree_toa = cor(data$outdegree, data$toa, use = "complete.obs") + indegree_toa = cor_safe(data$indegree, data$toa), + outdegree_toa = cor_safe(data$outdegree, data$toa) ) } @@ -357,34 +417,52 @@ compute_bootstrap_results <- function(data, R, conf.level) { d <- data[indices, ] tryCatch({ c( - indegree_toa = cor(d$indegree, d$toa, use = "complete.obs"), - outdegree_toa = cor(d$outdegree, d$toa, use = "complete.obs") + indegree_toa = cor_safe(d$indegree, d$toa ), + outdegree_toa = cor_safe(d$outdegree, d$toa ) ) }, error = function(e) c(indegree_toa = NA, outdegree_toa = NA)) } - + boot_result <- boot::boot(data, safe_bootstrap, R = R) - - # Compute confidence intervals + + # Observed correlations + obs_indegree <- cor_safe(data$indegree, data$toa) + obs_outdegree <- cor_safe(data$outdegree, data$toa) + + # Bias and standard error + bias_indegree <- mean(boot_result$t[,1], na.rm = TRUE) - obs_indegree + std_error_indegree <- sd(boot_result$t[,1], na.rm = TRUE) + bias_outdegree <- mean(boot_result$t[,2], na.rm = TRUE) - obs_outdegree + std_error_outdegree <- sd(boot_result$t[,2], na.rm = TRUE) + + # Confidence intervals indegree_ci <- tryCatch({ ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 1) ci$percent[4:5] }, error = function(e) c(NA, NA)) - + outdegree_ci <- tryCatch({ ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 2) ci$percent[4:5] }, error = function(e) c(NA, NA)) - + list( indegree = list( + correlation = obs_indegree, + bias = bias_indegree, + std_error = std_error_indegree, conf_int = indegree_ci, conf_level = conf.level ), outdegree = list( + correlation = obs_outdegree, + bias = bias_outdegree, + std_error = std_error_outdegree, conf_int = outdegree_ci, conf_level = conf.level - ) + ), + R = R, + boot_object = boot_result ) } @@ -401,15 +479,16 @@ create_empty_result <- function(degree_strategy, original_call, combine, sample_ } check_undirected_graph <- function(graph) { - if (is.array(graph) && length(dim(graph)) >= 2) { - # Check if symmetric for first time slice - if (length(dim(graph)) == 3) { - return(isSymmetric(graph[,,1])) - } else { - return(isSymmetric(graph)) - } + if (is.list(graph)) { + return(all(sapply(graph, function(g) isSymmetric(as.matrix(g))))) } - FALSE # Default to directed for unknown types + if (is.array(graph) && length(dim(graph)) == 3) { + return(all(sapply(seq_len(dim(graph)[3]), function(t) isSymmetric(as.matrix(graph[,,t]))))) + } + if (is.matrix(graph)) { + return(isSymmetric(as.matrix(graph))) + } + FALSE } # Print method ------------------------------------------------------------ @@ -418,10 +497,10 @@ check_undirected_graph <- function(graph) { print.degree_adoption_diagnostic <- function(x, ...) { cat("Degree and Time of Adoption Diagnostic\n") cat("======================================\n\n") - + # Basic info cat(sprintf("Degree aggregation strategy: %s \n", x$degree_strategy)) - + # Sample size info if (is.null(names(x$sample_size))) { cat(sprintf("Sample size (adopters only): %d \n", x$sample_size)) @@ -450,7 +529,7 @@ print_single_behavior_results <- function(x, undirected) { # Extract correlation values indeg_r <- x$correlations[["indegree_toa"]] outdeg_r <- x$correlations[["outdegree_toa"]] - + # Print correlations cat("\nCorrelations:\n") if (undirected) { @@ -460,10 +539,10 @@ print_single_behavior_results <- function(x, undirected) { cat(sprintf(" In-degree - Time of Adoption: %.3f\n", indeg_r)) cat(sprintf(" Out-degree - Time of Adoption: %.3f\n", outdeg_r)) } - + # Interpretation cat("\nInterpretation:\n") - + if (!is.null(x$bootstrap)) { bootstrap_data <- x$bootstrap deg_ci <- if (undirected && !is.null(bootstrap_data$indegree$conf_int)) { @@ -478,7 +557,7 @@ print_single_behavior_results <- function(x, undirected) { lvl <- if (!is.null(bootstrap_data$indegree$conf_level)) { bootstrap_data$indegree$conf_level * 100 } else NA_real_ - + if (undirected) { explain_degree_correlation("Degree", deg_r, deg_ci, lvl_arg = lvl) } else { @@ -499,7 +578,7 @@ print_multi_behavior_results <- function(x, undirected) { correlations_matrix <- x$correlations Q <- ncol(correlations_matrix) beh_names <- colnames(correlations_matrix) - + # Print correlations matrix cat("\nCorrelations:\n") if (undirected) { @@ -523,14 +602,14 @@ print_multi_behavior_results <- function(x, undirected) { cat(sprintf(" [%s]: %.3f\n", bname, outdeg_row[j])) } } - + # Interpretation per behavior cat("\nInterpretation:\n") for (j in seq_len(Q)) { bname <- if (length(beh_names)) beh_names[j] else paste0("B", j) r_in <- correlations_matrix["indegree_toa", j] r_out <- correlations_matrix["outdegree_toa", j] - + bootstrap_data <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL deg_ci <- if (undirected && !is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_int)) { bootstrap_data$indegree$conf_int @@ -544,7 +623,7 @@ print_multi_behavior_results <- function(x, undirected) { lvl <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_level)) { bootstrap_data$indegree$conf_level * 100 } else NA_real_ - + cat(sprintf(" [%s]\n", bname)) if (undirected) { explain_degree_correlation("Degree", r_in, deg_ci, lvl_arg = lvl) @@ -559,29 +638,17 @@ print_multi_behavior_results <- function(x, undirected) { explain_degree_correlation <- function(label, r, ci, lvl_arg = NA_real_, thr = 0.10) { # Handle NA correlations gracefully if (is.na(r)) { - if (is.null(ci)) { - cat(sprintf( - " %s: Weak relationship between centrality and adoption timing:\n r is NA; no CI.\n", - label - )) - } else { - lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 - ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) - cat(sprintf( - " %s: Weak relationship between centrality and adoption timing; %s statistically supported:\n r is NA; CI (%.1f%%) %s 0.\n", - label, - if (ci_includes_zero) "NOT" else "", - lvl_local, - if (ci_includes_zero) "includes" else "excludes" - )) - } + cat(sprintf( + " %s: Weak relationship between centrality and adoption timing:\n r is NA; no CI.\n", + label + )) return(invisible()) } abs_big <- abs(r) > thr degree_term <- switch(label, "In-degree" = "in-degree", - "Out-degree" = "out-degree", + "Out-degree" = "out-degree", "degree" ) @@ -594,7 +661,7 @@ explain_degree_correlation <- function(label, r, ci, lvl_arg = NA_real_, thr = 0 format_interpretation_no_ci <- function(label, r, abs_big, degree_term, thr) { if (!abs_big) { - cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| ≤ %.1f; no CI.\n", + cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \\u2264 %.1f; no CI.\n", label, degree_term, thr)) } else if (r > 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", @@ -608,9 +675,9 @@ format_interpretation_no_ci <- function(label, r, abs_big, degree_term, thr) { format_interpretation_with_ci <- function(label, r, ci, abs_big, degree_term, thr, lvl_arg) { lvl_local <- if (!is.na(lvl_arg)) lvl_arg else 95 ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) - + if (!abs_big) { - cat(sprintf(" %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| ≤ %.1f; CI (%.1f%%) %s 0.\n", + cat(sprintf(" %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| \\u2264 %.1f; CI (%.1f%%) %s 0.\n", label, degree_term, if (ci_includes_zero) "NOT" else "", thr, lvl_local, @@ -628,4 +695,15 @@ format_interpretation_with_ci <- function(label, r, ci, abs_big, degree_term, th thr, lvl_local, if (ci_includes_zero) "includes" else "excludes")) } -} \ No newline at end of file +} + +# Safe correlation: returns NA (no warnings) if zero-variance or too few pairs +cor_safe <- function(x, y) { + x <- as.numeric(x); y <- as.numeric(y) + ok <- is.finite(x) & is.finite(y) + if (!any(ok)) return(NA_real_) + x <- x[ok]; y <- y[ok] + if (length(x) < 2L) return(NA_real_) + if (sd(x) == 0 || sd(y) == 0) return(NA_real_) + stats::cor(x, y) +} diff --git a/man/degree_adoption_diagnostic.Rd b/man/degree_adoption_diagnostic.Rd index 591da4c..0ddeb9f 100644 --- a/man/degree_adoption_diagnostic.Rd +++ b/man/degree_adoption_diagnostic.Rd @@ -22,12 +22,12 @@ degree_adoption_diagnostic( ) } \arguments{ -\item{graph}{A `[diffnet()]` object or a graph data structure (classes include -`array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), +\item{graph}{A `[diffnet()]` object or a graph data structure (classes include +`array` (\eqn{n\times n \times T}{n*n*T}), `dgCMatrix` (sparse), `igraph`, etc.; see [netdiffuseR-graphs]).} \item{degree_strategy}{Character scalar. How to aggregate degree measures across -time periods: +time periods: - `"mean"` (default): Average degree across all time periods - `"first"`: Degree in the first time period - `"last"`: Degree in the last time period} @@ -89,7 +89,7 @@ adopters (opposers). \details{ This diagnostic function computes correlations between degree centrality measures (in-degree and out-degree) and time of adoption. Positive correlations suggest -that central actors (opinion leaders) adopted early, while negative correlations +that central actors (opinion leaders) adopted early, while negative correlations suggest they adopted late. When `bootstrap = TRUE`, the function uses the `boot` package to diff --git a/tests/testthat/test-degree-adoption-diagnostic.R b/tests/testthat/test-degree-adoption-diagnostic.R index 4396484..90f71c5 100644 --- a/tests/testthat/test-degree-adoption-diagnostic.R +++ b/tests/testthat/test-degree-adoption-diagnostic.R @@ -80,7 +80,7 @@ test_that("degree_adoption_diagnostic handles edge cases", { expect_error( degree_adoption_diagnostic(dn, bootstrap = FALSE), - "At least 3 adopters are required" + "Insufficient adopters for correlation analysis" ) # Test with insufficient adopters @@ -91,18 +91,18 @@ test_that("degree_adoption_diagnostic handles edge cases", { expect_error( degree_adoption_diagnostic(dn, bootstrap = FALSE), - "At least 3 adopters are required" + "Insufficient adopters for correlation analysis" ) }) test_that("degree_adoption_diagnostic input validation", { set.seed(333) - dn <- rdiffnet(15, 3, seed.p.adopt = 0.3) + dn <- rdiffnet(n = 15, t = 3, seed.p.adopt = 0.3) # Test invalid toa for non-diffnet input expect_error( degree_adoption_diagnostic("not a diffnet"), - "When 'graph' is not a diffnet, argument 'toa' must be provided." + "toa argument is required when graph is not a diffnet object" ) # Test valid sparse matrix input @@ -386,6 +386,6 @@ test_that("Behavior selection validates indices/names and min_adopters is enforc # For combined modes, too few rows should still error expect_error( degree_adoption_diagnostic(dn2, behavior = "X", combine = "pooled", min_adopters = 3), - "Not enough adopter rows after combining behaviors" + "Insufficient adopters for correlation analysis" ) }) From e2c03df1e0fc48c6eae7994408a3078c4ddb8c2e Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 16 Sep 2025 23:58:16 -0300 Subject: [PATCH 13/18] Updating printer: combo mode + bootstrap NA fix --- R/degree_adoption_diagnostic.R | 137 +++++++++++++++++++++------------ 1 file changed, 86 insertions(+), 51 deletions(-) diff --git a/R/degree_adoption_diagnostic.R b/R/degree_adoption_diagnostic.R index d135947..1fa9ada 100644 --- a/R/degree_adoption_diagnostic.R +++ b/R/degree_adoption_diagnostic.R @@ -412,57 +412,82 @@ compute_correlations <- function(data) { ) } -compute_bootstrap_results <- function(data, R, conf.level) { - safe_bootstrap <- function(data, indices) { - d <- data[indices, ] - tryCatch({ - c( - indegree_toa = cor_safe(d$indegree, d$toa ), - outdegree_toa = cor_safe(d$outdegree, d$toa ) - ) - }, error = function(e) c(indegree_toa = NA, outdegree_toa = NA)) +compute_bootstrap_results <- function(combined_data, R, conf.level) { + # Compute baseline correlations + base_corr <- compute_correlations(combined_data) + indeg_corr <- base_corr[["indegree_toa"]] + outdeg_corr <- base_corr[["outdegree_toa"]] + + indeg_boot_list <- NULL + out_boot_list <- NULL + + # Out-degree + if (!is.na(outdeg_corr)) { + # Out-degree bootstrap + safe_bootstrap_out <- function(data, indices) { + d <- data[indices, , drop = FALSE] + suppressWarnings(stats::cor(d$outdegree, d$toa, use = "complete.obs")) + } + boot_obj_out <- boot::boot(combined_data, statistic = safe_bootstrap_out, R = R) + bias_out <- mean(boot_obj_out$t, na.rm = TRUE) - outdeg_corr + se_out <- stats::sd(boot_obj_out$t, na.rm = TRUE) + + ci_out <- tryCatch({ + bci <- boot::boot.ci(boot_obj_out, conf = conf.level, type = "perc") + # Percentile CI vector (low, high) + if (!is.null(bci$percent)) bci$percent[4:5] else NULL + }, error = function(e) NULL) + + out_boot_list <- list( + correlation = outdeg_corr, + bias = bias_out, + std_error = se_out, + conf_int = ci_out, + conf_level = conf.level + ) + } else { + # Degenerate case: correlation is NA (do not include CI/SE/CL) + out_boot_list <- list( + correlation = NA_real_ + ) + boot_obj_out <- NULL } - boot_result <- boot::boot(data, safe_bootstrap, R = R) - - # Observed correlations - obs_indegree <- cor_safe(data$indegree, data$toa) - obs_outdegree <- cor_safe(data$outdegree, data$toa) - - # Bias and standard error - bias_indegree <- mean(boot_result$t[,1], na.rm = TRUE) - obs_indegree - std_error_indegree <- sd(boot_result$t[,1], na.rm = TRUE) - bias_outdegree <- mean(boot_result$t[,2], na.rm = TRUE) - obs_outdegree - std_error_outdegree <- sd(boot_result$t[,2], na.rm = TRUE) - - # Confidence intervals - indegree_ci <- tryCatch({ - ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 1) - ci$percent[4:5] - }, error = function(e) c(NA, NA)) - - outdegree_ci <- tryCatch({ - ci <- boot::boot.ci(boot_result, conf = conf.level, type = "perc", index = 2) - ci$percent[4:5] - }, error = function(e) c(NA, NA)) + # In-degree + if (!is.na(indeg_corr)) { + # In-degree bootstrap + safe_bootstrap_in <- function(data, indices) { + d <- data[indices, , drop = FALSE] + suppressWarnings(stats::cor(d$indegree, d$toa, use = "complete.obs")) + } + boot_obj_in <- boot::boot(combined_data, statistic = safe_bootstrap_in, R = R) + bias_in <- mean(boot_obj_in$t, na.rm = TRUE) - indeg_corr + se_in <- stats::sd(boot_obj_in$t, na.rm = TRUE) + + ci_in <- tryCatch({ + bci <- boot::boot.ci(boot_obj_in, conf = conf.level, type = "perc") + if (!is.null(bci$percent)) bci$percent[4:5] else NULL + }, error = function(e) NULL) + + indeg_boot_list <- list( + correlation = indeg_corr, + bias = bias_in, + std_error = se_in, + conf_int = ci_in, + conf_level = conf.level + ) + } else { + indeg_boot_list <- list( + correlation = NA_real_ + ) + boot_obj_in <- NULL + } list( - indegree = list( - correlation = obs_indegree, - bias = bias_indegree, - std_error = std_error_indegree, - conf_int = indegree_ci, - conf_level = conf.level - ), - outdegree = list( - correlation = obs_outdegree, - bias = bias_outdegree, - std_error = std_error_outdegree, - conf_int = outdegree_ci, - conf_level = conf.level - ), - R = R, - boot_object = boot_result + indegree = indeg_boot_list, + outdegree = out_boot_list, + R = R, + boot_object = list(indegree = boot_obj_in, outdegree = boot_obj_out) ) } @@ -500,6 +525,17 @@ print.degree_adoption_diagnostic <- function(x, ...) { # Basic info cat(sprintf("Degree aggregation strategy: %s \n", x$degree_strategy)) + # Combination mode info (only for multi-diffusion) + if (!is.null(x$combine)) { + mode_label <- switch(x$combine, + "none" = "none (per-behavior)", + "pooled" = "pooled (stacked rows)", + "average" = "average (mean TOA per actor)", + "earliest" = "earliest (min TOA per actor)", + x$combine + ) + cat(sprintf("Behavior combination: %s \n\n", mode_label)) + } # Sample size info if (is.null(names(x$sample_size))) { @@ -510,7 +546,6 @@ print.degree_adoption_diagnostic <- function(x, ...) { for (j in seq_along(x$sample_size)) { cat(sprintf(" - %s: %d\n", if (length(beh_names)) beh_names[j] else paste0("B", j), x$sample_size[j])) } - cat("\n") } undirected <- isTRUE(x$undirected) @@ -531,7 +566,7 @@ print_single_behavior_results <- function(x, undirected) { outdeg_r <- x$correlations[["outdegree_toa"]] # Print correlations - cat("\nCorrelations:\n") + cat("Correlations:\n") if (undirected) { deg_r <- indeg_r # For undirected graphs, in-degree = out-degree = degree cat(sprintf(" Degree - Time of Adoption: %.3f\n", deg_r)) @@ -661,7 +696,7 @@ explain_degree_correlation <- function(label, r, ci, lvl_arg = NA_real_, thr = 0 format_interpretation_no_ci <- function(label, r, abs_big, degree_term, thr) { if (!abs_big) { - cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \\u2264 %.1f; no CI.\n", + cat(sprintf(" %s: Weak relationship between %s and adoption timing:\n |r| \u2264 %.1f; no CI.\n", label, degree_term, thr)) } else if (r > 0) { cat(sprintf(" %s: Central actors (high %s) tended to adopt early (supporters):\n |r| > %.1f; no CI.\n", @@ -677,7 +712,7 @@ format_interpretation_with_ci <- function(label, r, ci, abs_big, degree_term, th ci_includes_zero <- (length(ci) >= 2) && is.finite(ci[1]) && is.finite(ci[2]) && (ci[1] <= 0 && ci[2] >= 0) if (!abs_big) { - cat(sprintf(" %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| \\u2264 %.1f; CI (%.1f%%) %s 0.\n", + cat(sprintf(" %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) %s 0.\n", label, degree_term, if (ci_includes_zero) "NOT" else "", thr, lvl_local, From 11102b54637678995c2a554e7c82ff31a7ce7eb2 Mon Sep 17 00:00:00 2001 From: "George G. Vega Yon" Date: Thu, 4 Dec 2025 16:36:52 +0000 Subject: [PATCH 14/18] Updating README and version number --- .devcontainer/{Dockerfile => Containerfile} | 2 +- .devcontainer/devcontainer.json | 6 +- .github/workflows/pkgdown.yml | 4 +- .github/workflows/r.yml | 8 +- .gitignore | 2 + DESCRIPTION | 2 +- Makefile | 10 +- NEWS.md | 5 + README.md | 349 ++++++++---------- README.qmd | 54 ++- README_files/figure-gfm/Adopters-1.png | Bin 44408 -> 0 bytes .../BoringThreshold, plot_threshold-1.png | Bin 175079 -> 0 bytes README_files/figure-gfm/Hazard-1.png | Bin 34730 -> 0 bytes README_files/figure-gfm/NiceThreshold-1.png | Bin 160369 -> 0 bytes README_files/figure-gfm/mosaic-1.png | Bin 24062 -> 0 bytes README_files/figure-gfm/plot_diffnet-1.png | Bin 76334 -> 0 bytes .../figure-gfm/plot_diffnet2 with map-1.png | Bin 58372 -> 0 bytes README_files/figure-gfm/plot_diffnet2-1.png | Bin 114071 -> 0 bytes .../figure-gfm/plot_infectsuscept-1.png | Bin 59104 -> 0 bytes .../figure-gfm/plot_infectsuscept-2.png | Bin 90225 -> 0 bytes .../figure-markdown_github/Adopters-1.png | Bin 33185 -> 0 bytes .../BoringThreshold, plot_threshold-1.png | Bin 214280 -> 0 bytes .../figure-markdown_github/Hazard-1.png | Bin 21984 -> 0 bytes .../NiceThreshold-1.png | Bin 142916 -> 0 bytes .../figure-markdown_github/mosaic-1.png | Bin 14005 -> 0 bytes .../figure-markdown_github/plot_diffnet-1.png | Bin 63669 -> 0 bytes .../plot_diffnet2 with map-1.png | Bin 37396 -> 0 bytes .../plot_diffnet2-1.png | Bin 89819 -> 0 bytes .../plot_infectsuscept-1.png | Bin 28709 -> 0 bytes .../plot_infectsuscept-2.png | Bin 41660 -> 0 bytes man/figures/README-Adopters-1.png | Bin 0 -> 37756 bytes ...ADME-BoringThreshold, plot_threshold-1.png | Bin 0 -> 165895 bytes man/figures/README-Hazard-1.png | Bin 0 -> 27036 bytes man/figures/README-NiceThreshold-1.png | Bin 0 -> 186599 bytes man/figures/README-mosaic-1.png | Bin 0 -> 19514 bytes man/figures/README-plot_diffnet-1.png | Bin 0 -> 68517 bytes .../README-plot_diffnet2 with map-1.png | Bin 0 -> 42564 bytes man/figures/README-plot_diffnet2-1.png | Bin 0 -> 97177 bytes man/figures/README-plot_infectsuscept-1.png | Bin 0 -> 35488 bytes man/figures/README-plot_infectsuscept-2.png | Bin 0 -> 43861 bytes 40 files changed, 201 insertions(+), 241 deletions(-) rename .devcontainer/{Dockerfile => Containerfile} (91%) delete mode 100644 README_files/figure-gfm/Adopters-1.png delete mode 100644 README_files/figure-gfm/BoringThreshold, plot_threshold-1.png delete mode 100644 README_files/figure-gfm/Hazard-1.png delete mode 100644 README_files/figure-gfm/NiceThreshold-1.png delete mode 100644 README_files/figure-gfm/mosaic-1.png delete mode 100644 README_files/figure-gfm/plot_diffnet-1.png delete mode 100644 README_files/figure-gfm/plot_diffnet2 with map-1.png delete mode 100644 README_files/figure-gfm/plot_diffnet2-1.png delete mode 100644 README_files/figure-gfm/plot_infectsuscept-1.png delete mode 100644 README_files/figure-gfm/plot_infectsuscept-2.png delete mode 100644 README_files/figure-markdown_github/Adopters-1.png delete mode 100644 README_files/figure-markdown_github/BoringThreshold, plot_threshold-1.png delete mode 100644 README_files/figure-markdown_github/Hazard-1.png delete mode 100644 README_files/figure-markdown_github/NiceThreshold-1.png delete mode 100644 README_files/figure-markdown_github/mosaic-1.png delete mode 100644 README_files/figure-markdown_github/plot_diffnet-1.png delete mode 100644 README_files/figure-markdown_github/plot_diffnet2 with map-1.png delete mode 100644 README_files/figure-markdown_github/plot_diffnet2-1.png delete mode 100644 README_files/figure-markdown_github/plot_infectsuscept-1.png delete mode 100644 README_files/figure-markdown_github/plot_infectsuscept-2.png create mode 100644 man/figures/README-Adopters-1.png create mode 100644 man/figures/README-BoringThreshold, plot_threshold-1.png create mode 100644 man/figures/README-Hazard-1.png create mode 100644 man/figures/README-NiceThreshold-1.png create mode 100644 man/figures/README-mosaic-1.png create mode 100644 man/figures/README-plot_diffnet-1.png create mode 100644 man/figures/README-plot_diffnet2 with map-1.png create mode 100644 man/figures/README-plot_diffnet2-1.png create mode 100644 man/figures/README-plot_infectsuscept-1.png create mode 100644 man/figures/README-plot_infectsuscept-2.png diff --git a/.devcontainer/Dockerfile b/.devcontainer/Containerfile similarity index 91% rename from .devcontainer/Dockerfile rename to .devcontainer/Containerfile index 77347cb..f9b3746 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Containerfile @@ -1,4 +1,4 @@ -FROM rocker/tidyverse:4.5.0 +FROM ghcr.io/rocker-org/devcontainer/tidyverse:4.5 # RUN \ # echo 'options(repos=c(CRAN="https://packagemanager.posit.co/cran/__linux__/bookworm/latest"))' >> ~/.Rprofile && \ diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 4ec8930..d07306e 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -3,7 +3,7 @@ { "name": "netdiffuseR", "build": { - "dockerfile": "Dockerfile" + "dockerfile": "Containerfile" }, "customizations": { "vscode": { @@ -19,7 +19,7 @@ "mounts": [ // Mount the .vscode configuration into the container "source=${localWorkspaceFolder}/.devcontainer/.vscode,target=/workspaces/${localWorkspaceFolderBasename}/.vscode,type=bind,consistency=cached" - ] + ], // Features to add to the dev container. More info: https://containers.dev/features. // "features": {}, @@ -33,5 +33,5 @@ // "customizations": {}, // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root. - // "remoteUser": "root" + "remoteUser": "root" } diff --git a/.github/workflows/pkgdown.yml b/.github/workflows/pkgdown.yml index 23bad37..53ba6a5 100644 --- a/.github/workflows/pkgdown.yml +++ b/.github/workflows/pkgdown.yml @@ -24,7 +24,7 @@ jobs: id-token: write pages: write steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 - uses: r-lib/actions/setup-pandoc@v2 with: @@ -35,6 +35,8 @@ jobs: - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: any::pkgdown + install-quarto: true + install-pandoc: true - name: Install the package run: R CMD INSTALL . diff --git a/.github/workflows/r.yml b/.github/workflows/r.yml index ccc947c..b6594ba 100644 --- a/.github/workflows/r.yml +++ b/.github/workflows/r.yml @@ -22,7 +22,9 @@ jobs: matrix: config: - {os: macOS-latest, r: 'release'} + - {os: macOS-latest, r: 'devel', http-user-agent: 'release'} - {os: windows-latest, r: 'release'} + - {os: windows-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - {os: ubuntu-latest, r: 'devel', valgrind: true, http-user-agent: 'release'} @@ -32,9 +34,7 @@ jobs: R_KEEP_PKG_SOURCE: yes steps: - - uses: actions/checkout@v4 - - - uses: r-lib/actions/setup-pandoc@v2 + - uses: actions/checkout@v5 - uses: r-lib/actions/setup-r@v2 with: @@ -46,6 +46,8 @@ jobs: with: extra-packages: any::rcmdcheck needs: check + install-quarto: true + install-pandoc: true - uses: r-lib/actions/check-r-package@v2 if: ${{ matrix.config.valgrind != true }} diff --git a/.gitignore b/.gitignore index 8a137da..3aba50b 100755 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,5 @@ inst/doc *.tex playground/ doc/ + +src/Makevars \ No newline at end of file diff --git a/DESCRIPTION b/DESCRIPTION index 041f783..2c7e957 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: netdiffuseR Title: Analysis of Diffusion and Contagion Processes on Networks -Version: 1.23.0 +Version: 1.24.0 Authors@R: c( person("George", "Vega Yon", email="g.vegayon@gmail.com", role=c("aut", "cre"), comment=c(ORCID = "0000-0002-3171-0844", what="Rewrite functions with Rcpp, plus new features") diff --git a/Makefile b/Makefile index 832637c..03fa9d2 100644 --- a/Makefile +++ b/Makefile @@ -9,16 +9,16 @@ help: @echo " make docs - Generate documentation" install: - R CMD INSTALL . + Rscript --vanilla -e 'devtools::install()' build: R CMD build . -README.md: README.Rmd - Rscript -e 'rmarkdown::render("README.Rmd")' +README.md: README.qmd + quarto render README.qmd -check: netdiffuseR_$(VERSION).tar.gz - R CMD check --as-cran netdiffuseR_$(VERSION).tar.gz +check: + Rscript --vanilla -e 'devtools::check()' checkv: netdiffuseR_$(VERSION).tar.gz R CMD check --as-cran --use-valgrind netdiffuseR_$(VERSION).tar.gz diff --git a/NEWS.md b/NEWS.md index 36c59d1..f822556 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,8 @@ +# Changes in netdiffuseR version 1.24.0 (DEV) + + + + # Changes in netdiffuseR version 1.23.0 (2025-06-10) * New methods for simulating multi-diffusion models. diff --git a/README.md b/README.md index f9a223c..96e7ee4 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,11 @@ [![R-CMD-check](https://github.com/USCCANA/netdiffuseR/actions/workflows/r.yml/badge.svg)](https://github.com/USCCANA/netdiffuseR/actions/workflows/r.yml) [![codecov.io](https://codecov.io/github/USCCANA/netdiffuseR/coverage.svg?branch=master)](https://app.codecov.io/github/USCCANA/netdiffuseR?branch=master) [![](https://cranlogs.r-pkg.org/badges/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) -[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) +[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) [![](https://cranlogs.r-pkg.org/badges/grand-total/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1039317.svg)](https://doi.org/10.5281/zenodo.1039317) [![Dependencies](https://tinyverse.netlify.app/badge/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) -[![USC’s Department of Preventive -Medicine](https://raw.githubusercontent.com/USCbiostats/badges/master/tommy-uscprevmed-badge.svg)](https://keck.usc.edu/cana/) +[![USC's Department of Preventive Medicine](https://raw.githubusercontent.com/USCbiostats/badges/master/tommy-uscprevmed-badge.svg)](https://keck.usc.edu/cana/)

@@ -42,24 +41,23 @@ Health. ``` r citation(package="netdiffuseR") -``` - - To cite netdiffuseR in publications use the following paper: +To cite netdiffuseR in publications use the following paper: - Valente TW, Vega Yon GG. Diffusion/Contagion Processes on Social - Networks. Health Education & Behavior. 2020;47(2):235-248. - doi:10.1177/1090198120901497 + Valente TW, Vega Yon GG. Diffusion/Contagion Processes on Social + Networks. Health Education & Behavior. 2020;47(2):235-248. + doi:10.1177/1090198120901497 - And the actual R package: +And the actual R package: - Vega Yon G, Olivera Morales A, Valente T (2025). _netdiffuseR: - Analysis of Diffusion and Contagion Processes on Networks_. - doi:10.5281/zenodo.1039317 , - R package version 1.23.0, . + Vega Yon G, Olivera Morales A, Valente T (2025). _netdiffuseR: + Analysis of Diffusion and Contagion Processes on Networks_. + doi:10.5281/zenodo.1039317 , + R package version 1.24.0, . - To see these entries in BibTeX format, use 'print(, - bibtex=TRUE)', 'toBibtex(.)', or set - 'options(citation.bibtex.max=999)'. +To see these entries in BibTeX format, use 'print(, +bibtex=TRUE)', 'toBibtex(.)', or set +'options(citation.bibtex.max=999)'. +``` ## News @@ -85,42 +83,19 @@ remotes::install_github('USCCANA/netdiffuseR', build_vignettes = TRUE) ``` You can skip building vignettes by setting `build_vignettes = FALSE` (so -it is not required). +it is not required). You can also get the package (pre-built binaries) +from our R-Universe repository (recommended): -For the case of OSX users, there seems to be a problem when installing -packages depending on `Rcpp`. This issue, developed -[here](https://github.com/USCCANA/netdiffuseR/issues/3), can be solved -by open the terminal and typing the following - -``` sh -curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2 -sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C / +``` r +install.packages( + 'epiworldR', + repos = c( + 'https://uofuepibio.r-universe.dev', + 'https://cloud.r-project.org' + ) +) ``` -before installing the package through `devtools`. - -### Binary versions - -For the case of windows and mac users, they can find binary versions of -the package [here](https://github.com/USCCANA/netdiffuseR/releases), -netdiffuseR_1…zip, and netdiffuseR_1…tgz respectively. They can install -this directly as follows (using the 1.16.3.29 version): - -1. Install dependencies from CRAN - `r > install.packages(c("igraph", "Matrix", "SparseM", "RcppArmadillo", "sna"), dependencies=TRUE)` - -2. Download the binary version and install it as follows: - - ``` r - > install.packages("netdiffuseR_1.16.3.29.zip", repos=NULL) - ``` - - For windows users, and for Mac users: - - ``` r - > install.packages("netdiffuseR_1.16.3.29.tgz", repos=NULL) - ``` - ## Tutorials Since starting netdiffuseR, we have done a couple of workshops at @@ -141,19 +116,16 @@ This example has been taken from the package’s vignettes: ``` r library(netdiffuseR) +#> Thank you for using netdiffuseR! Please consider citing it in your work. +#> You can find the citation information by running +#> citation("netdiffuseR") +#> +#> Attaching package: 'netdiffuseR' +#> The following object is masked from 'package:base': +#> +#> %*% ``` - Thank you for using netdiffuseR! Please consider citing it in your work. - You can find the citation information by running - citation("netdiffuseR") - - - Attaching package: 'netdiffuseR' - - The following object is masked from 'package:base': - - %*% - ### Infectiousness and Susceptibility ``` r @@ -164,81 +136,72 @@ nper <- 20 graph <- rgraph_er(n, nper, .5) toa <- sample(c(1:(1+nper-1), NA), n, TRUE) head(toa) -``` - - [1] 16 3 14 3 13 5 +#> [1] 16 3 14 3 13 5 -``` r # Creating a diffnet object diffnet <- as_diffnet(graph, toa) diffnet -``` - - Dynamic network of class -diffnet- - Name : Diffusion Network - Behavior : Unknown - # of nodes : 100 (1, 2, 3, 4, 5, 6, 7, 8, ...) - # of time periods : 20 (1 - 20) - Type : directed - Num of behaviors : 1 - Final prevalence : 0.95 - Static attributes : - - Dynamic attributes : - - -``` r +#> Dynamic network of class -diffnet- +#> Name : Diffusion Network +#> Behavior : Unknown +#> # of nodes : 100 (1, 2, 3, 4, 5, 6, 7, 8, ...) +#> # of time periods : 20 (1 - 20) +#> Type : directed +#> Num of behaviors : 1 +#> Final prevalence : 0.95 +#> Static attributes : - +#> Dynamic attributes : - summary(diffnet) -``` +#> Diffusion network summary statistics +#> Name : Diffusion Network +#> Behavior : Unknown +#> ----------------------------------------------------------------------------- +#> Period Adopters Cum Adopt. (%) Hazard Rate Density Moran's I (sd) +#> -------- ---------- ---------------- ------------- --------- ---------------- +#> 1 8 8 (0.08) - 0.50 -0.01 (0.00) +#> 2 3 11 (0.11) 0.03 0.50 -0.01 (0.00) +#> 3 6 17 (0.17) 0.07 0.51 -0.01 (0.00) +#> 4 3 20 (0.20) 0.04 0.49 -0.01 (0.00) +#> 5 9 29 (0.29) 0.11 0.50 -0.01 (0.00) +#> 6 5 34 (0.34) 0.07 0.50 -0.01 (0.00) +#> 7 2 36 (0.36) 0.03 0.51 -0.01 (0.00) +#> 8 3 39 (0.39) 0.05 0.50 -0.01 (0.00) +#> 9 5 44 (0.44) 0.08 0.50 -0.01 (0.00) +#> 10 1 45 (0.45) 0.02 0.49 -0.01 (0.00) +#> 11 3 48 (0.48) 0.05 0.50 -0.01 (0.00) +#> 12 6 54 (0.54) 0.12 0.50 -0.01 (0.00) +#> 13 8 62 (0.62) 0.17 0.50 -0.01 (0.00) +#> 14 9 71 (0.71) 0.24 0.50 -0.01 (0.00) +#> 15 5 76 (0.76) 0.17 0.50 -0.00 (0.00) ** +#> 16 7 83 (0.83) 0.29 0.50 -0.01 (0.00) +#> 17 5 88 (0.88) 0.29 0.49 -0.00 (0.00) *** +#> 18 4 92 (0.92) 0.33 0.50 -0.01 (0.00) +#> 19 1 93 (0.93) 0.12 0.50 -0.01 (0.00) +#> 20 2 95 (0.95) 0.29 0.50 -0.01 (0.00) +#> ----------------------------------------------------------------------------- +#> Left censoring : 0.08 (8) +#> Right centoring : 0.05 (5) +#> # of nodes : 100 +#> +#> Moran's I was computed on contemporaneous autocorrelation using 1/geodesic +#> values. Significane levels *** <= .01, ** <= .05, * <= .1. - Diffusion network summary statistics - Name : Diffusion Network - Behavior : Unknown - ----------------------------------------------------------------------------- - Period Adopters Cum Adopt. (%) Hazard Rate Density Moran's I (sd) - -------- ---------- ---------------- ------------- --------- ---------------- - 1 8 8 (0.08) - 0.50 -0.01 (0.00) - 2 3 11 (0.11) 0.03 0.50 -0.01 (0.00) - 3 6 17 (0.17) 0.07 0.51 -0.01 (0.00) - 4 3 20 (0.20) 0.04 0.49 -0.01 (0.00) - 5 9 29 (0.29) 0.11 0.50 -0.01 (0.00) - 6 5 34 (0.34) 0.07 0.50 -0.01 (0.00) - 7 2 36 (0.36) 0.03 0.51 -0.01 (0.00) - 8 3 39 (0.39) 0.05 0.50 -0.01 (0.00) - 9 5 44 (0.44) 0.08 0.50 -0.01 (0.00) - 10 1 45 (0.45) 0.02 0.49 -0.01 (0.00) - 11 3 48 (0.48) 0.05 0.50 -0.01 (0.00) - 12 6 54 (0.54) 0.12 0.50 -0.01 (0.00) - 13 8 62 (0.62) 0.17 0.50 -0.01 (0.00) - 14 9 71 (0.71) 0.24 0.50 -0.01 (0.00) - 15 5 76 (0.76) 0.17 0.50 -0.00 (0.00) ** - 16 7 83 (0.83) 0.29 0.50 -0.01 (0.00) - 17 5 88 (0.88) 0.29 0.49 -0.00 (0.00) *** - 18 4 92 (0.92) 0.33 0.50 -0.01 (0.00) - 19 1 93 (0.93) 0.12 0.50 -0.01 (0.00) - 20 2 95 (0.95) 0.29 0.50 -0.01 (0.00) - ----------------------------------------------------------------------------- - Left censoring : 0.08 (8) - Right centoring : 0.05 (5) - # of nodes : 100 - - Moran's I was computed on contemporaneous autocorrelation using 1/geodesic - values. Significane levels *** <= .01, ** <= .05, * <= .1. - -``` r # Visualizing distribution of suscep/infect out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = FALSE, h=.01) ``` -![](README_files/figure-commonmark/plot_infectsuscept-1.png) + ``` r out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = TRUE, exclude.zeros = TRUE, h=1) +#> Warning in plot_infectsuscep.list(graph$graph, graph$toa, t0, normalize, : When +#> applying logscale some observations are missing. ``` - Warning in plot_infectsuscep.list(graph$graph, graph$toa, t0, normalize, : When - applying logscale some observations are missing. - -![](README_files/figure-commonmark/plot_infectsuscept-2.png) + ### Threshold @@ -250,25 +213,23 @@ diffnet <- rdiffnet(500, 20, rgraph.args = list(m=3), threshold.dist = function(x) runif(1, .3, .7)) diffnet -``` - - Dynamic network of class -diffnet- - Name : A diffusion network - Behavior : Random contagion - # of nodes : 500 (1, 2, 3, 4, 5, 6, 7, 8, ...) - # of time periods : 20 (1 - 20) - Type : directed - Num of behaviors : 1 - Final prevalence : 1.00 - Static attributes : real_threshold (1) - Dynamic attributes : - +#> Dynamic network of class -diffnet- +#> Name : A diffusion network +#> Behavior : Random contagion +#> # of nodes : 500 (1, 2, 3, 4, 5, 6, 7, 8, ...) +#> # of time periods : 20 (1 - 20) +#> Type : directed +#> Num of behaviors : 1 +#> Final prevalence : 1.00 +#> Static attributes : real_threshold (1) +#> Dynamic attributes : - -``` r # Threshold with fixed vertex size plot_threshold(diffnet) ``` -![](README_files/figure-commonmark/BoringThreshold,%20plot_threshold-1.png) + Using more features @@ -282,12 +243,12 @@ plot_threshold( sub = "Note: Vertices' sizes and shapes given by degree and city respectively", jitter.factor = c(1,1), jitter.amount = c(.25,.025) ) +#> Warning in (function (graph, expo, toa, include_censored = FALSE, t0 = min(toa, +#> : -vertex.sides- will be coerced to integer. ``` - Warning in (function (graph, expo, toa, include_censored = FALSE, t0 = min(toa, - : -vertex.sides- will be coerced to integer. - -![](README_files/figure-commonmark/NiceThreshold-1.png) + ### Adoption rate @@ -295,7 +256,7 @@ plot_threshold( plot_adopters(diffnet) ``` -![](README_files/figure-commonmark/Adopters-1.png) + ### Hazard rate @@ -303,7 +264,7 @@ plot_adopters(diffnet) hazard_rate(diffnet) ``` -![](README_files/figure-commonmark/Hazard-1.png) + ### Diffusion process @@ -311,14 +272,15 @@ hazard_rate(diffnet) plot_diffnet(medInnovationsDiffNet, slices=c(1,9,8)) ``` -![](README_files/figure-commonmark/plot_diffnet-1.png) + ``` r diffnet.toa(brfarmersDiffNet)[brfarmersDiffNet$toa >= 1965] <- NA plot_diffnet2(brfarmersDiffNet, vertex.size = "indegree") ``` -![](README_files/figure-commonmark/plot_diffnet2-1.png) + ``` r set.seed(1231) @@ -348,7 +310,8 @@ mtext("Both networks have the same distribution on times of adoption", 1, outer = TRUE) ``` -![](README_files/figure-commonmark/plot_diffnet2%20with%20map-1.png) + ``` r par(oldpar) @@ -359,17 +322,14 @@ par(oldpar) ``` r out <- classify(kfamilyDiffNet, include_censored = TRUE) ftable(out) -``` - - thr Non-Adopters Very Low Thresh. Low Thresh. High Thresh. Very High Thresh. - toa - Non-Adopters 0.00 0.00 0.00 0.00 0.00 - Early Adopters 0.00 14.04 8.40 0.57 0.29 - Early Majority 0.00 5.64 11.65 5.54 2.58 - Late Majority 0.00 1.34 5.06 6.21 2.96 - Laggards 0.00 1.53 0.00 0.00 34.19 +#> thr Non-Adopters Very Low Thresh. Low Thresh. High Thresh. Very High Thresh. +#> toa +#> Non-Adopters 0.00 0.00 0.00 0.00 0.00 +#> Early Adopters 0.00 14.04 8.40 0.57 0.29 +#> Early Majority 0.00 5.64 11.65 5.54 2.58 +#> Late Majority 0.00 1.34 5.06 6.21 2.96 +#> Laggards 0.00 1.53 0.00 0.00 34.19 -``` r # Plotting oldpar <- par(no.readonly = TRUE) par(xpd=TRUE) @@ -381,7 +341,7 @@ legend("bottom", legend = levels(out$thr), fill=viridisLite::inferno(5), horiz = cex=.6, bty="n", inset=c(0,-.1)) ``` -![](README_files/figure-commonmark/mosaic-1.png) + ``` r par(oldpar) @@ -391,49 +351,48 @@ par(oldpar) ``` r sessionInfo() +#> R version 4.5.2 (2025-10-31) +#> Platform: x86_64-pc-linux-gnu +#> Running under: Ubuntu 24.04.3 LTS +#> +#> Matrix products: default +#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 +#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0 +#> +#> locale: +#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C +#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 +#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 +#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C +#> [9] LC_ADDRESS=C LC_TELEPHONE=C +#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C +#> +#> time zone: Etc/UTC +#> tzcode source: system (glibc) +#> +#> attached base packages: +#> [1] stats graphics grDevices utils datasets methods base +#> +#> other attached packages: +#> [1] netdiffuseR_1.24.0 +#> +#> loaded via a namespace (and not attached): +#> [1] Matrix_1.7-4 jsonlite_2.0.0 dplyr_1.1.4 +#> [4] compiler_4.5.2 tidyselect_1.2.1 Rcpp_1.1.0 +#> [7] networkLite_1.1.0 boot_1.3-32 yaml_2.3.10 +#> [10] fastmap_1.2.0 lattice_0.22-7 coda_0.19-4.1 +#> [13] R6_2.6.1 generics_0.1.4 MatchIt_4.7.2 +#> [16] igraph_2.2.1 knitr_1.50 MASS_7.3-65 +#> [19] backports_1.5.0 tibble_3.3.0 statnet.common_4.12.0 +#> [22] pillar_1.11.1 rlang_1.1.6 xfun_0.53 +#> [25] viridisLite_0.4.2 cli_3.6.5 magrittr_2.0.4 +#> [28] network_1.19.0 digest_0.6.37 grid_4.5.2 +#> [31] lifecycle_1.0.4 vctrs_0.6.5 sna_2.8 +#> [34] evaluate_1.0.5 SparseM_1.84-2 glue_1.8.0 +#> [37] rmarkdown_2.30 tools_4.5.2 pkgconfig_2.0.3 +#> [40] networkDynamic_0.11.5 htmltools_0.5.8.1 ``` - R version 4.5.0 (2025-04-11) - Platform: x86_64-pc-linux-gnu - Running under: Ubuntu 24.04.2 LTS - - Matrix products: default - BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 - LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0 - - locale: - [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C - [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 - [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 - [7] LC_PAPER=en_US.UTF-8 LC_NAME=C - [9] LC_ADDRESS=C LC_TELEPHONE=C - [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C - - time zone: Etc/UTC - tzcode source: system (glibc) - - attached base packages: - [1] stats graphics grDevices utils datasets methods base - - other attached packages: - [1] netdiffuseR_1.23.0 - - loaded via a namespace (and not attached): - [1] Matrix_1.7-3 jsonlite_2.0.0 dplyr_1.1.4 - [4] compiler_4.5.0 tidyselect_1.2.1 Rcpp_1.0.14 - [7] networkLite_1.1.0 boot_1.3-31 yaml_2.3.10 - [10] fastmap_1.2.0 lattice_0.22-6 coda_0.19-4.1 - [13] R6_2.6.1 generics_0.1.4 MatchIt_4.7.2 - [16] igraph_2.1.4 knitr_1.50 MASS_7.3-65 - [19] backports_1.5.0 tibble_3.3.0 statnet.common_4.12.0 - [22] pillar_1.10.2 rlang_1.1.6 xfun_0.52 - [25] viridisLite_0.4.2 cli_3.6.5 magrittr_2.0.3 - [28] network_1.19.0 digest_0.6.37 grid_4.5.0 - [31] lifecycle_1.0.4 vctrs_0.6.5 sna_2.8 - [34] evaluate_1.0.3 SparseM_1.84-2 glue_1.8.0 - [37] rmarkdown_2.29 tools_4.5.0 pkgconfig_2.0.3 - [40] networkDynamic_0.11.5 htmltools_0.5.8.1 - ## To-do list - Import/Export functions for interfacing other package’s clases, in diff --git a/README.qmd b/README.qmd index f279f82..b4f0181 100644 --- a/README.qmd +++ b/README.qmd @@ -2,6 +2,18 @@ format: gfm --- +```{r} +#| label: setup +#| echo: false +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + fig.path = "man/figures/README-", + out.width = "100%" +) +``` + +```{=markdown} [![R-CMD-check](https://github.com/USCCANA/netdiffuseR/actions/workflows/r.yml/badge.svg)](https://github.com/USCCANA/netdiffuseR/actions/workflows/r.yml) [![codecov.io](https://codecov.io/github/USCCANA/netdiffuseR/coverage.svg?branch=master)](https://app.codecov.io/github/USCCANA/netdiffuseR?branch=master) [![](https://cranlogs.r-pkg.org/badges/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) @@ -10,6 +22,7 @@ format: gfm [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1039317.svg)](https://doi.org/10.5281/zenodo.1039317) [![Dependencies](https://tinyverse.netlify.app/badge/netdiffuseR)](https://cran.r-project.org/package=netdiffuseR) [![USC's Department of Preventive Medicine](https://raw.githubusercontent.com/USCbiostats/badges/master/tommy-uscprevmed-badge.svg)](https://keck.usc.edu/cana/) +```

netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

@@ -59,41 +72,18 @@ If you want the latest (unstable) version of __netdiffuseR__, using the `remotes remotes::install_github('USCCANA/netdiffuseR', build_vignettes = TRUE) ``` -You can skip building vignettes by setting `build_vignettes = FALSE` (so it is not required). +You can skip building vignettes by setting `build_vignettes = FALSE` (so it is not required). You can also get the package (pre-built binaries) from our R-Universe repository (recommended): -For the case of OSX users, there seems to be a problem when installing packages -depending on `Rcpp`. This issue, developed [here](https://github.com/USCCANA/netdiffuseR/issues/3), -can be solved by open the terminal and typing the following - -```sh -curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2 -sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C / +```r +install.packages( + 'epiworldR', + repos = c( + 'https://uofuepibio.r-universe.dev', + 'https://cloud.r-project.org' + ) +) ``` -before installing the package through `devtools`. - -### Binary versions - -For the case of windows and mac users, they can find binary versions of the package [here](https://github.com/USCCANA/netdiffuseR/releases), netdiffuseR_1...zip, and netdiffuseR_1...tgz respectively. They can install this directly as follows (using the 1.16.3.29 version): - - -1. Install dependencies from CRAN - ``` r - > install.packages(c("igraph", "Matrix", "SparseM", "RcppArmadillo", "sna"), dependencies=TRUE) - ``` - -2. Download the binary version and install it as follows: - - ``` r - > install.packages("netdiffuseR_1.16.3.29.zip", repos=NULL) - ``` - - For windows users, and for Mac users: - - ``` r - > install.packages("netdiffuseR_1.16.3.29.tgz", repos=NULL) - ``` - ## Tutorials Since starting netdiffuseR, we have done a couple of workshops at Sunbelt and NASN. 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