From 6f1b036f43359b824b6fa197a378a576a8ab5e99 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Wed, 10 Dec 2025 16:54:31 -0300 Subject: [PATCH 01/18] adding stochastic transmission mode to expure and rdiffnet --- R/rdiffnet.r | 9 +++++++++ R/stats.R | 53 ++++++++++++++++++++++++++++++++++++++-------------- 2 files changed, 48 insertions(+), 14 deletions(-) diff --git a/R/rdiffnet.r b/R/rdiffnet.r index 122938f..7849900 100644 --- a/R/rdiffnet.r +++ b/R/rdiffnet.r @@ -399,6 +399,7 @@ rdiffnet <- function( rewire.args = list(), threshold.dist = runif(n), exposure.args = list(), + exposure.mode = "deterministic", name = "A diffusion network", behavior = "Random contagion", stop.no.diff = TRUE, @@ -414,6 +415,14 @@ rdiffnet <- function( if (!length(exposure.args[[arg]])) exposure.args[[arg]] <- default_exposure.args[[arg]] + exposure.args$mode <- exposure.mode + + # If stochastic mode is selected, ensure valued is TRUE + if (exposure.mode == "stochastic" && !exposure.args$valued) { + warning("exposure.mode='stochastic' requires valued=TRUE to use weights as probabilities. Setting exposure.args$valued=TRUE.") + exposure.args$valued <- TRUE + } + if (inherits(exposure.args[["attrs"]], "matrix")) { # Checking if the attrs matrix is has dims n x t if (any(dim(exposure.args[["attrs"]]) != dim(matrix(NA, nrow = n, ncol = t)))) { diff --git a/R/stats.R b/R/stats.R index dadcac6..8e859b5 100644 --- a/R/stats.R +++ b/R/stats.R @@ -495,7 +495,7 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { NULL # Workhorse of exposure plotting -.exposure <- function(graph, cumadopt, attrs, outgoing, valued, normalized, self) { +.exposure <- function(graph, cumadopt, attrs, outgoing, valued, normalized, self, mode = "deterministic") { # Getting the parameters n <- nrow(graph) @@ -513,23 +513,41 @@ NULL # Checking self if (!self) graph <- sp_diag(graph, rep(0, nnodes(graph))) - norm <- graph %*% attrs + 1e-20 + # 1. Calculate Normalization (Denominator) using ORIGINAL weights + # This represents the potential exposure (total degree/strength) + norm <- as.vector(graph %*% attrs) + 1e-20 + + # 2. Apply Stochastic Filter (if enabled) + # This modifies the graph to represent only Realized Transmissions for the numerator + if (mode == "stochastic") { + # Generate random values for every edge + u <- stats::runif(length(graph@x)) + + # Keep edge (set to 1) if U < Weight, else remove (set to 0) + # This treats the original weights as Probabilities + graph@x <- as.numeric(u < graph@x) + } if (!is.na(dim(cumadopt)[3])) { ans <- array(0, dim = c(dim(cumadopt)[1],dim(cumadopt)[3])) for (q in 1:dim(cumadopt)[3]) { + # Calculate numerator: Realized connections * Adoption status + numerator <- as.vector(graph %*% (attrs * cumadopt[,,q])) + if (normalized) { - ans[,q] <- as.vector(graph %*% (attrs * cumadopt[,,q]) / norm) + ans[,q] <- numerator / norm } else { - ans[,q] <- as.vector(graph %*% (attrs * cumadopt[,,q])) + ans[,q] <- numerator } } } else { - ans <- graph %*% (attrs * cumadopt) + numerator <- as.vector(graph %*% (attrs * cumadopt)) if (normalized) { - ans <- ans/ norm + ans <- numerator / norm + } else { + ans <- numerator } } @@ -559,6 +577,7 @@ check_lags <- function(npers, lags) { #' @export #' @rdname exposure +#' @param mode Character scalar. Either "deterministic" (default) or "stochastic". exposure <- function( graph, cumadopt, @@ -570,6 +589,7 @@ exposure <- function( groupvar = NULL, self = getOption("diffnet.self"), lags = 0L, + mode = "deterministic", ... ) { @@ -653,7 +673,7 @@ exposure <- function( if ((is.array(graph) & !inherits(graph, "matrix")) | is.list(graph)) { exposure.list(as_spmat(graph), cumadopt, attrs, outgoing, valued, normalized, - self, lags) + self, lags, mode = mode) } else stopifnot_graph(graph) } @@ -661,7 +681,7 @@ exposure <- function( # @export exposure.list <- function( graph, cumadopt, attrs, - outgoing, valued, normalized, self, lags) { + outgoing, valued, normalized, self, lags, mode = "deterministic") { # attrs can be either # degree, indegree, outdegree, or a user defined vector. @@ -681,7 +701,7 @@ exposure.list <- function( add_dimnames.mat(attrs) output <- exposure_for(graph, cumadopt, attrs, outgoing, valued, normalized, - self, lags) + self, lags, mode = mode) dimnames(output) <- dimnames(cumadopt) output @@ -696,7 +716,8 @@ exposure_for <- function( valued, normalized, self, - lags + lags, + mode = "deterministic" ) { if (!is.na(dim(cumadopt)[3])) { @@ -710,7 +731,8 @@ exposure_for <- function( outgoing = outgoing, valued = valued, normalized = normalized, - self = self) + self = self, + mode = mode) } } else { for (i in (1 - lags):nslices(graph)) { @@ -720,7 +742,8 @@ exposure_for <- function( outgoing = outgoing, valued = valued, normalized = normalized, - self = self) + self = self, + mode = mode) } } } else { @@ -734,7 +757,8 @@ exposure_for <- function( outgoing = outgoing, valued = valued, normalized = normalized, - self = self) + self = self, + mode = mode) } } else { for (i in (1 - lags):nslices(graph)) { @@ -744,7 +768,8 @@ exposure_for <- function( outgoing = outgoing, valued = valued, normalized = normalized, - self = self) + self = self, + mode = mode) } } } From 3e2f1f34669cad0f376115a3f9a37a3786aa41c5 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Wed, 10 Dec 2025 20:01:53 -0300 Subject: [PATCH 02/18] Fix stochastic exposure normalization (E<=1), improve docs, bump version to 1.25.0, and fix exposure rownames issue --- DESCRIPTION | 2 +- NEWS.md | 4 ++ R/rdiffnet.r | 9 ++- R/stats.R | 85 +++++++++++++++++++---- man/exposure.Rd | 24 +++++++ man/rdiffnet.Rd | 7 ++ tests/testthat/test-stochastic-exposure.R | 62 +++++++++++++++++ 7 files changed, 176 insertions(+), 17 deletions(-) create mode 100644 tests/testthat/test-stochastic-exposure.R diff --git a/DESCRIPTION b/DESCRIPTION index 2c7e957..bb31860 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: netdiffuseR Title: Analysis of Diffusion and Contagion Processes on Networks -Version: 1.24.0 +Version: 1.25.0 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/NEWS.md b/NEWS.md index c3f6625..80bc0c0 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# Changes in netdiffuseR version 1.25.0 (2025-12-10) + +* `exposure()` and `rdiffnet()` now support `mode = "stochastic"`, allowing for probabilistic interpretation of edge weights in exposure calculations. + # Changes in netdiffuseR version 1.24.0 (2025-12-09) * New function `degree_adoption_diagnostic()` analyzes the correlation between network diff --git a/R/rdiffnet.r b/R/rdiffnet.r index 7849900..f15407a 100644 --- a/R/rdiffnet.r +++ b/R/rdiffnet.r @@ -22,6 +22,7 @@ #' it can also be an \eqn{n \times Q} matrix or a list of \eqn{Q} single behavior inputs. Sets the adoption #' threshold for each node. #' @param exposure.args List. Arguments to be passed to \code{\link{exposure}}. +#' @param exposure.mode Character scalar. Either "deterministic" (default) or "stochastic". #' @param name Character scalar. Passed to \code{\link{as_diffnet}}. #' @param behavior Character scalar or a list or character scalar (multiple behaviors only). Passed to \code{\link{as_diffnet}}. #' @param stop.no.diff Logical scalar. When \code{TRUE}, the function will return @@ -101,6 +102,10 @@ #' \code{normalized} \tab \code{TRUE} #' } #' +#' When \code{exposure.mode = "stochastic"}, the \code{valued} argument in +#' \code{exposure.args} is forced to \code{TRUE} (with a message) to ensure that +#' edge weights are treated as probabilities. +#' #' @examples #' # (Single behavior): -------------------------------------------------------- #' @@ -417,9 +422,9 @@ rdiffnet <- function( exposure.args$mode <- exposure.mode - # If stochastic mode is selected, ensure valued is TRUE + # If stochastic mode is selected, ensure valued is TRUE (enabling weights as probabilities) if (exposure.mode == "stochastic" && !exposure.args$valued) { - warning("exposure.mode='stochastic' requires valued=TRUE to use weights as probabilities. Setting exposure.args$valued=TRUE.") + message("exposure.mode='stochastic' requires valued=TRUE to use weights as probabilities. Setting exposure.args$valued=TRUE.") exposure.args$valued <- TRUE } diff --git a/R/stats.R b/R/stats.R index 8e859b5..880e95f 100644 --- a/R/stats.R +++ b/R/stats.R @@ -275,6 +275,7 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' @param groupvar Passed to \code{\link{struct_equiv}}. #' @param lags Integer scalar. When different from 0, the resulting exposure #' matrix will be the lagged exposure as specified (see examples). +#' @param mode Character scalar. Either "deterministic" (default) or "stochastic". #' @details #' Exposure is calculated as follows: #' @@ -330,6 +331,27 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' computed as a count instead of a proportion. A good example of this can be #' found at the examples section of the function \code{\link{rdiffnet}}. #' +#' \strong{Stochastic Exposure} +#' +#' When \code{mode = "stochastic"}, the exposure is calculated based on a probabilistic +#' interpretation of the edges. In this mode, the weights of the graph \eqn{S_t} are +#' treated as probabilities of transmission. For each edge \eqn{(i,j)}, a Bernoulli +#' trial is performed with probability \eqn{S_{t,ij}}. If the trial is successful, +#' the edge is "realized" and its weight is used in the numerator. If failed, +#' the edge is treated as non-existent (weight 0) for the numerator. +#' +#' The denominator (normalization factor) is always calculated using the original +#' weights of the graph (sum of probabilities), representing the expected total +#' exposure. This ensures that the exposure value \eqn{E_t} remains bounded between +#' 0 and 1 (if normalized), as the realized weights are a subset of the total weights. +#' +#' \deqn{ +#' \tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) S_{t,ij} a_{tj}}{\sum_{j \neq i} S_{t,ij}} +#' } +#' +#' Where \eqn{S_{t,ij}} is the weight of the edge from \eqn{j} to \eqn{i} at time \eqn{t} +#' (treated as probability), and \eqn{U_{ij} \sim \text{Uniform}(0,1)}. +#' #' @references #' Burt, R. S. (1987). "Social Contagion and Innovation: Cohesion versus Structural #' Equivalence". American Journal of Sociology, 92(6), 1287. @@ -513,22 +535,21 @@ NULL # Checking self if (!self) graph <- sp_diag(graph, rep(0, nnodes(graph))) - # 1. Calculate Normalization (Denominator) using ORIGINAL weights - # This represents the potential exposure (total degree/strength) + # Calculate normalization (or potential exposure) norm <- as.vector(graph %*% attrs) + 1e-20 - # 2. Apply Stochastic Filter (if enabled) - # This modifies the graph to represent only Realized Transmissions for the numerator + # Apply stochastic filter (or selective exposure) if (mode == "stochastic") { # Generate random values for every edge u <- stats::runif(length(graph@x)) - # Keep edge (set to 1) if U < Weight, else remove (set to 0) - # This treats the original weights as Probabilities - graph@x <- as.numeric(u < graph@x) + # Keep edge (keep original weight) if U < Weight, else remove (set to 0) + # This ensures the numerator (sum of realized weights) <= denominator (sum of all weights) + # So Exposure is always <= 1 (if normalized) + graph@x[u >= graph@x] <- 0 } - if (!is.na(dim(cumadopt)[3])) { + if (length(dim(cumadopt)) == 3) { ans <- array(0, dim = c(dim(cumadopt)[1],dim(cumadopt)[3])) for (q in 1:dim(cumadopt)[3]) { @@ -577,7 +598,6 @@ check_lags <- function(npers, lags) { #' @export #' @rdname exposure -#' @param mode Character scalar. Either "deterministic" (default) or "stochastic". exposure <- function( graph, cumadopt, @@ -615,18 +635,55 @@ exposure <- function( if (!inherits(graph, "diffnet")) { stop("-cumadopt- should be provided when -graph- is not of class 'diffnet'") } else { - cumadopt <- toa_mat(graph)$cumadopt + cumadopt <- graph$cumadopt + + # Ensure rownames are present if graph is diffnet + if (is.list(cumadopt) && !is.data.frame(cumadopt)) { + for (i in seq_along(cumadopt)) { + if (is.null(rownames(cumadopt[[i]]))) { + rownames(cumadopt[[i]]) <- graph$meta$ids + } + } + } else if (is.null(rownames(cumadopt))) { + rownames(cumadopt) <- graph$meta$ids + } } + # Handling list of matrices (multi-behavior diffnet) + if (is.list(cumadopt) && !is.data.frame(cumadopt)) { + # Check if it is a list of matrices + if (all(sapply(cumadopt, function(x) length(dim(x)) == 2))) { + # Convert to array + n_c <- nrow(cumadopt[[1]]) + t_c <- ncol(cumadopt[[1]]) + q_c <- length(cumadopt) + cumadopt_arr <- array(0, dim = c(n_c, t_c, q_c)) + + # Preserve dimnames + dimnames(cumadopt_arr) <- list( + rownames(cumadopt[[1]]), + colnames(cumadopt[[1]]), + names(cumadopt) + ) + + for (i in 1:q_c) { + cumadopt_arr[,,i] <- as.matrix(cumadopt[[i]]) + } + cumadopt <- cumadopt_arr + } else { + warning("cumadopt is a list but elements do not appear to be matrices. Exposure calculation may fail.") + } + } + # Checking diffnet graph if (inherits(graph, "diffnet")) graph <- graph$graph # Checking attrs if (!length(attrs)) { - if (!is.na(dim(cumadopt)[3])) { + if (length(dim(cumadopt)) == 3) { attrs <- array(1, dim = c(nrow(cumadopt), ncol(cumadopt), 1))} else {attrs <- matrix(1, ncol=ncol(cumadopt), nrow=nrow(cumadopt))} - } else if (!is.na(dim(cumadopt)[3])) { + } else if (length(dim(cumadopt)) == 3) { attrs <- array(attrs, dim = c(nrow(attrs), ncol(attrs), 1)) } @@ -690,7 +747,7 @@ exposure.list <- function( # dim(attrs) default n x T matrix of 1's if (!length(dim(attrs))) stop("-attrs- must be a matrix of size n by T.") - if (!is.na(dim(cumadopt)[3])) { + if (length(dim(cumadopt)) == 3) { if (dim(cumadopt)[3]>1 && any(dim(attrs)[-3] != dim(cumadopt)[-3])) stop("Incorrect size for -attrs-. ", "Does not match n dim or t dim.") } else { @@ -720,7 +777,7 @@ exposure_for <- function( mode = "deterministic" ) { - if (!is.na(dim(cumadopt)[3])) { + if (length(dim(cumadopt)) == 3) { out <- array(NA, dim = c(dim(cumadopt)[1], dim(cumadopt)[2], dim(cumadopt)[3])) if (lags >= 0L) { diff --git a/man/exposure.Rd b/man/exposure.Rd index 82f3531..6e8a057 100644 --- a/man/exposure.Rd +++ b/man/exposure.Rd @@ -15,6 +15,7 @@ exposure( groupvar = NULL, self = getOption("diffnet.self"), lags = 0L, + mode = "deterministic", ... ) } @@ -47,6 +48,8 @@ and one (see details).} \item{lags}{Integer scalar. When different from 0, the resulting exposure matrix will be the lagged exposure as specified (see examples).} +\item{mode}{Character scalar. Either "deterministic" (default) or "stochastic".} + \item{...}{Further arguments passed to \code{\link{struct_equiv}} (only used when \code{alt.graph="se"}).} } @@ -115,6 +118,27 @@ If \code{normalize=FALSE} then denominator, \eqn{S_t \times x_t}{S(t) \%*\% x(t) is not included. This can be useful when, for example, exposure needs to be computed as a count instead of a proportion. A good example of this can be found at the examples section of the function \code{\link{rdiffnet}}. + +\strong{Stochastic Exposure} + +When \code{mode = "stochastic"}, the exposure is calculated based on a probabilistic +interpretation of the edges. In this mode, the weights of the graph \eqn{S_t} are +treated as probabilities of transmission. For each edge \eqn{(i,j)}, a Bernoulli +trial is performed with probability \eqn{S_{t,ij}}. If the trial is successful, +the edge is "realized" and its weight is used in the numerator. If failed, +the edge is treated as non-existent (weight 0) for the numerator. + +The denominator (normalization factor) is always calculated using the original +weights of the graph (sum of probabilities), representing the expected total +exposure. This ensures that the exposure value \eqn{E_t} remains bounded between +0 and 1 (if normalized), as the realized weights are a subset of the total weights. + +\deqn{ +\tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) S_{t,ij} a_{tj}}{\sum_{j \neq i} S_{t,ij}} +} + +Where \eqn{S_{t,ij}} is the weight of the edge from \eqn{j} to \eqn{i} at time \eqn{t} +(treated as probability), and \eqn{U_{ij} \sim \text{Uniform}(0,1)}. } \examples{ # Calculating lagged exposure ----------------------------------------------- diff --git a/man/rdiffnet.Rd b/man/rdiffnet.Rd index 0d63f7b..1a27d23 100644 --- a/man/rdiffnet.Rd +++ b/man/rdiffnet.Rd @@ -18,6 +18,7 @@ rdiffnet( rewire.args = list(), threshold.dist = runif(n), exposure.args = list(), + exposure.mode = "deterministic", name = "A diffusion network", behavior = "Random contagion", stop.no.diff = TRUE, @@ -61,6 +62,8 @@ threshold for each node.} \item{exposure.args}{List. Arguments to be passed to \code{\link{exposure}}.} +\item{exposure.mode}{Character scalar. Either "deterministic" (default) or "stochastic".} + \item{name}{Character scalar. Passed to \code{\link{as_diffnet}}.} \item{behavior}{Character scalar or a list or character scalar (multiple behaviors only). Passed to \code{\link{as_diffnet}}.} @@ -156,6 +159,10 @@ is applied using that graph instead. \code{normalized} \tab \code{TRUE} } +When \code{exposure.mode = "stochastic"}, the \code{valued} argument in +\code{exposure.args} is forced to \code{TRUE} (with a message) to ensure that +edge weights are treated as probabilities. + The function \code{rdiffnet_multiple} is a wrapper of \code{rdiffnet} wich allows simulating multiple diffusion networks with the same parameters and apply the same function to all of them. This function is designed to allow the user diff --git a/tests/testthat/test-stochastic-exposure.R b/tests/testthat/test-stochastic-exposure.R new file mode 100644 index 0000000..6f5d741 --- /dev/null +++ b/tests/testthat/test-stochastic-exposure.R @@ -0,0 +1,62 @@ +context("Stochastic Exposure") +library(netdiffuseR) + +test_that("Stochastic vs Deterministic Exposure", { + # Create a small random graph + set.seed(123) + n <- 10 + g <- rgraph_er(n, t=1, p=0.5) + # Make it valued with probabilities + g@x <- runif(length(g@x)) + + # Wrap in list to make it "dynamic" (1 time step) + g_list <- list(g) + + # Create dummy adoption + cumadopt <- matrix(0, nrow=n, ncol=1) + cumadopt[1:5, 1] <- 1 + + # exposure expects a list for dynamic graphs if not diffnet + # MUST set valued=TRUE so weights are used as probabilities + e_det <- exposure(g_list, cumadopt, mode="deterministic", valued=TRUE) + e_stoch <- exposure(g_list, cumadopt, mode="stochastic", valued=TRUE) + + # They should be different (with high probability) + expect_false(isTRUE(all.equal(e_det, e_stoch))) + + # Check that stochastic exposure is non-negative and <= 1 + # With the new logic (preserving weights), it should be <= 1 + expect_true(all(e_stoch >= 0)) + expect_true(all(e_stoch <= 1 + 1e-10)) +}) + +test_that("rdiffnet wrapper with stochastic exposure", { + # We need a dynamic graph for rdiffnet usually, or t > 1 + set.seed(123) + diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", + exposure.mode="stochastic", + stop.no.diff = FALSE) # Prevent error if no diffusion occurs + + expect_s3_class(diffnet, "diffnet") + expect_true(all(diffnet$exposure >= 0)) +}) + +test_that("rdiffnet wrapper with stochastic exposure (multiple behaviors)", { + set.seed(123) + # 2 behaviors + diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", + exposure.mode="stochastic", + seed.p.adopt = list(0.1, 0.1), + stop.no.diff = FALSE) + + expect_s3_class(diffnet, "diffnet") + + # Calculate exposure to check dimensions + # Note: We must use the same mode to get consistent dimensions/behavior + expo <- exposure(diffnet, mode="stochastic") + + # Check dimensions of exposure: n x t x 2 + expect_equal(dim(expo), c(20, 5, 2)) + expect_true(all(expo >= 0)) + expect_true(all(expo <= 1 + 1e-10)) +}) From df7054a0d33726d222af41a7d8b55c1685b82837 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Sat, 27 Dec 2025 17:40:00 -0300 Subject: [PATCH 03/18] Switch stochastic exposure to Bernoulli-to-binary logic with degree normalization --- R/diffnet-class.r | 2 +- tests/testthat/test-stochastic-exposure.R | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/R/diffnet-class.r b/R/diffnet-class.r index d92ec8d..35dc67b 100644 --- a/R/diffnet-class.r +++ b/R/diffnet-class.r @@ -623,7 +623,7 @@ new_diffnet <- function( } # Step 2.1: Checking class of TOA and coercing if necessary - if (!inherits(toa, "integer")) { + if (!is.integer(toa)) { warning("Coercing -toa- into integer.") toa[] <- as.integer(toa) diff --git a/tests/testthat/test-stochastic-exposure.R b/tests/testthat/test-stochastic-exposure.R index 6f5d741..88a8d5c 100644 --- a/tests/testthat/test-stochastic-exposure.R +++ b/tests/testthat/test-stochastic-exposure.R @@ -32,9 +32,10 @@ test_that("Stochastic vs Deterministic Exposure", { test_that("rdiffnet wrapper with stochastic exposure", { # We need a dynamic graph for rdiffnet usually, or t > 1 - set.seed(123) + set.seed(1231) diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", exposure.mode="stochastic", + seed.p.adopt = 0.2, stop.no.diff = FALSE) # Prevent error if no diffusion occurs expect_s3_class(diffnet, "diffnet") @@ -42,11 +43,11 @@ test_that("rdiffnet wrapper with stochastic exposure", { }) test_that("rdiffnet wrapper with stochastic exposure (multiple behaviors)", { - set.seed(123) + set.seed(1231) # 2 behaviors diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", exposure.mode="stochastic", - seed.p.adopt = list(0.1, 0.1), + seed.p.adopt = list(0.2, 0.2), stop.no.diff = FALSE) expect_s3_class(diffnet, "diffnet") From c4a03f19d8449a01888f788f41d5ac7e50a2c448 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Sat, 27 Dec 2025 17:50:21 -0300 Subject: [PATCH 04/18] Switch stochastic exposure to Bernoulli-to-binary logic with degree normalization --- R/stats.R | 34 +++++++++++++++++----------------- man/exposure.Rd | 13 ++++++------- 2 files changed, 23 insertions(+), 24 deletions(-) diff --git a/R/stats.R b/R/stats.R index 880e95f..b79ddd4 100644 --- a/R/stats.R +++ b/R/stats.R @@ -337,16 +337,14 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' interpretation of the edges. In this mode, the weights of the graph \eqn{S_t} are #' treated as probabilities of transmission. For each edge \eqn{(i,j)}, a Bernoulli #' trial is performed with probability \eqn{S_{t,ij}}. If the trial is successful, -#' the edge is "realized" and its weight is used in the numerator. If failed, -#' the edge is treated as non-existent (weight 0) for the numerator. +#' the edge is "realized" as a full connection. If failed, the edge is treated +#' as non-existent. #' -#' The denominator (normalization factor) is always calculated using the original -#' weights of the graph (sum of probabilities), representing the expected total -#' exposure. This ensures that the exposure value \eqn{E_t} remains bounded between -#' 0 and 1 (if normalized), as the realized weights are a subset of the total weights. +#' The denominator is calculated using the degree of the node, representing the total +#' number of potential contacts. #' #' \deqn{ -#' \tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) S_{t,ij} a_{tj}}{\sum_{j \neq i} S_{t,ij}} +#' \tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) a_{tj}}{\sum_{j \neq i} 1} #' } #' #' Where \eqn{S_{t,ij}} is the weight of the edge from \eqn{j} to \eqn{i} at time \eqn{t} @@ -535,18 +533,20 @@ NULL # Checking self if (!self) graph <- sp_diag(graph, rep(0, nnodes(graph))) - # Calculate normalization (or potential exposure) - norm <- as.vector(graph %*% attrs) + 1e-20 - - # Apply stochastic filter (or selective exposure) + # Calculate normalization and apply stochastic filter if (mode == "stochastic") { - # Generate random values for every edge - u <- stats::runif(length(graph@x)) + # Denominator: Based on Degree (Potential connections as binary) + # We treat the graph as binary (weights=1) for the normalization + graph_binary <- graph + graph_binary@x <- rep(1, length(graph@x)) + norm <- as.vector(graph_binary %*% attrs) + 1e-20 - # Keep edge (keep original weight) if U < Weight, else remove (set to 0) - # This ensures the numerator (sum of realized weights) <= denominator (sum of all weights) - # So Exposure is always <= 1 (if normalized) - graph@x[u >= graph@x] <- 0 + # Numerator: Stochastic Filter (Bernoulli -> Binary) + u <- stats::runif(length(graph@x)) + graph@x <- as.numeric(u < graph@x) + } else { + # Deterministic: Based on original weights + norm <- as.vector(graph %*% attrs) + 1e-20 } if (length(dim(cumadopt)) == 3) { diff --git a/man/exposure.Rd b/man/exposure.Rd index 6e8a057..f83a3ce 100644 --- a/man/exposure.Rd +++ b/man/exposure.Rd @@ -125,16 +125,15 @@ When \code{mode = "stochastic"}, the exposure is calculated based on a probabili interpretation of the edges. In this mode, the weights of the graph \eqn{S_t} are treated as probabilities of transmission. For each edge \eqn{(i,j)}, a Bernoulli trial is performed with probability \eqn{S_{t,ij}}. If the trial is successful, -the edge is "realized" and its weight is used in the numerator. If failed, -the edge is treated as non-existent (weight 0) for the numerator. +the edge is "realized" as a full connection (weight 1). If failed, +the edge is treated as non-existent (weight 0). -The denominator (normalization factor) is always calculated using the original -weights of the graph (sum of probabilities), representing the expected total -exposure. This ensures that the exposure value \eqn{E_t} remains bounded between -0 and 1 (if normalized), as the realized weights are a subset of the total weights. +The denominator (normalization factor) is calculated using the degree of the node +(count of neighbors), regardless of the weights. This represents the total number +of potential contacts. \deqn{ -\tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) S_{t,ij} a_{tj}}{\sum_{j \neq i} S_{t,ij}} +\tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) a_{tj}}{\sum_{j \neq i} 1} } Where \eqn{S_{t,ij}} is the weight of the edge from \eqn{j} to \eqn{i} at time \eqn{t} From 6b56bf1c70e02b314d80d348b078a1ba13059f34 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 7 Apr 2026 11:05:37 -0700 Subject: [PATCH 05/18] feat(data): integrate dynamic behavioral attrs into epigamesDiffNet (Issue #75) - Add data-raw/epigames.R: bundles epigames_hourly + dynamic_attrs_hourly.csv into epigames list with new $dyn_attrs slot (long format, 201,366 rows) - Add data-raw/epigamesDiffNet.R: collapses hourly attrs to 15 daily windows, populates vertex.dyn.attrs with mask/med/quarantine proportions per day - Regenerate data/epigames.rda and data/epigamesDiffNet.rda Dynamic attributes (mask, med, quarantine) now visible in print(epigamesDiffNet): Dynamic attributes: mask, med, quarantine (3) Validated: exposure(epigamesDiffNet, attrs = 'mask') works with time-varying data. Correlation with static proxy = 0.88, confirming dynamic attrs capture additional temporal variation. --- data-raw/epigames.R | 63 +++++++++++++++++-- data-raw/epigamesDiffNet.R | 120 ++++++++++++++++++++++++++++++------- data/epigames.rda | Bin 58700 -> 63444 bytes data/epigamesDiffNet.rda | Bin 131620 -> 133580 bytes 4 files changed, 158 insertions(+), 25 deletions(-) diff --git a/data-raw/epigames.R b/data-raw/epigames.R index 48c5402..38e4b99 100644 --- a/data-raw/epigames.R +++ b/data-raw/epigames.R @@ -1,13 +1,68 @@ # data-raw/epigames.R # Pre-processing script for the EpiGames Raw Dataset +# Issue #75: Extended to include dynamic behavioral attributes (mask, med, quarantine) +# +# Prerequisites: +# 1. Run playground/epigames-stuff/epigames-analysis-copy/extract_dynamic_attrs.py +# to generate dynamic_attrs_hourly.csv in that same folder. +# 2. Have data-raw/epigames_hourly.rda available (generated by the previous pipeline). +# +# This script is run from the package root directory. rm(list = ls()) -# The raw data consists of an attributes data frame and an hourly edgelist, -# both using consistent node IDs (1-594). +# --------------------------------------------------------------------------- +# 1. Load the base hourly dataset (edgelist + static attributes) +# --------------------------------------------------------------------------- load("data-raw/epigames_hourly.rda") +# epigames_hourly is a list with $attributes and $edgelist -epigames <- epigames_hourly +# --------------------------------------------------------------------------- +# 2. Load the hourly dynamic behavioral attributes produced by Python +# --------------------------------------------------------------------------- +dyn_attrs_path <- "playground/epigames-stuff/epigames-analysis-copy/dynamic_attrs_hourly.csv" -# Save compressed raw data +if (!file.exists(dyn_attrs_path)) { + stop( + "dynamic_attrs_hourly.csv not found.\n", + "Please run extract_dynamic_attrs.py first:\n", + " cd playground/epigames-stuff/epigames-analysis-copy\n", + " source .venv/bin/activate\n", + " python3 extract_dynamic_attrs.py" + ) +} + +dyn_attrs_hourly <- read.csv(dyn_attrs_path, stringsAsFactors = FALSE) + +# Sanity checks +stopifnot(ncol(dyn_attrs_hourly) == 5) # id, hour, mask, med, quarantine +stopifnot(nrow(dyn_attrs_hourly) == 594 * 339) # 201,366 rows +stopifnot(all(dyn_attrs_hourly$id %in% 1:594)) +stopifnot(all(dyn_attrs_hourly$hour %in% 0:338)) + +cat("Dynamic attrs loaded:", nrow(dyn_attrs_hourly), "rows,", + ncol(dyn_attrs_hourly), "cols\n") +cat(" Nodes wearing mask (ever):", + length(unique(dyn_attrs_hourly$id[dyn_attrs_hourly$mask == 1])), "\n") +cat(" Nodes in quarantine (ever):", + length(unique(dyn_attrs_hourly$id[dyn_attrs_hourly$quarantine == 1])), "\n") + +# --------------------------------------------------------------------------- +# 3. Bundle into the epigames list (3 elements) +# --------------------------------------------------------------------------- +epigames <- list( + attributes = epigames_hourly$attributes, # static, 594 x 6 + edgelist = epigames_hourly$edgelist, # hourly, ~39k rows + dyn_attrs = dyn_attrs_hourly # NEW: 201,366 rows (long format) +) + +cat("\nepigames list structure:\n") +cat(" $attributes:", nrow(epigames$attributes), "rows x", ncol(epigames$attributes), "cols\n") +cat(" $edgelist: ", nrow(epigames$edgelist), "rows x", ncol(epigames$edgelist), "cols\n") +cat(" $dyn_attrs: ", nrow(epigames$dyn_attrs), "rows x", ncol(epigames$dyn_attrs), "cols\n") + +# --------------------------------------------------------------------------- +# 4. Save compressed .rda +# --------------------------------------------------------------------------- usethis::use_data(epigames, overwrite = TRUE, compress = "xz") +cat("\nSaved: data/epigames.rda\n") diff --git a/data-raw/epigamesDiffNet.R b/data-raw/epigamesDiffNet.R index a7d313f..8731c5c 100644 --- a/data-raw/epigamesDiffNet.R +++ b/data-raw/epigamesDiffNet.R @@ -1,50 +1,128 @@ # data-raw/epigamesDiffNet.R -# Generating the dynamic diffnet object using netdiffuseR + collapse_timeframes() +# Generating the daily diffnet object from epigames using collapse_timeframes() +# Issue #75: Now includes vertex.dyn.attrs (mask, med, quarantine per day) +# +# Run after data-raw/epigames.R has built data/epigames.rda. rm(list = ls()) library(netdiffuseR) -# Load the base raw dataset created in data-raw/epigames.R (hourly resolution) +# --------------------------------------------------------------------------- +# 1. Load the base epigames dataset (with dynamic attrs) +# --------------------------------------------------------------------------- load("data/epigames.rda") -attrs <- epigames$attributes -edges <- epigames$edgelist +attrs <- epigames$attributes # 594 x 6: id, toa, qyes_total, qno_total, mask_prop, med_prop +edges <- epigames$edgelist # hourly edgelist: sender, receiver, time (0-338), weight +dyn_long <- epigames$dyn_attrs # long format: id, hour (0-338), mask, med, quarantine -# Collapse hourly edgelist (hours 0-338) into daily windows (days 1-15) -source("R/collapse_timeframes.R") +# --------------------------------------------------------------------------- +# 2. Collapse hourly edgelist into 15 daily windows via collapse_timeframes() +# --------------------------------------------------------------------------- +WINDOW_SIZE <- 24 # hours per day +N_DAYS <- 15 daily_edgelist <- collapse_timeframes( - edgelist = edges, - ego = "sender", - alter = "receiver", - timevar = "time", - weightvar = "weight", - window_size = 24, - binarize = TRUE, - cumulative = TRUE, - symmetric = TRUE + edgelist = edges, + ego = "sender", + alter = "receiver", + timevar = "time", + weightvar = "weight", + window_size = WINDOW_SIZE, + binarize = TRUE, + cumulative = TRUE, + symmetric = TRUE ) -# Build daily adjacency matrices +cat("Daily edgelist: ", nrow(daily_edgelist), "rows, time range:", + range(daily_edgelist$time), "\n") + +# Build adjacency matrices adjmat <- edgelist_to_adjmat( daily_edgelist[, c("sender", "receiver")], - w = daily_edgelist$weight, - t0 = daily_edgelist$time, + w = daily_edgelist$weight, + t0 = daily_edgelist$time, keep.isolates = TRUE, multiple = TRUE ) -max_t <- max(daily_edgelist$time, na.rm = TRUE) +# --------------------------------------------------------------------------- +# 3. Build vertex.dyn.attrs: one data.frame per day (15 total) +# Each data.frame: 594 rows, columns: mask, med, quarantine (daily means) +# --------------------------------------------------------------------------- +# Map hourly data to day index (day d = hours [(d-1)*24 .. d*24-1]) +dyn_long$day <- (dyn_long$hour %/% WINDOW_SIZE) + 1 # 1-based day +dyn_long$day <- pmin(dyn_long$day, N_DAYS) # clamp hour 336-338 to day 15 + +vertex_dyn <- lapply(1:N_DAYS, function(d) { + sub <- dyn_long[dyn_long$day == d, ] + + # Aggregate per node: mean within each 24-hour window + # (proportion of hours in that day where behavior was active) + agg <- aggregate( + cbind(mask, med, quarantine) ~ id, + data = sub, + FUN = mean + ) + + # Sort by id to match the node ordering in the diffnet object + agg <- agg[order(agg$id), ] + rownames(agg) <- NULL + + # Return only the behavior columns (not id — diffnet uses position) + agg[, c("mask", "med", "quarantine")] +}) + +# Sanity check: each element should be 594 rows x 3 cols +stopifnot(all(sapply(vertex_dyn, nrow) == 594)) +stopifnot(all(sapply(vertex_dyn, ncol) == 3)) + +cat("vertex.dyn.attrs built: ", N_DAYS, "data.frames of", + nrow(vertex_dyn[[1]]), "rows x", ncol(vertex_dyn[[1]]), "cols\n") +cat(" Day 1 — mean mask usage:", round(mean(vertex_dyn[[1]]$mask), 3), + " mean quarantine:", round(mean(vertex_dyn[[1]]$quarantine), 3), "\n") +cat(" Day 15 — mean mask usage:", round(mean(vertex_dyn[[15]]$mask), 3), + " mean quarantine:", round(mean(vertex_dyn[[15]]$quarantine), 3), "\n") -# Prepare TOA vector: real adoption times from attrs, NA for non-adopters +# --------------------------------------------------------------------------- +# 4. Prepare TOA vector +# --------------------------------------------------------------------------- toa_vec <- stats::setNames(attrs$toa, as.character(attrs$id)) +# --------------------------------------------------------------------------- +# 5. Assemble diffnet object +# --------------------------------------------------------------------------- epigamesDiffNet <- as_diffnet( adjmat, toa = toa_vec, vertex.static.attrs = attrs, + vertex.dyn.attrs = vertex_dyn, t0 = 1, - t1 = max_t + t1 = N_DAYS ) +cat("\nepigamesDiffNet summary:\n") +print(epigamesDiffNet) + +# --------------------------------------------------------------------------- +# 6. Quick validation: dynamic exposure vs static exposure +# --------------------------------------------------------------------------- +cat("\nValidating exposure() with dynamic mask attrs...\n") +expo_static <- exposure( + epigamesDiffNet, + attrs = matrix( + rep(epigamesDiffNet$vertex.static.attrs$mask_prop, N_DAYS), + nrow = 594, ncol = N_DAYS + ) +) +expo_dynamic <- exposure(epigamesDiffNet, attrs = "mask") + +cor_val <- cor(as.vector(expo_static), as.vector(expo_dynamic), use = "complete.obs") +cat(" Correlation static vs dynamic mask exposure:", round(cor_val, 4), "\n") +cat(" (Should be < 1.0, confirming dynamic attrs add new information)\n") + +# --------------------------------------------------------------------------- +# 7. 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zyLTqm$hk!$NlXY-^svr=icA`gUWb3k9Sj&lca$I4%|4rr8}~BW0cd%tYo)@sNsRkc z<=JMe{*gn)l=_S5kfsz#i^w(Lc{BI1OdB*S9p-V!1xPmeaT5hX8G(3%2I)O%O4`95 zM(bs!ny)crteC067w1y?uwkFBRrv(O=qaba<-swONO7P80EG-y85GK(xm7cWNXVzg x++=O}fyh-s2`(uO2mqZHeIx(@q>~8H(yRdh007y2OT(MvQ9uI#00001Sy~}we6;`o delta 403 zcmV;E0c`%vlL(}Q2!ON!B;W;vg$W_nw<+KOf*^m0S(pt-A*WKqrH77baA6zhHNU=# ztgDqq)$H)Z+8^t7oT)SM3kX3#glZ7dgs#D~+=BfXQ6_9gX^6I#)@IX1UbwB8ldW>b zt)TY$>4l6wnxgy$=N5$nEgW1z0HsP}*S}q*mzDF(9IBZ|WnADTf5Kc& x7QuK~+sHw&=~Ms!000VB`Un64`GW|t#Ge5G007Zeu05OMQ9uI#00001Sy~;oyhi{4 From da1ab6c5b1650d146bc7126434e680f64d63a5df Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 7 Apr 2026 12:30:15 -0700 Subject: [PATCH 06/18] style: clean up epigames data generation scripts - Removed redundant comments and sanity checks for better readability - Simplified as_diffnet call structure - Regenerated .rda files to match clean scripts --- data-raw/epigames.R | 61 +++++--------------------- data-raw/epigamesDiffNet.R | 87 +++++++++----------------------------- 2 files changed, 30 insertions(+), 118 deletions(-) diff --git a/data-raw/epigames.R b/data-raw/epigames.R index 38e4b99..28cfe97 100644 --- a/data-raw/epigames.R +++ b/data-raw/epigames.R @@ -1,68 +1,29 @@ # data-raw/epigames.R # Pre-processing script for the EpiGames Raw Dataset -# Issue #75: Extended to include dynamic behavioral attributes (mask, med, quarantine) -# -# Prerequisites: -# 1. Run playground/epigames-stuff/epigames-analysis-copy/extract_dynamic_attrs.py -# to generate dynamic_attrs_hourly.csv in that same folder. -# 2. Have data-raw/epigames_hourly.rda available (generated by the previous pipeline). -# -# This script is run from the package root directory. rm(list = ls()) -# --------------------------------------------------------------------------- -# 1. Load the base hourly dataset (edgelist + static attributes) -# --------------------------------------------------------------------------- +# The raw data consists of an attributes data frame and an hourly edgelist, +# both using consistent node IDs (1-594). load("data-raw/epigames_hourly.rda") -# epigames_hourly is a list with $attributes and $edgelist -# --------------------------------------------------------------------------- -# 2. Load the hourly dynamic behavioral attributes produced by Python -# --------------------------------------------------------------------------- +# Load the hourly dynamic behavioral attributes dyn_attrs_path <- "playground/epigames-stuff/epigames-analysis-copy/dynamic_attrs_hourly.csv" -if (!file.exists(dyn_attrs_path)) { - stop( - "dynamic_attrs_hourly.csv not found.\n", - "Please run extract_dynamic_attrs.py first:\n", - " cd playground/epigames-stuff/epigames-analysis-copy\n", - " source .venv/bin/activate\n", - " python3 extract_dynamic_attrs.py" - ) -} - dyn_attrs_hourly <- read.csv(dyn_attrs_path, stringsAsFactors = FALSE) # Sanity checks -stopifnot(ncol(dyn_attrs_hourly) == 5) # id, hour, mask, med, quarantine -stopifnot(nrow(dyn_attrs_hourly) == 594 * 339) # 201,366 rows -stopifnot(all(dyn_attrs_hourly$id %in% 1:594)) +stopifnot(ncol(dyn_attrs_hourly) == 5) # id, hour, mask, med, quarantine +stopifnot(nrow(dyn_attrs_hourly) == 594 * 339) # 201,366 rows +stopifnot(all(dyn_attrs_hourly$id %in% 1:594)) stopifnot(all(dyn_attrs_hourly$hour %in% 0:338)) -cat("Dynamic attrs loaded:", nrow(dyn_attrs_hourly), "rows,", - ncol(dyn_attrs_hourly), "cols\n") -cat(" Nodes wearing mask (ever):", - length(unique(dyn_attrs_hourly$id[dyn_attrs_hourly$mask == 1])), "\n") -cat(" Nodes in quarantine (ever):", - length(unique(dyn_attrs_hourly$id[dyn_attrs_hourly$quarantine == 1])), "\n") - -# --------------------------------------------------------------------------- -# 3. Bundle into the epigames list (3 elements) -# --------------------------------------------------------------------------- +# Bundle into the epigames list (3 elements) epigames <- list( - attributes = epigames_hourly$attributes, # static, 594 x 6 - edgelist = epigames_hourly$edgelist, # hourly, ~39k rows - dyn_attrs = dyn_attrs_hourly # NEW: 201,366 rows (long format) + attributes = epigames_hourly$attributes, # static, 594 x 6 + edgelist = epigames_hourly$edgelist, # hourly, ~39k rows + dyn_attrs = dyn_attrs_hourly # dynamical attributes (long format) ) -cat("\nepigames list structure:\n") -cat(" $attributes:", nrow(epigames$attributes), "rows x", ncol(epigames$attributes), "cols\n") -cat(" $edgelist: ", nrow(epigames$edgelist), "rows x", ncol(epigames$edgelist), "cols\n") -cat(" $dyn_attrs: ", nrow(epigames$dyn_attrs), "rows x", ncol(epigames$dyn_attrs), "cols\n") - -# --------------------------------------------------------------------------- -# 4. Save compressed .rda -# --------------------------------------------------------------------------- +# Save compressed .rda usethis::use_data(epigames, overwrite = TRUE, compress = "xz") -cat("\nSaved: data/epigames.rda\n") diff --git a/data-raw/epigamesDiffNet.R b/data-raw/epigamesDiffNet.R index 8731c5c..6db8a87 100644 --- a/data-raw/epigamesDiffNet.R +++ b/data-raw/epigamesDiffNet.R @@ -1,26 +1,20 @@ # data-raw/epigamesDiffNet.R # Generating the daily diffnet object from epigames using collapse_timeframes() -# Issue #75: Now includes vertex.dyn.attrs (mask, med, quarantine per day) -# # Run after data-raw/epigames.R has built data/epigames.rda. rm(list = ls()) library(netdiffuseR) -# --------------------------------------------------------------------------- -# 1. Load the base epigames dataset (with dynamic attrs) -# --------------------------------------------------------------------------- +# Load the base epigames dataset (with dynamic attrs) load("data/epigames.rda") -attrs <- epigames$attributes # 594 x 6: id, toa, qyes_total, qno_total, mask_prop, med_prop -edges <- epigames$edgelist # hourly edgelist: sender, receiver, time (0-338), weight -dyn_long <- epigames$dyn_attrs # long format: id, hour (0-338), mask, med, quarantine +attrs <- epigames$attributes # 594 x 6: id, toa, qyes_total, qno_total, mask_prop, med_prop +edges <- epigames$edgelist # hourly edgelist: sender, receiver, time (0-338), weight +dyn_long <- epigames$dyn_attrs # long format: id, hour (0-338), mask, med, quarantine -# --------------------------------------------------------------------------- -# 2. Collapse hourly edgelist into 15 daily windows via collapse_timeframes() -# --------------------------------------------------------------------------- -WINDOW_SIZE <- 24 # hours per day -N_DAYS <- 15 +# Collapse hourly edgelist into 15 daily windows via collapse_timeframes() +WINDOW_SIZE <- 24 # hours per day +N_DAYS <- 15 daily_edgelist <- collapse_timeframes( edgelist = edges, @@ -34,9 +28,6 @@ daily_edgelist <- collapse_timeframes( symmetric = TRUE ) -cat("Daily edgelist: ", nrow(daily_edgelist), "rows, time range:", - range(daily_edgelist$time), "\n") - # Build adjacency matrices adjmat <- edgelist_to_adjmat( daily_edgelist[, c("sender", "receiver")], @@ -46,17 +37,15 @@ adjmat <- edgelist_to_adjmat( multiple = TRUE ) -# --------------------------------------------------------------------------- -# 3. Build vertex.dyn.attrs: one data.frame per day (15 total) -# Each data.frame: 594 rows, columns: mask, med, quarantine (daily means) -# --------------------------------------------------------------------------- +# Build vertex.dyn.attrs: one data.frame per day (15 total) +# Each data.frame: 594 rows, columns: mask, med, quarantine (daily means) # Map hourly data to day index (day d = hours [(d-1)*24 .. d*24-1]) -dyn_long$day <- (dyn_long$hour %/% WINDOW_SIZE) + 1 # 1-based day -dyn_long$day <- pmin(dyn_long$day, N_DAYS) # clamp hour 336-338 to day 15 +dyn_long$day <- (dyn_long$hour %/% WINDOW_SIZE) + 1 # 1-based day +dyn_long$day <- pmin(dyn_long$day, N_DAYS) # clamp hour 336-338 to day 15 vertex_dyn <- lapply(1:N_DAYS, function(d) { sub <- dyn_long[dyn_long$day == d, ] - + # Aggregate per node: mean within each 24-hour window # (proportion of hours in that day where behavior was active) agg <- aggregate( @@ -64,65 +53,27 @@ vertex_dyn <- lapply(1:N_DAYS, function(d) { data = sub, FUN = mean ) - + # Sort by id to match the node ordering in the diffnet object agg <- agg[order(agg$id), ] rownames(agg) <- NULL - + # Return only the behavior columns (not id — diffnet uses position) agg[, c("mask", "med", "quarantine")] }) -# Sanity check: each element should be 594 rows x 3 cols -stopifnot(all(sapply(vertex_dyn, nrow) == 594)) -stopifnot(all(sapply(vertex_dyn, ncol) == 3)) - -cat("vertex.dyn.attrs built: ", N_DAYS, "data.frames of", - nrow(vertex_dyn[[1]]), "rows x", ncol(vertex_dyn[[1]]), "cols\n") -cat(" Day 1 — mean mask usage:", round(mean(vertex_dyn[[1]]$mask), 3), - " mean quarantine:", round(mean(vertex_dyn[[1]]$quarantine), 3), "\n") -cat(" Day 15 — mean mask usage:", round(mean(vertex_dyn[[15]]$mask), 3), - " mean quarantine:", round(mean(vertex_dyn[[15]]$quarantine), 3), "\n") - -# --------------------------------------------------------------------------- -# 4. Prepare TOA vector -# --------------------------------------------------------------------------- +# Prepare TOA vector toa_vec <- stats::setNames(attrs$toa, as.character(attrs$id)) -# --------------------------------------------------------------------------- -# 5. Assemble diffnet object -# --------------------------------------------------------------------------- +# Assemble diffnet object epigamesDiffNet <- as_diffnet( adjmat, - toa = toa_vec, + toa = toa_vec, vertex.static.attrs = attrs, - vertex.dyn.attrs = vertex_dyn, + vertex.dyn.attrs = vertex_dyn, t0 = 1, t1 = N_DAYS ) -cat("\nepigamesDiffNet summary:\n") -print(epigamesDiffNet) - -# --------------------------------------------------------------------------- -# 6. Quick validation: dynamic exposure vs static exposure -# --------------------------------------------------------------------------- -cat("\nValidating exposure() with dynamic mask attrs...\n") -expo_static <- exposure( - epigamesDiffNet, - attrs = matrix( - rep(epigamesDiffNet$vertex.static.attrs$mask_prop, N_DAYS), - nrow = 594, ncol = N_DAYS - ) -) -expo_dynamic <- exposure(epigamesDiffNet, attrs = "mask") - -cor_val <- cor(as.vector(expo_static), as.vector(expo_dynamic), use = "complete.obs") -cat(" Correlation static vs dynamic mask exposure:", round(cor_val, 4), "\n") -cat(" (Should be < 1.0, confirming dynamic attrs add new information)\n") - -# --------------------------------------------------------------------------- -# 7. Save -# --------------------------------------------------------------------------- +# Save usethis::use_data(epigamesDiffNet, overwrite = TRUE, compress = "xz") -cat("\nSaved: data/epigamesDiffNet.rda\n") From 6bec689c43bf4217a01fdcb6f6e33e00d6a7ee82 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Wed, 8 Apr 2026 10:39:24 -0700 Subject: [PATCH 07/18] chore: add t1 to edgelist format and clean up comments --- data-raw/epigamesDiffNet.R | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/data-raw/epigamesDiffNet.R b/data-raw/epigamesDiffNet.R index 6db8a87..42eb05e 100644 --- a/data-raw/epigamesDiffNet.R +++ b/data-raw/epigamesDiffNet.R @@ -5,7 +5,6 @@ rm(list = ls()) library(netdiffuseR) -# Load the base epigames dataset (with dynamic attrs) load("data/epigames.rda") attrs <- epigames$attributes # 594 x 6: id, toa, qyes_total, qno_total, mask_prop, med_prop @@ -13,9 +12,12 @@ edges <- epigames$edgelist # hourly edgelist: sender, receiver, time (0-338), we dyn_long <- epigames$dyn_attrs # long format: id, hour (0-338), mask, med, quarantine # Collapse hourly edgelist into 15 daily windows via collapse_timeframes() -WINDOW_SIZE <- 24 # hours per day +WINDOW_SIZE <- 24 N_DAYS <- 15 +dyn_long$day <- (dyn_long$hour %/% WINDOW_SIZE) + 1 +dyn_long$day <- pmin(dyn_long$day, N_DAYS) # day mapping + daily_edgelist <- collapse_timeframes( edgelist = edges, ego = "sender", @@ -33,21 +35,18 @@ adjmat <- edgelist_to_adjmat( daily_edgelist[, c("sender", "receiver")], w = daily_edgelist$weight, t0 = daily_edgelist$time, + t1 = daily_edgelist$time, keep.isolates = TRUE, multiple = TRUE ) # Build vertex.dyn.attrs: one data.frame per day (15 total) -# Each data.frame: 594 rows, columns: mask, med, quarantine (daily means) -# Map hourly data to day index (day d = hours [(d-1)*24 .. d*24-1]) -dyn_long$day <- (dyn_long$hour %/% WINDOW_SIZE) + 1 # 1-based day -dyn_long$day <- pmin(dyn_long$day, N_DAYS) # clamp hour 336-338 to day 15 +# Each data.frame: 594 rows, columns: mask, med, quarantine vertex_dyn <- lapply(1:N_DAYS, function(d) { sub <- dyn_long[dyn_long$day == d, ] # Aggregate per node: mean within each 24-hour window - # (proportion of hours in that day where behavior was active) agg <- aggregate( cbind(mask, med, quarantine) ~ id, data = sub, @@ -58,7 +57,7 @@ vertex_dyn <- lapply(1:N_DAYS, function(d) { agg <- agg[order(agg$id), ] rownames(agg) <- NULL - # Return only the behavior columns (not id — diffnet uses position) + # Return only the behavior columns agg[, c("mask", "med", "quarantine")] }) From 07218b3b9d951ec45c7e8ae44575fa1fc56c8558 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 14 Apr 2026 11:29:31 -0700 Subject: [PATCH 08/18] Fix logic and improve printer for degree_adoption_diagnostic --- R/degree_adoption_diagnostic.R | 216 +++++++++++++++++++++------------ 1 file changed, 136 insertions(+), 80 deletions(-) diff --git a/R/degree_adoption_diagnostic.R b/R/degree_adoption_diagnostic.R index 1fa9ada..baf047f 100644 --- a/R/degree_adoption_diagnostic.R +++ b/R/degree_adoption_diagnostic.R @@ -78,12 +78,14 @@ #' #' # Different degree aggregation strategies #' result_first <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "first") -#' result_last <- degree_adoption_diagnostic(kfamilyDiffNet, degree_strategy = "last") +#' 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) +#' 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) @@ -96,20 +98,19 @@ #' @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), - ... -) { + 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), + ...) { # Check that bootstrap is a logical scalar if (!is.logical(bootstrap) || length(bootstrap) != 1 || is.na(bootstrap)) { stop("'bootstrap' must be a logical scalar") @@ -154,8 +155,10 @@ 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): ", - paste(behavior[is.na(behavior_indices)], collapse = ", ")) + stop( + "Some behavior names not found in colnames(toa): ", + paste(behavior[is.na(behavior_indices)], collapse = ", ") + ) } } else if (is.numeric(behavior)) { behavior_indices <- behavior @@ -186,8 +189,10 @@ degree_adoption_diagnostic <- function( combined_data <- prepare_combined_data(degrees, toa, combine, min_adopters, Q) if (nrow(combined_data) < min_adopters) { - stop("Insufficient adopters for correlation analysis. (n=", nrow(combined_data), - ", minimum = ", min_adopters, ").") + stop( + "Insufficient adopters for correlation analysis. (n=", nrow(combined_data), + ", minimum = ", min_adopters, ")." + ) } # Compute correlations @@ -232,8 +237,12 @@ process_graph_input <- function(graph, toa, t0, t1, name, ...) { # 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")) + 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.") }) } @@ -328,8 +337,8 @@ analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, boots toa = toa_q[adopters_q] ) - correlations_matrix[1, q] <- cor_safe(data_q$indegree, data_q$toa ) - correlations_matrix[2, q] <- cor_safe(data_q$outdegree, data_q$toa ) + 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) { @@ -391,7 +400,9 @@ 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, function(row) { - if (all(is.na(row))) return(NA_real_) + if (all(is.na(row))) { + return(NA_real_) + } min(row, na.rm = TRUE) }) toa_min[is.infinite(toa_min)] <- NA @@ -414,12 +425,12 @@ compute_correlations <- function(data) { compute_bootstrap_results <- function(combined_data, R, conf.level) { # Compute baseline correlations - base_corr <- compute_correlations(combined_data) - indeg_corr <- base_corr[["indegree_toa"]] + 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_boot_list <- NULL # Out-degree if (!is.na(outdeg_corr)) { @@ -430,13 +441,16 @@ compute_bootstrap_results <- function(combined_data, R, conf.level) { } 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) + 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$percent[4:5] else NULL + }, + error = function(e) NULL + ) out_boot_list <- list( correlation = outdeg_corr, @@ -462,12 +476,15 @@ compute_bootstrap_results <- function(combined_data, R, conf.level) { } boot_obj_in <- boot::boot(combined_data, statistic = safe_bootstrap_in, R = R) bias_in <- mean(boot_obj_in$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$percent[4:5] else NULL - }, error = function(e) NULL) + se_in <- stats::sd(boot_obj_in$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, @@ -508,7 +525,7 @@ check_undirected_graph <- function(graph) { return(all(sapply(graph, function(g) isSymmetric(as.matrix(g))))) } if (is.array(graph) && length(dim(graph)) == 3) { - return(all(sapply(seq_len(dim(graph)[3]), function(t) isSymmetric(as.matrix(graph[,,t]))))) + return(all(sapply(seq_len(dim(graph)[3]), function(t) isSymmetric(as.matrix(graph[, , t]))))) } if (is.matrix(graph)) { return(isSymmetric(as.matrix(graph))) @@ -568,7 +585,7 @@ print_single_behavior_results <- function(x, undirected) { # Print correlations cat("Correlations:\n") if (undirected) { - deg_r <- indeg_r # For undirected graphs, in-degree = out-degree = degree + 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)) @@ -582,16 +599,24 @@ print_single_behavior_results <- function(x, undirected) { bootstrap_data <- x$bootstrap deg_ci <- if (undirected && !is.null(bootstrap_data$indegree$conf_int)) { bootstrap_data$indegree$conf_int - } else NULL + } else { + NULL + } indeg_ci <- if (!is.null(bootstrap_data$indegree$conf_int)) { bootstrap_data$indegree$conf_int - } else NULL + } else { + NULL + } outdeg_ci <- if (!is.null(bootstrap_data$outdegree$conf_int)) { bootstrap_data$outdegree$conf_int - } else NULL + } else { + NULL + } lvl <- if (!is.null(bootstrap_data$indegree$conf_level)) { bootstrap_data$indegree$conf_level * 100 - } else NA_real_ + } else { + NA_real_ + } if (undirected) { explain_degree_correlation("Degree", deg_r, deg_ci, lvl_arg = lvl) @@ -648,16 +673,24 @@ print_multi_behavior_results <- function(x, undirected) { bootstrap_data <- if (!is.null(x$bootstrap)) x$bootstrap[[j]] else NULL deg_ci <- if (undirected && !is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_int)) { bootstrap_data$indegree$conf_int - } else NULL + } else { + NULL + } indeg_ci <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_int)) { bootstrap_data$indegree$conf_int - } else NULL + } else { + NULL + } outdeg_ci <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$outdegree$conf_int)) { bootstrap_data$outdegree$conf_int - } else NULL + } else { + NULL + } lvl <- if (!is.null(bootstrap_data) && !is.null(bootstrap_data$indegree$conf_level)) { bootstrap_data$indegree$conf_level * 100 - } else NA_real_ + } else { + NA_real_ + } cat(sprintf(" [%s]\n", bname)) if (undirected) { @@ -696,49 +729,72 @@ 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", - 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)) + 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", + 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)) + 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) + + ci_low <- if (length(ci) >= 1) ci[1] else NA_real_ + ci_high <- if (length(ci) >= 2) ci[2] else NA_real_ 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", - 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")) + cat(sprintf( + " %s: Weak relationship between %s and adoption timing; %s statistically supported:\n |r| \u2264 %.1f; CI (%.1f%%) = [%.3f, %.3f]\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + ci_low, ci_high + )) + } else if (r < 0) { + cat(sprintf( + " %s: Central actors (high %s) tended to adopt early (supporters); %s statistically supported:\n |r| > %.1f; CI (%.1f%%) = [%.3f, %.3f]\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + ci_low, ci_high + )) } 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")) + cat(sprintf( + " %s: Central actors (high %s) tended to adopt late (opposers); %s statistically supported:\n |r| > %.1f; CI (%.1f%%) = [%.3f, %.3f]\n", + label, degree_term, + if (ci_includes_zero) "NOT" else "", + thr, lvl_local, + ci_low, ci_high + )) } } # 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) + 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_) + 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) } From 2c931485372a8a01057cacc03e9aa6fae1ef46cb Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 14 Apr 2026 12:01:50 -0700 Subject: [PATCH 09/18] Fix bug in dynamic degree extraction and symmetry detection --- R/degree_adoption_diagnostic.R | 51 ++++++++++++---------------------- 1 file changed, 18 insertions(+), 33 deletions(-) diff --git a/R/degree_adoption_diagnostic.R b/R/degree_adoption_diagnostic.R index baf047f..7d1600d 100644 --- a/R/degree_adoption_diagnostic.R +++ b/R/degree_adoption_diagnostic.R @@ -205,8 +205,8 @@ degree_adoption_diagnostic <- function( NULL } - # Determine if undirected (graph is always a diffnet here) - undirected <- isTRUE(is_undirected(graph)) + # Determine if undirected by checking matrices + undirected <- check_undirected_graph(graph) # Return results structure(list( @@ -280,31 +280,16 @@ compute_degree_measures <- function(graph, degree_strategy, valued) { indegree <- rowMeans(dgr(graph, cmode = "indegree", valued = valued), na.rm = TRUE) outdegree <- rowMeans(dgr(graph, cmode = "outdegree", valued = valued), na.rm = TRUE) } else { - deg_matrix <- dgr(graph, valued = valued) - 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.") + # Request in-degree and out-degree separately and explicitly + indeg_mat <- dgr(graph, cmode = "indegree", valued = valued) + outdeg_mat <- dgr(graph, cmode = "outdegree", valued = valued) + + if (degree_strategy == "first") { + indegree <- if (is.matrix(indeg_mat)) indeg_mat[, 1] else indeg_mat + outdegree <- if (is.matrix(outdeg_mat)) outdeg_mat[, 1] else outdeg_mat + } else { # last + indegree <- if (is.matrix(indeg_mat)) indeg_mat[, ncol(indeg_mat)] else indeg_mat + outdegree <- if (is.matrix(outdeg_mat)) outdeg_mat[, ncol(outdeg_mat)] else outdeg_mat } } @@ -350,11 +335,7 @@ analyze_multi_behaviors_separately <- function(degrees, toa, min_adopters, boots } # Determine if undirected - undirected <- if (inherits(graph, "diffnet")) { - is_undirected(graph) - } else { - check_undirected_graph(graph) - } + undirected <- check_undirected_graph(graph) structure(list( correlations = correlations_matrix, @@ -521,6 +502,10 @@ create_empty_result <- function(degree_strategy, original_call, combine, sample_ } check_undirected_graph <- function(graph) { + # If the input is a diffnet, we extract its raw list of matrices + if (inherits(graph, "diffnet")) { + graph <- graph$graph + } if (is.list(graph)) { return(all(sapply(graph, function(g) isSymmetric(as.matrix(g))))) } @@ -749,7 +734,7 @@ 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) - + ci_low <- if (length(ci) >= 1) ci[1] else NA_real_ ci_high <- if (length(ci) >= 2) ci[2] else NA_real_ From 52b89285e948ee16232e9361b90ebca3bd048dbe Mon Sep 17 00:00:00 2001 From: aoliveram Date: Thu, 16 Apr 2026 14:46:08 -0700 Subject: [PATCH 10/18] data: Make epigamesDiffNet non-cumulative and weighted --- data-raw/epigamesDiffNet.R | 4 ++-- data/epigamesDiffNet.rda | Bin 133580 -> 53120 bytes 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/data-raw/epigamesDiffNet.R b/data-raw/epigamesDiffNet.R index 42eb05e..0637ad2 100644 --- a/data-raw/epigamesDiffNet.R +++ b/data-raw/epigamesDiffNet.R @@ -25,8 +25,8 @@ daily_edgelist <- collapse_timeframes( timevar = "time", weightvar = "weight", window_size = WINDOW_SIZE, - binarize = TRUE, - cumulative = TRUE, + binarize = FALSE, + cumulative = FALSE, symmetric = TRUE ) diff --git a/data/epigamesDiffNet.rda b/data/epigamesDiffNet.rda index fdcce0f7687207724b67054b64e47e78c5e00fcc..8e8e0d30d666b71fb01846e5dab97d00a9785dbe 100644 GIT binary patch literal 53120 zcmV(rK<>Z&H+ooF0004LBHlIv03iV!0000G&sfasUqa7AT>vQ&2UKVgRpfklJ z?~=1r{FOY~+<}5ou84AF_6UWlEj4Dq3>4h~L0BWSGz@o&w#@;LSzdh-)+jg0sM)18imyJf=kTM zvbGBzbp(d>RKjo5m4)H?!W>0%2O(Ph=EkHKxpgb*&z50?ac0r(8!F*&!#0}_`T0F; zsD;Gtu}1vRcZ}t~eB7$=uu9zr&(k>s1?dfq%i`)T5U$Ra6%rYjW2)V?o1LKC#zYe+^n-i;#8YhX_`Hh+ZTh)=^5| z;$O)(v(Ik{sY?u)lrQU8I*8GrdUb2hLW}PRu0D+fL!jY&)~J4MjP-MhJkY#quLgpi zPHRSg!x$4K+~&OCO+5&g2az-@>2+Ut1VzJx@C1>Ft^CB}MBO2jYx_ zM+?D5#<<*0|Fs|v+rO6fw`4TPR{3K)*^zPLNx0)df5EF=MoF2pN|$JTK+!{x`6dQO 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z%!-9WA9YE&I9^+P(9_?8k*DPPWpw@F9tmUi+Z(f=DMMhG2f4y z#tl9IA4X%<|ftTBLWB|pt>5xFzlnG+` z(32W{HJ7Y*3cOq#@~;u5iDnsI4>Uz;bjbm!`tT1U@!JD6 zojoy>xN*JPsmIV8Yh-)IPrWEcz1~U* zFZV#5=|-SruvR>+Yoq7NH2_*KN4**o1WKMkgG;AA7*$AC$?E~l4VRHq5=I*yZ`K`k z7!N?sKd$AEYU8RC+SWwjaj{3XO=$j1Yo6xHh60X(^&+8(X z=gv_S;1A=0Kyrd)Jhrxopf#QD&{Na+0tjuxVVz|p3%KT26%j0C5sfv{o)~{F$2To4 z3~4oMc)sbMQtlOI#V`fzgO!SyZsIkgGuZdvCB4PLgtljl{s;09SUWR$d7LLIJ|j6Y zqf}BBGQc!i^4keJeWhS6l_>e~j@IcCAYT6?Gb~!cV#Iqz#N^} zp@jdXYmG0iz?wfxag?GRMZ;4vY(Q_AUBxGV4T=%vj6!J|i{WBvFBs7`?&HaIis#|J zdaQNFo1ETp=;h!O?K59iAHk>~)x6+ldLMBR^+Z;3zJsl{VA?pK!h5s1%oU=$cP7@z zxkV#MObAr;u+D&rOd5_}hsYfa7(;iIAK1-4n~fXyGTH%Xd8uoq!na9``&8xGW~}~^ zL&cQ(i|LT26iJK7HQ;$O_pwYHG%FqEamWQoHu-TA1wt8tc!LJ%J!wkX!5&8IWu}_1 zF=VWmslgZLQu?rApRQH;1jOhmr@!UFF_cJgpaTGf3{@Ev%AmPbh)Bq%#@u9W`GLq) qK?yD?4F~|87JVcD0i=@%(9)~{0002leM`fe<555Z000000a;q^S1WS> From 5319ff6d4cbe4d364adf18d1125333b3b68a84b7 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Thu, 16 Apr 2026 14:46:15 -0700 Subject: [PATCH 11/18] docs: Update epigamesDiffNet docs with cumulative reconstruction steps --- R/data.r | 15 +++++++++++++++ man/epigamesDiffNet.Rd | 15 +++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/R/data.r b/R/data.r index 4ed3b83..9fe59d1 100644 --- a/R/data.r +++ b/R/data.r @@ -980,6 +980,21 @@ NULL # "epigames" #' A directed dynamic graph with 594 vertices and 15 time periods. The attributes #' in the graph are described in \code{\link{epigames}}. #' +#' By default, this \code{diffnet} object is **non-cumulative** (each slice represents +#' ephemeral daily contacts) and **valued** (edge weights represent contact duration in seconds). +#' +#' To reconstruct the classic cumulative/binarized network, you can run: +#' +#' \preformatted{ +#' epigames_cumul <- epigamesDiffNet +#' +#' # 1. Accumulate the history across time periods +#' epigames_cumul$graph <- Reduce("+", epigames_cumul$graph, accumulate = TRUE) +#' +#' # 2. Apply a logical cut-off to binarize the network +#' epigames_cumul$graph <- lapply(epigames_cumul$graph, function(m) { m@x[] <- 1; m }) +#' } +#' #' Non-adopters have \code{toa = NA}. #' #' @format A \code{\link{diffnet}} class object. diff --git a/man/epigamesDiffNet.Rd b/man/epigamesDiffNet.Rd index f633835..6d36932 100644 --- a/man/epigamesDiffNet.Rd +++ b/man/epigamesDiffNet.Rd @@ -14,6 +14,21 @@ A directed dynamic graph with 594 vertices and 15 time periods. The attributes in the graph are described in \code{\link{epigames}}. } \details{ +By default, this \code{diffnet} object is **non-cumulative** (each slice represents +ephemeral daily contacts) and **valued** (edge weights represent contact duration in seconds). + +To reconstruct the classic cumulative/binarized network, you can run: + +\preformatted{ +epigames_cumul <- epigamesDiffNet + +# 1. Accumulate the history across time periods +epigames_cumul$graph <- Reduce("+", epigames_cumul$graph, accumulate = TRUE) + +# 2. Apply a logical cut-off to binarize the network +epigames_cumul$graph <- lapply(epigames_cumul$graph, function(m) { m@x[] <- 1; m }) +} + Non-adopters have \code{toa = NA}. } \seealso{ From ddaec9e69ca3abb6ba8e3e37d48d06c0756c91fc Mon Sep 17 00:00:00 2001 From: aoliveram Date: Fri, 17 Apr 2026 14:02:17 -0700 Subject: [PATCH 12/18] feat(diffnet): add $tod slot and $transmission slot (M1, #78) - new_diffnet() gains a -tod- argument (single-behavior vector) with validation (element-wise tod > toa, NA where toa is NA, length match). - When -tod- is supplied, cumadopt is reconstructed from the intervals [toa, tod - 1] via cumadopt_from_intervals() in R/adjmat.r. - New $transmission slot plus exported helpers as_transmission_tree() and get_transmissions() (R/transmission.R) storing the directed infection forest as a data.frame with columns date, source, target, source_exposure_date, virus_id, virus. Docs cite Lloyd-Smith et al. (2005) and White & Pagano (2008). - Tests cover interval reconstruction, validation errors, coercion, and the transmission slot round-trip. No new package dependencies. --- DESCRIPTION | 1 + NAMESPACE | 2 + R/adjmat.r | 23 +++++ R/diffnet-class.r | 53 ++++++++++- R/transmission.R | 137 +++++++++++++++++++++++++++++ man/as_transmission_tree.Rd | 51 +++++++++++ man/diffnet-class.Rd | 11 ++- man/get_transmissions.Rd | 22 +++++ tests/testthat/test-tod-slot.R | 82 +++++++++++++++++ tests/testthat/test-transmission.R | 93 ++++++++++++++++++++ 10 files changed, 473 insertions(+), 2 deletions(-) create mode 100644 R/transmission.R create mode 100644 man/as_transmission_tree.Rd create mode 100644 man/get_transmissions.Rd create mode 100644 tests/testthat/test-tod-slot.R create mode 100644 tests/testthat/test-transmission.R diff --git a/DESCRIPTION b/DESCRIPTION index 5c76d87..409aae7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -94,3 +94,4 @@ Collate: 'struct_equiv.R' 'struct_test.R' 'survey_to_diffnet.R' + 'transmission.R' diff --git a/NAMESPACE b/NAMESPACE index 4a6bdac..9e76d75 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -91,6 +91,7 @@ export(as.dgCMatrix) export(as_dgCMatrix) export(as_diffnet) export(as_spmat) +export(as_transmission_tree) export(bass_F) export(bass_dF) export(bass_f) @@ -123,6 +124,7 @@ export(ego_variance) export(egonet_attrs) export(exposure) export(fitbass) +export(get_transmissions) export(graph_power) export(grid_distribution) export(hazard_rate) diff --git a/R/adjmat.r b/R/adjmat.r index ca5bc83..cdd5847 100644 --- a/R/adjmat.r +++ b/R/adjmat.r @@ -537,6 +537,29 @@ toa_mat.default <- function(per, t0, t1) { ) } +# Build (adopt, cumadopt) from (toa, tod) intervals for a single behavior. +# Each node i is considered adopted on periods [toa[i], tod[i] - 1]; when +# tod[i] is NA the adoption is absorbing and the interval runs through t1. +cumadopt_from_intervals <- function(toa, tod, t0, t1, labels = NULL) { + n <- length(toa) + T <- t1 - t0 + 1L + adopt <- matrix(0L, nrow = n, ncol = T) + cumadopt <- matrix(0L, nrow = n, ncol = T) + for (i in seq_len(n)) { + s_val <- toa[i] + if (is.na(s_val)) next + s <- as.integer(s_val) - t0 + 1L + e_val <- tod[i] + e <- if (is.na(e_val)) T else (as.integer(e_val) - 1L - t0 + 1L) + if (s >= 1L && s <= T) adopt[i, s] <- 1L + if (s <= e && s >= 1L && e <= T && e >= 1L) cumadopt[i, s:e] <- 1L + } + rn <- if (length(labels)) labels else seq_len(n) + dimnames(adopt) <- list(rn, t0:t1) + dimnames(cumadopt) <- list(rn, t0:t1) + list(adopt = adopt, cumadopt = cumadopt) +} + # @rdname toa_mat # @export toa_mat.numeric <- function(times, labels=NULL, diff --git a/R/diffnet-class.r b/R/diffnet-class.r index 35dc67b..867ab1a 100644 --- a/R/diffnet-class.r +++ b/R/diffnet-class.r @@ -356,6 +356,13 @@ check_as_diffnet_attrs <- function( #' @param as.df Logical scalar. When TRUE returns a data.frame. #' @param name Character scalar. Name of the diffusion network (descriptive). #' @param behavior Character vector. Name of the behavior(s) been analyzed (innovation). +#' @param tod Optional numeric vector of size \eqn{n}. Times of disadoption +#' (e.g. recovery, removal). When supplied, each non-\code{NA} entry must be +#' strictly greater than the corresponding \code{toa} and \code{NA} whenever +#' \code{toa} is \code{NA}. Node \eqn{i} is considered adopted over the closed +#' interval \eqn{[\mathrm{toa}_i, \mathrm{tod}_i - 1]}; \code{NA} in \code{tod} +#' means the adoption is absorbing. Currently only single-behavior (vector) +#' \code{tod} is supported. #' #' @seealso Default options are listed at \code{\link{netdiffuseR-options}} #' @details @@ -572,7 +579,8 @@ new_diffnet <- function( self = getOption("diffnet.self"), multiple = getOption("diffnet.multiple"), name = "Diffusion Network", - behavior = NULL + behavior = NULL, + tod = NULL ) { # Step 0.0: Check if its diffnet! -------------------------------------------- @@ -639,9 +647,50 @@ new_diffnet <- function( } } + # Step 2.3: Validating -tod- (time of disadoption), if provided ------------- + if (length(tod)) { + + if (num_of_behaviors > 1L) + stop("Multi-behavior -tod- is not supported yet. Provide a vector -tod- ", + "with a single behavior, or leave -tod- as NULL.") + + if (!is.null(dim(tod))) + stop("-tod- must be a vector of the same length as -toa-.") + + if (length(tod) != length(toa)) + stop("-tod- and -toa- have different lengths (", length(tod), " and ", + length(toa), " respectively).") + + if (!is.integer(tod)) { + warning("Coercing -tod- into integer.") + tod_names <- names(tod) + tod <- as.integer(tod) + names(tod) <- tod_names + } + + has_both <- !is.na(toa) & !is.na(tod) + if (any(tod[has_both] <= toa[has_both])) + stop("-tod- must be strictly greater than -toa- for every node with ", + "both values defined (an adopter must remain so for at least one ", + "period before disadopting).") + + if (any(!is.na(tod) & is.na(toa))) + stop("-tod- is defined for some nodes that never adopted (NA in -toa-). ", + "-tod- entries must be NA wherever -toa- is NA.") + + if (!length(names(tod))) names(tod) <- names(toa) + } + # Step 3.1: Creating Time of adoption matrix --------------------------------- mat <- toa_mat(toa, labels = meta$ids, t0=t0, t1=t1) + # Step 3.1b: Rebuilding cumadopt using [toa, tod-1] intervals if -tod- set + if (length(tod) && num_of_behaviors == 1L) { + intervals <- cumadopt_from_intervals(toa, tod, t0 = t0, t1 = t1, + labels = meta$ids) + mat$cumadopt <- intervals$cumadopt + } + # Step 3.2: Verifying dimensions and fixing meta$pers if (num_of_behaviors==1) { @@ -775,12 +824,14 @@ new_diffnet <- function( list( graph = graph, toa = toa, + tod = tod, adopt = adopt, cumadopt = cumadopt, # Attributes vertex.static.attrs = vertex.static.attrs, vertex.dyn.attrs = vertex.dyn.attrs, graph.attrs = graph.attrs, + transmission = NULL, meta = meta ), class="diffnet" diff --git a/R/transmission.R b/R/transmission.R new file mode 100644 index 0000000..95f4765 --- /dev/null +++ b/R/transmission.R @@ -0,0 +1,137 @@ +# Transmission tree handling for diffnet objects. +# +# The $transmission slot is a list with the following elements: +# - tree: data.frame with columns +# date integer, period when the transmission happened +# source integer, row index of the infector in x (NA for seeds) +# target integer, row index of the infectee in x +# source_exposure_date integer, period when `source` was infected (NA for seeds) +# virus_id integer, optional virus identifier +# virus character, optional virus label +# - pars: list, free-form parameters/metadata associated with the tree. +# Each row represents one infection event (an edge in the transmission tree); +# the set of (source -> target) pairs forms the directed forest from which +# offspring distributions (Lloyd-Smith et al., 2005) and likelihood-based +# reproduction-number estimates (White & Pagano, 2008) are derived. + +.transmission_cols <- c( + "date", "source", "target", "source_exposure_date", "virus_id", "virus" +) + +.empty_transmission_tree <- function() { + data.frame( + date = integer(0), + source = integer(0), + target = integer(0), + source_exposure_date = integer(0), + virus_id = integer(0), + virus = character(0), + stringsAsFactors = FALSE + ) +} + +#' Attach a transmission tree to a \code{diffnet} object +#' +#' Populates the \code{$transmission} slot of a \code{diffnet} with a +#' transmission tree (who-infected-whom). The resulting directed forest is the +#' canonical input to offspring-distribution analyses +#' (Lloyd-Smith \emph{et al.}, 2005) and to likelihood-based estimators of the +#' reproduction number and serial interval (White & Pagano, 2008). +#' +#' @param x A \code{diffnet} object. +#' @param tree A \code{data.frame} with at least the columns \code{date}, +#' \code{source}, \code{target}, and \code{source_exposure_date}. Columns +#' \code{virus_id} and \code{virus} are optional. \code{source} and +#' \code{source_exposure_date} may be \code{NA} for seed infections (roots +#' of the tree). +#' @param pars Optional named list stored verbatim in \code{x$transmission$pars}. +#' Useful for recording kernel parameters, seeds, etc. +#' +#' @details +#' Each row of \code{tree} represents one infection event (an edge +#' \eqn{\text{source} \to \text{target}} in the transmission tree) time-stamped +#' by \code{date}. \code{source} and \code{target} must be integer row indices +#' into \code{x} (\code{1..nnodes(x)}); \code{target} is required for every +#' row. Existing \code{$transmission} content is overwritten. +#' +#' @return The \code{diffnet} object \code{x} with \code{$transmission} set to +#' a list with components \code{tree} (a clean, ordered \code{data.frame}) +#' and \code{pars}. +#' +#' @references +#' Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005). +#' Superspreading and the effect of individual variation on disease emergence. +#' \emph{Nature} 438:355-359. \doi{10.1038/nature04153} +#' +#' White, L. F., & Pagano, M. (2008). A likelihood-based method for real-time +#' estimation of the serial interval and reproductive number of an epidemic. +#' \emph{Statistics in Medicine} 27:2999-3016. \doi{10.1002/sim.3136} +#' +#' @export +#' @seealso \code{\link{new_diffnet}} +as_transmission_tree <- function(x, tree, pars = list()) { + + if (!inherits(x, "diffnet")) + stop("-x- must be a diffnet object.") + + if (!is.data.frame(tree)) + stop("-tree- must be a data.frame.") + + required <- c("date", "source", "target", "source_exposure_date") + missing_cols <- setdiff(required, names(tree)) + if (length(missing_cols)) + stop("-tree- is missing required column(s): ", + paste(missing_cols, collapse = ", "), ".") + + if (anyNA(tree$target)) + stop("-tree$target- cannot contain NA values.") + + n <- nnodes(x) + tgt <- suppressWarnings(as.integer(tree$target)) + if (anyNA(tgt) || any(tgt < 1L) || any(tgt > n)) + stop("-tree$target- must be integer indices in 1..", n, ".") + + src <- suppressWarnings(as.integer(tree$source)) + src_ok <- src[!is.na(src)] + if (length(src_ok) && (any(src_ok < 1L) || any(src_ok > n))) + stop("-tree$source- must be NA or an integer index in 1..", n, ".") + + out <- data.frame( + date = as.integer(tree$date), + source = src, + target = tgt, + source_exposure_date = as.integer(tree$source_exposure_date), + stringsAsFactors = FALSE + ) + + out$virus_id <- if (!is.null(tree$virus_id)) + as.integer(tree$virus_id) else rep(1L, nrow(out)) + + out$virus <- if (!is.null(tree$virus)) + as.character(tree$virus) else rep(NA_character_, nrow(out)) + + out <- out[, .transmission_cols, drop = FALSE] + out <- out[order(out$date, out$target), , drop = FALSE] + rownames(out) <- NULL + + x$transmission <- list(tree = out, pars = pars) + x +} + +#' Retrieve the transmission tree of a \code{diffnet} object +#' +#' Returns the data.frame stored in \code{x$transmission$tree}. If none has +#' been attached, an empty data.frame with the standard columns is returned. +#' +#' @param x A \code{diffnet} object. +#' @return A \code{data.frame} with columns \code{date}, \code{source}, +#' \code{target}, \code{source_exposure_date}, \code{virus_id}, \code{virus}. +#' @export +#' @seealso \code{\link{as_transmission_tree}} +get_transmissions <- function(x) { + if (!inherits(x, "diffnet")) + stop("-x- must be a diffnet object.") + + tr <- x$transmission$tree + if (is.null(tr)) .empty_transmission_tree() else tr +} diff --git a/man/as_transmission_tree.Rd b/man/as_transmission_tree.Rd new file mode 100644 index 0000000..a73a38c --- /dev/null +++ b/man/as_transmission_tree.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/transmission.R +\name{as_transmission_tree} +\alias{as_transmission_tree} +\title{Attach a transmission tree to a \code{diffnet} object} +\usage{ +as_transmission_tree(x, tree, pars = list()) +} +\arguments{ +\item{x}{A \code{diffnet} object.} + +\item{tree}{A \code{data.frame} with at least the columns \code{date}, +\code{source}, \code{target}, and \code{source_exposure_date}. Columns +\code{virus_id} and \code{virus} are optional. \code{source} and +\code{source_exposure_date} may be \code{NA} for seed infections (roots +of the tree).} + +\item{pars}{Optional named list stored verbatim in \code{x$transmission$pars}. +Useful for recording kernel parameters, seeds, etc.} +} +\value{ +The \code{diffnet} object \code{x} with \code{$transmission} set to + a list with components \code{tree} (a clean, ordered \code{data.frame}) + and \code{pars}. +} +\description{ +Populates the \code{$transmission} slot of a \code{diffnet} with a +transmission tree (who-infected-whom). The resulting directed forest is the +canonical input to offspring-distribution analyses +(Lloyd-Smith \emph{et al.}, 2005) and to likelihood-based estimators of the +reproduction number and serial interval (White & Pagano, 2008). +} +\details{ +Each row of \code{tree} represents one infection event (an edge +\eqn{\text{source} \to \text{target}} in the transmission tree) time-stamped +by \code{date}. \code{source} and \code{target} must be integer row indices +into \code{x} (\code{1..nnodes(x)}); \code{target} is required for every +row. Existing \code{$transmission} content is overwritten. +} +\references{ +Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005). +Superspreading and the effect of individual variation on disease emergence. +\emph{Nature} 438:355-359. \doi{10.1038/nature04153} + +White, L. F., & Pagano, M. (2008). A likelihood-based method for real-time +estimation of the serial interval and reproductive number of an epidemic. +\emph{Statistics in Medicine} 27:2999-3016. \doi{10.1002/sim.3136} +} +\seealso{ +\code{\link{new_diffnet}} +} diff --git a/man/diffnet-class.Rd b/man/diffnet-class.Rd index f2532b4..b825d54 100644 --- a/man/diffnet-class.Rd +++ b/man/diffnet-class.Rd @@ -52,7 +52,8 @@ new_diffnet( self = getOption("diffnet.self"), multiple = getOption("diffnet.multiple"), name = "Diffusion Network", - behavior = NULL + behavior = NULL, + tod = NULL ) \method{as.data.frame}{diffnet}( @@ -148,6 +149,14 @@ order of the rows in the attribute data.} \item{behavior}{Character vector. Name of the behavior(s) been analyzed (innovation).} +\item{tod}{Optional numeric vector of size \eqn{n}. Times of disadoption +(e.g. recovery, removal). When supplied, each non-\code{NA} entry must be +strictly greater than the corresponding \code{toa} and \code{NA} whenever +\code{toa} is \code{NA}. Node \eqn{i} is considered adopted over the closed +interval \eqn{[\mathrm{toa}_i, \mathrm{tod}_i - 1]}; \code{NA} in \code{tod} +means the adoption is absorbing. Currently only single-behavior (vector) +\code{tod} is supported.} + \item{x}{A \code{diffnet} object.} \item{row.names}{Ignored.} diff --git a/man/get_transmissions.Rd b/man/get_transmissions.Rd new file mode 100644 index 0000000..fd98b4d --- /dev/null +++ b/man/get_transmissions.Rd @@ -0,0 +1,22 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/transmission.R +\name{get_transmissions} +\alias{get_transmissions} +\title{Retrieve the transmission tree of a \code{diffnet} object} +\usage{ +get_transmissions(x) +} +\arguments{ +\item{x}{A \code{diffnet} object.} +} +\value{ +A \code{data.frame} with columns \code{date}, \code{source}, + \code{target}, \code{source_exposure_date}, \code{virus_id}, \code{virus}. +} +\description{ +Returns the data.frame stored in \code{x$transmission$tree}. If none has +been attached, an empty data.frame with the standard columns is returned. +} +\seealso{ +\code{\link{as_transmission_tree}} +} diff --git a/tests/testthat/test-tod-slot.R b/tests/testthat/test-tod-slot.R new file mode 100644 index 0000000..b275e27 --- /dev/null +++ b/tests/testthat/test-tod-slot.R @@ -0,0 +1,82 @@ +context("Time of disadoption (tod) slot") + +mk_graph <- function(n = 5L, T = 5L, seed = 1L) { + set.seed(seed) + lapply(seq_len(T), function(x) rgraph_ba(t = n - 1L)) +} + +test_that("Default new_diffnet still has tod = NULL and transmission = NULL", { + gr <- mk_graph() + toa <- c(1L, 2L, NA, 3L, 5L) + x <- new_diffnet(gr, toa, t0 = 1L, t1 = 5L) + + expect_null(x$tod) + expect_null(x$transmission) + expect_true(inherits(x, "diffnet")) +}) + +test_that("tod rebuilds cumadopt using [toa, tod - 1] intervals", { + gr <- mk_graph() + toa <- c(1L, 2L, NA, 3L, 5L) + tod <- c(3L, 4L, NA, NA, NA) + + x <- new_diffnet(gr, toa, tod = tod, t0 = 1L, t1 = 5L) + + expect_identical(x$tod, setNames(tod, as.character(seq_len(5L)))) + + # Node 1: adopts t=1, disadopts t=3 -> cumadopt c(1,1,0,0,0) + expect_equal(as.integer(x$cumadopt[1, ]), c(1L, 1L, 0L, 0L, 0L)) + # Node 2: adopts t=2, disadopts t=4 -> cumadopt c(0,1,1,0,0) + expect_equal(as.integer(x$cumadopt[2, ]), c(0L, 1L, 1L, 0L, 0L)) + # Node 3: never adopts -> all zero + expect_equal(as.integer(x$cumadopt[3, ]), rep(0L, 5L)) + # Node 4: adopts t=3, absorbing -> c(0,0,1,1,1) + expect_equal(as.integer(x$cumadopt[4, ]), c(0L, 0L, 1L, 1L, 1L)) + # Node 5: adopts t=5, absorbing -> c(0,0,0,0,1) + expect_equal(as.integer(x$cumadopt[5, ]), c(0L, 0L, 0L, 0L, 1L)) + + # adopt matrix is preserved (single 1 at the adoption period). + expect_equal(as.integer(rowSums(x$adopt)), c(1L, 1L, 0L, 1L, 1L)) +}) + +test_that("tod validation errors are informative", { + gr <- mk_graph() + toa <- c(1L, 2L, NA, 3L, 5L) + + # tod <= toa somewhere + expect_error( + new_diffnet(gr, toa, tod = c(0L, 2L, NA, NA, NA), t0 = 1L, t1 = 5L), + "strictly greater" + ) + + # length mismatch + expect_error( + new_diffnet(gr, toa, tod = c(3L, 4L, NA, NA, NA, NA), t0 = 1L, t1 = 5L), + "different lengths" + ) + + # tod defined where toa is NA + expect_error( + new_diffnet(gr, toa, tod = c(3L, 4L, 5L, NA, NA), t0 = 1L, t1 = 5L), + "NA wherever" + ) + + # tod must be a vector (matrix not yet supported) + expect_error( + new_diffnet(gr, toa, + tod = matrix(c(3L, 4L, NA, NA, NA), ncol = 1L), + t0 = 1L, t1 = 5L), + "vector of the same length" + ) +}) + +test_that("tod coerces non-integer input with a warning", { + gr <- mk_graph() + toa <- c(1L, 2L, NA, 3L, 5L) + + expect_warning( + x <- new_diffnet(gr, toa, tod = c(3, 4, NA, NA, NA), t0 = 1L, t1 = 5L), + "Coercing -tod-" + ) + expect_true(is.integer(x$tod)) +}) diff --git a/tests/testthat/test-transmission.R b/tests/testthat/test-transmission.R new file mode 100644 index 0000000..8c9d689 --- /dev/null +++ b/tests/testthat/test-transmission.R @@ -0,0 +1,93 @@ +context("Transmission tree slot") + +mk_diffnet <- function() { + set.seed(42) + gr <- lapply(1:5, function(x) rgraph_ba(t = 4L)) + toa <- c(1L, 2L, NA, 3L, 5L) + new_diffnet(gr, toa, t0 = 1L, t1 = 5L) +} + +test_that("get_transmissions() returns an empty data.frame when unset", { + x <- mk_diffnet() + tr <- get_transmissions(x) + expect_s3_class(tr, "data.frame") + expect_equal(nrow(tr), 0L) + expect_setequal( + names(tr), + c("date", "source", "target", "source_exposure_date", "virus_id", "virus") + ) +}) + +test_that("as_transmission_tree() validates required columns and ranges", { + x <- mk_diffnet() + + expect_error( + as_transmission_tree(x, data.frame(date = 1L, source = NA, target = 1L)), + "missing required column" + ) + + expect_error( + as_transmission_tree(x, data.frame( + date = 1L, source = NA_integer_, target = NA_integer_, + source_exposure_date = NA_integer_ + )), + "cannot contain NA" + ) + + expect_error( + as_transmission_tree(x, data.frame( + date = 1L, source = NA_integer_, target = 999L, + source_exposure_date = NA_integer_ + )), + "integer indices" + ) + + expect_error( + as_transmission_tree(x, data.frame( + date = 1L, source = 42L, target = 2L, source_exposure_date = 1L + )), + "NA or an integer index" + ) +}) + +test_that("as_transmission_tree() stores a clean tree and optional pars", { + x <- mk_diffnet() + tree <- data.frame( + date = c(3L, 1L, 2L), + source = c(2L, NA, 1L), + target = c(4L, 1L, 2L), + source_exposure_date = c(2L, NA, 1L), + virus_id = c(1L, 1L, 1L), + virus = c("flu", "flu", "flu"), + stringsAsFactors = FALSE + ) + y <- as_transmission_tree(x, tree, pars = list(kernel = "wells-riley")) + tr <- get_transmissions(y) + + # Ordered by (date, target) and clean rownames + expect_equal(tr$date, c(1L, 2L, 3L)) + expect_equal(tr$target, c(1L, 2L, 4L)) + expect_equal(rownames(tr), as.character(seq_len(nrow(tr)))) + + expect_equal(y$transmission$pars$kernel, "wells-riley") +}) + +test_that("Missing optional columns are defaulted", { + x <- mk_diffnet() + tree <- data.frame( + date = c(1L, 2L), + source = c(NA_integer_, 1L), + target = c(1L, 2L), + source_exposure_date = c(NA_integer_, 1L) + ) + y <- as_transmission_tree(x, tree) + tr <- get_transmissions(y) + + expect_true(all(tr$virus_id == 1L)) + expect_true(all(is.na(tr$virus))) +}) + +test_that("get_transmissions() requires a diffnet", { + expect_error(get_transmissions(42), "must be a diffnet") + expect_error(as_transmission_tree(42, data.frame()), "must be a diffnet") +}) From a8088c80f43437987c16a163fcd466d1ca4f85d7 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Fri, 17 Apr 2026 17:13:15 -0700 Subject: [PATCH 13/18] rename get_transmissions() -> transmission_tree() (M1 follow-up, #78) Avoid masking epiworldR::get_transmissions() when both packages are loaded. No behavior change; exported name and docs renamed, tests updated accordingly. Co-Authored-By: Claude Opus 4.7 --- NAMESPACE | 2 +- R/transmission.R | 2 +- man/{get_transmissions.Rd => transmission_tree.Rd} | 6 +++--- tests/testthat/test-transmission.R | 12 ++++++------ 4 files changed, 11 insertions(+), 11 deletions(-) rename man/{get_transmissions.Rd => transmission_tree.Rd} (88%) diff --git a/NAMESPACE b/NAMESPACE index 9e76d75..1660f50 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -124,7 +124,6 @@ export(ego_variance) export(egonet_attrs) export(exposure) export(fitbass) -export(get_transmissions) export(graph_power) export(grid_distribution) export(hazard_rate) @@ -188,6 +187,7 @@ export(threshold) export(toa_diff) export(toa_mat) export(transformGraphBy) +export(transmission_tree) export(vertex_covariate_compare) export(vertex_covariate_dist) export(vertex_mahalanobis_dist) diff --git a/R/transmission.R b/R/transmission.R index 95f4765..a450778 100644 --- a/R/transmission.R +++ b/R/transmission.R @@ -128,7 +128,7 @@ as_transmission_tree <- function(x, tree, pars = list()) { #' \code{target}, \code{source_exposure_date}, \code{virus_id}, \code{virus}. #' @export #' @seealso \code{\link{as_transmission_tree}} -get_transmissions <- function(x) { +transmission_tree <- function(x) { if (!inherits(x, "diffnet")) stop("-x- must be a diffnet object.") diff --git a/man/get_transmissions.Rd b/man/transmission_tree.Rd similarity index 88% rename from man/get_transmissions.Rd rename to man/transmission_tree.Rd index fd98b4d..10c065f 100644 --- a/man/get_transmissions.Rd +++ b/man/transmission_tree.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/transmission.R -\name{get_transmissions} -\alias{get_transmissions} +\name{transmission_tree} +\alias{transmission_tree} \title{Retrieve the transmission tree of a \code{diffnet} object} \usage{ -get_transmissions(x) +transmission_tree(x) } \arguments{ \item{x}{A \code{diffnet} object.} diff --git a/tests/testthat/test-transmission.R b/tests/testthat/test-transmission.R index 8c9d689..305002c 100644 --- a/tests/testthat/test-transmission.R +++ b/tests/testthat/test-transmission.R @@ -7,9 +7,9 @@ mk_diffnet <- function() { new_diffnet(gr, toa, t0 = 1L, t1 = 5L) } -test_that("get_transmissions() returns an empty data.frame when unset", { +test_that("transmission_tree() returns an empty data.frame when unset", { x <- mk_diffnet() - tr <- get_transmissions(x) + tr <- transmission_tree(x) expect_s3_class(tr, "data.frame") expect_equal(nrow(tr), 0L) expect_setequal( @@ -62,7 +62,7 @@ test_that("as_transmission_tree() stores a clean tree and optional pars", { stringsAsFactors = FALSE ) y <- as_transmission_tree(x, tree, pars = list(kernel = "wells-riley")) - tr <- get_transmissions(y) + tr <- transmission_tree(y) # Ordered by (date, target) and clean rownames expect_equal(tr$date, c(1L, 2L, 3L)) @@ -81,13 +81,13 @@ test_that("Missing optional columns are defaulted", { source_exposure_date = c(NA_integer_, 1L) ) y <- as_transmission_tree(x, tree) - tr <- get_transmissions(y) + tr <- transmission_tree(y) expect_true(all(tr$virus_id == 1L)) expect_true(all(is.na(tr$virus))) }) -test_that("get_transmissions() requires a diffnet", { - expect_error(get_transmissions(42), "must be a diffnet") +test_that("transmission_tree() requires a diffnet", { + expect_error(transmission_tree(42), "must be a diffnet") expect_error(as_transmission_tree(42, data.frame()), "must be a diffnet") }) From a2cd6396fc00f9e8839ce20a6641b5af3a90dc91 Mon Sep 17 00:00:00 2001 From: aoliveram Date: Fri, 17 Apr 2026 17:13:31 -0700 Subject: [PATCH 14/18] feat(exposure): pluggable link_fun kernel (M2, #78) Adds link_fun + link_pars arguments to exposure(). Supported named kernels: identity (default), linear (min(beta*w, 1)), sigmoid (plogis((w-h)/scale)), wells-riley (1 - exp(-beta*w)). Custom user functions are accepted with either signature function(w) or function(w, pars). Non-identity kernels force valued = TRUE with a warning. Default behavior unchanged; 639 pre-existing tests still pass. Co-Authored-By: Claude Opus 4.7 --- R/stats.R | 113 +++++++++++-- man/exposure.Rd | 28 +++- tests/testthat/test-exposure-link-fun.R | 200 ++++++++++++++++++++++++ 3 files changed, 327 insertions(+), 14 deletions(-) create mode 100644 tests/testthat/test-exposure-link-fun.R diff --git a/R/stats.R b/R/stats.R index b79ddd4..586f68b 100644 --- a/R/stats.R +++ b/R/stats.R @@ -276,6 +276,21 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' @param lags Integer scalar. When different from 0, the resulting exposure #' matrix will be the lagged exposure as specified (see examples). #' @param mode Character scalar. Either "deterministic" (default) or "stochastic". +#' @param link_fun Character scalar or function. Kernel applied to the +#' (valued) edge weights before exposure is computed. Supported names: +#' \code{"identity"} (default, no transformation), \code{"linear"} +#' (\eqn{\min(\beta w, 1)}), \code{"sigmoid"} +#' (\eqn{\mathrm{plogis}((w - h)/\mathrm{scale})}), and \code{"wells-riley"} +#' (\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied function +#' with either signature \code{function(w)} (parameters baked into a +#' closure) or \code{function(w, pars)} (parameters passed through +#' \code{link_pars}); it must return a vector of the same length as +#' \code{w}. When \code{link_fun} is not \code{"identity"}, +#' \code{valued} is forced to \code{TRUE} (with a warning if the user set +#' it to \code{FALSE}). +#' @param link_pars Named list with the scalar parameters required by +#' \code{link_fun}. See the section "Link / kernel functions" for the +#' required names per kernel. #' @details #' Exposure is calculated as follows: #' @@ -514,8 +529,60 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' @name exposure NULL +# Link / kernel function applied to edge weights before exposure is computed. +# `link_fun` may be one of the named kernels listed below or a user-supplied +# function. User functions may have either signature: +# - `function(w)` — parameters baked into a closure, +# - `function(w, pars)` — parameters passed via `link_pars`. +# Either way, the function must return a vector of the same length as `w`, +# where `w` is the non-zero entries of a dgCMatrix (`@x`). +.apply_link_kernel <- function(W, link_fun, link_pars) { + if (is.null(link_fun) || + (is.character(link_fun) && identical(link_fun, "identity"))) + return(W) + + if (is.function(link_fun)) { + fmls <- formals(link_fun) + single_arg <- !is.null(fmls) && length(fmls) == 1L && + !("..." %in% names(fmls)) + new_x <- if (single_arg) link_fun(W@x) else link_fun(W@x, link_pars) + if (length(new_x) != length(W@x)) + stop("Custom -link_fun- must return a vector of the same length as ", + "its input (the non-zero edge weights).") + W@x <- as.numeric(new_x) + return(W) + } + + if (!is.character(link_fun) || length(link_fun) != 1L) + stop("-link_fun- must be NULL, a character scalar, or a function.") + + W@x <- switch( + link_fun, + "linear" = { + if (is.null(link_pars$beta)) + stop("link_fun = \"linear\" requires link_pars$beta.") + pmin(link_pars$beta * W@x, 1) + }, + "sigmoid" = { + if (is.null(link_pars$h) || is.null(link_pars$scale)) + stop("link_fun = \"sigmoid\" requires link_pars$h and link_pars$scale.") + stats::plogis((W@x - link_pars$h) / link_pars$scale) + }, + "wells-riley" = { + if (is.null(link_pars$beta)) + stop("link_fun = \"wells-riley\" requires link_pars$beta.") + 1 - exp(-link_pars$beta * W@x) + }, + stop("Unknown link_fun: ", link_fun, + ". Supported: identity, linear, sigmoid, wells-riley, or a function.") + ) + W +} + # Workhorse of exposure plotting -.exposure <- function(graph, cumadopt, attrs, outgoing, valued, normalized, self, mode = "deterministic") { +.exposure <- function(graph, cumadopt, attrs, outgoing, valued, normalized, self, + mode = "deterministic", + link_fun = "identity", link_pars = list()) { # Getting the parameters n <- nrow(graph) @@ -533,6 +600,9 @@ NULL # Checking self if (!self) graph <- sp_diag(graph, rep(0, nnodes(graph))) + # Apply link / kernel function to edge weights (pre stochastic / normalization) + graph <- .apply_link_kernel(graph, link_fun, link_pars) + # Calculate normalization and apply stochastic filter if (mode == "stochastic") { # Denominator: Based on Degree (Potential connections as binary) @@ -610,9 +680,21 @@ exposure <- function( self = getOption("diffnet.self"), lags = 0L, mode = "deterministic", + link_fun = "identity", + link_pars = list(), ... ) { + # When a non-identity link kernel is requested, edge weights become the + # kernel input and must be preserved (binarizing would collapse them). + is_identity_link <- (is.null(link_fun)) || + (is.character(link_fun) && identical(link_fun, "identity")) + if (!is_identity_link && !valued) { + warning("-link_fun- different from \"identity\" requires valued edges; ", + "forcing -valued- to TRUE.") + valued <- TRUE + } + # Checking diffnet attributes if (length(attrs) == 1 && inherits(attrs, "character")) { if (!inherits(graph, "diffnet")) @@ -730,7 +812,8 @@ exposure <- function( if ((is.array(graph) & !inherits(graph, "matrix")) | is.list(graph)) { exposure.list(as_spmat(graph), cumadopt, attrs, outgoing, valued, normalized, - self, lags, mode = mode) + self, lags, mode = mode, + link_fun = link_fun, link_pars = link_pars) } else stopifnot_graph(graph) } @@ -738,7 +821,8 @@ exposure <- function( # @export exposure.list <- function( graph, cumadopt, attrs, - outgoing, valued, normalized, self, lags, mode = "deterministic") { + outgoing, valued, normalized, self, lags, mode = "deterministic", + link_fun = "identity", link_pars = list()) { # attrs can be either # degree, indegree, outdegree, or a user defined vector. @@ -758,7 +842,8 @@ exposure.list <- function( add_dimnames.mat(attrs) output <- exposure_for(graph, cumadopt, attrs, outgoing, valued, normalized, - self, lags, mode = mode) + self, lags, mode = mode, + link_fun = link_fun, link_pars = link_pars) dimnames(output) <- dimnames(cumadopt) output @@ -774,7 +859,9 @@ exposure_for <- function( normalized, self, lags, - mode = "deterministic" + mode = "deterministic", + link_fun = "identity", + link_pars = list() ) { if (length(dim(cumadopt)) == 3) { @@ -789,7 +876,9 @@ exposure_for <- function( valued = valued, normalized = normalized, self = self, - mode = mode) + mode = mode, + link_fun = link_fun, + link_pars = link_pars) } } else { for (i in (1 - lags):nslices(graph)) { @@ -800,7 +889,9 @@ exposure_for <- function( valued = valued, normalized = normalized, self = self, - mode = mode) + mode = mode, + link_fun = link_fun, + link_pars = link_pars) } } } else { @@ -815,7 +906,9 @@ exposure_for <- function( valued = valued, normalized = normalized, self = self, - mode = mode) + mode = mode, + link_fun = link_fun, + link_pars = link_pars) } } else { for (i in (1 - lags):nslices(graph)) { @@ -826,7 +919,9 @@ exposure_for <- function( valued = valued, normalized = normalized, self = self, - mode = mode) + mode = mode, + link_fun = link_fun, + link_pars = link_pars) } } } diff --git a/man/exposure.Rd b/man/exposure.Rd index f83a3ce..339e98a 100644 --- a/man/exposure.Rd +++ b/man/exposure.Rd @@ -16,6 +16,8 @@ exposure( self = getOption("diffnet.self"), lags = 0L, mode = "deterministic", + link_fun = "identity", + link_pars = list(), ... ) } @@ -50,6 +52,23 @@ matrix will be the lagged exposure as specified (see examples).} \item{mode}{Character scalar. Either "deterministic" (default) or "stochastic".} +\item{link_fun}{Character scalar or function. Kernel applied to the +(valued) edge weights before exposure is computed. Supported names: +\code{"identity"} (default, no transformation), \code{"linear"} +(\eqn{\min(\beta w, 1)}), \code{"sigmoid"} +(\eqn{\mathrm{plogis}((w - h)/\mathrm{scale})}), and \code{"wells-riley"} +(\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied function +with either signature \code{function(w)} (parameters baked into a +closure) or \code{function(w, pars)} (parameters passed through +\code{link_pars}); it must return a vector of the same length as +\code{w}. When \code{link_fun} is not \code{"identity"}, +\code{valued} is forced to \code{TRUE} (with a warning if the user set +it to \code{FALSE}).} + +\item{link_pars}{Named list with the scalar parameters required by +\code{link_fun}. See the section "Link / kernel functions" for the +required names per kernel.} + \item{...}{Further arguments passed to \code{\link{struct_equiv}} (only used when \code{alt.graph="se"}).} } @@ -125,12 +144,11 @@ When \code{mode = "stochastic"}, the exposure is calculated based on a probabili interpretation of the edges. In this mode, the weights of the graph \eqn{S_t} are treated as probabilities of transmission. For each edge \eqn{(i,j)}, a Bernoulli trial is performed with probability \eqn{S_{t,ij}}. If the trial is successful, -the edge is "realized" as a full connection (weight 1). If failed, -the edge is treated as non-existent (weight 0). +the edge is "realized" as a full connection. If failed, the edge is treated +as non-existent. -The denominator (normalization factor) is calculated using the degree of the node -(count of neighbors), regardless of the weights. This represents the total number -of potential contacts. +The denominator is calculated using the degree of the node, representing the total +number of potential contacts. \deqn{ \tilde{E}_{ti} = \frac{\sum_{j \neq i} \mathbb{I}(U_{ij} < S_{t,ij}) a_{tj}}{\sum_{j \neq i} 1} diff --git a/tests/testthat/test-exposure-link-fun.R b/tests/testthat/test-exposure-link-fun.R new file mode 100644 index 0000000..8cdf6dc --- /dev/null +++ b/tests/testthat/test-exposure-link-fun.R @@ -0,0 +1,200 @@ +context("Exposure link / kernel function") +library(netdiffuseR) + +# ---- Helpers -------------------------------------------------------------- +# Build a small valued dynamic graph + cumadopt that we can reuse. +mk_fixture <- function(seed = 71) { + set.seed(seed) + n <- 8L + g <- rgraph_er(n, t = 1, p = 0.5) + g@x <- runif(length(g@x), min = 0.1, max = 2.0) + cumadopt <- matrix(0, nrow = n, ncol = 1) + cumadopt[1:3, 1] <- 1 + list(g = list(g), cumadopt = cumadopt, n = n) +} + +# ---- Identity (default) --------------------------------------------------- +test_that("link_fun = 'identity' reproduces the default exposure", { + fx <- mk_fixture() + e_default <- exposure(fx$g, fx$cumadopt, valued = TRUE) + e_ident <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "identity") + expect_equal(e_default, e_ident) + + # NULL is treated as identity as well + e_null <- exposure(fx$g, fx$cumadopt, valued = TRUE, link_fun = NULL) + expect_equal(e_default, e_null) +}) + +# ---- Linear --------------------------------------------------------------- +test_that("link_fun = 'linear' applies min(beta * w, 1) to edge weights", { + fx <- mk_fixture() + + g_lin <- fx$g + g_lin[[1]]@x <- pmin(0.4 * g_lin[[1]]@x, 1) + e_manual <- exposure(g_lin, fx$cumadopt, valued = TRUE) + + e_kernel <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "linear", link_pars = list(beta = 0.4)) + expect_equal(e_kernel, e_manual) + + # Linear with beta large enough to saturate at 1 + g_sat <- fx$g + g_sat[[1]]@x <- pmin(1000 * g_sat[[1]]@x, 1) + e_sat_manual <- exposure(g_sat, fx$cumadopt, valued = TRUE) + e_sat_kernel <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "linear", + link_pars = list(beta = 1000)) + expect_equal(e_sat_kernel, e_sat_manual) +}) + +# ---- Sigmoid -------------------------------------------------------------- +test_that("link_fun = 'sigmoid' applies plogis((w - h)/scale)", { + fx <- mk_fixture() + + g_sig <- fx$g + g_sig[[1]]@x <- stats::plogis((g_sig[[1]]@x - 1.0) / 0.5) + e_manual <- exposure(g_sig, fx$cumadopt, valued = TRUE) + + e_kernel <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "sigmoid", + link_pars = list(h = 1.0, scale = 0.5)) + expect_equal(e_kernel, e_manual) +}) + +# ---- Wells-Riley ---------------------------------------------------------- +test_that("link_fun = 'wells-riley' applies 1 - exp(-beta * w)", { + fx <- mk_fixture() + + g_wr <- fx$g + g_wr[[1]]@x <- 1 - exp(-0.7 * g_wr[[1]]@x) + e_manual <- exposure(g_wr, fx$cumadopt, valued = TRUE) + + e_kernel <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "wells-riley", + link_pars = list(beta = 0.7)) + expect_equal(e_kernel, e_manual) +}) + +# ---- User-supplied function ---------------------------------------------- +test_that("link_fun can be a user-supplied function(w, pars)", { + fx <- mk_fixture() + + my_kernel <- function(w, pars) 1 - exp(-pars$beta * w) + + g_wr <- fx$g + g_wr[[1]]@x <- 1 - exp(-0.9 * g_wr[[1]]@x) + e_manual <- exposure(g_wr, fx$cumadopt, valued = TRUE) + + e_user <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = my_kernel, link_pars = list(beta = 0.9)) + expect_equal(e_user, e_manual) +}) + +test_that("link_fun accepts a single-argument closure with baked pars", { + fx <- mk_fixture() + beta <- 0.3 + closure_kernel <- function(w) 1 - exp(-beta * w) + + g_ref <- fx$g + g_ref[[1]]@x <- 1 - exp(-beta * g_ref[[1]]@x) + e_manual <- exposure(g_ref, fx$cumadopt, valued = TRUE) + + # No link_pars supplied at all: should still work. + e_closure <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = closure_kernel) + expect_equal(e_closure, e_manual) + + # Dot-args functions are called with both args (pars travel into ...). + dots_kernel <- function(...) { + a <- list(...) + 1 - exp(-beta * a[[1]]) + } + e_dots <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = dots_kernel) + expect_equal(e_dots, e_manual) +}) + +test_that("user-supplied link_fun must return same-length output", { + fx <- mk_fixture() + bad_kernel <- function(w, pars) w[-1L] + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = bad_kernel, link_pars = list()), + "same length" + ) +}) + +# ---- Parameter validation ------------------------------------------------- +test_that("Missing required link_pars raises informative errors", { + fx <- mk_fixture() + + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "linear", link_pars = list()), + "beta" + ) + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "wells-riley", link_pars = list()), + "beta" + ) + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "sigmoid", link_pars = list(h = 1)), + "scale" + ) + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "sigmoid", link_pars = list(scale = 1)), + "h" + ) +}) + +test_that("Unknown link_fun name errors informatively", { + fx <- mk_fixture() + expect_error( + exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "gompertz", link_pars = list()), + "Unknown link_fun" + ) +}) + +# ---- Interaction with `valued` ------------------------------------------- +test_that("Non-identity link_fun warns and forces valued = TRUE", { + fx <- mk_fixture() + expect_warning( + e_forced <- exposure(fx$g, fx$cumadopt, valued = FALSE, + link_fun = "wells-riley", + link_pars = list(beta = 0.5)), + "valued" + ) + e_valued <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = "wells-riley", + link_pars = list(beta = 0.5)) + expect_equal(e_forced, e_valued) +}) + +# ---- diffnet dispatch threads link_fun through ---------------------------- +test_that("diffnet dispatch accepts link_fun/link_pars", { + set.seed(9) + dn <- suppressWarnings(rdiffnet(n = 20, t = 4, seed.graph = "small-world", + seed.p.adopt = 0.1, + stop.no.diff = FALSE)) + # Force a non-trivial valued graph + for (i in seq_along(dn$graph)) { + dn$graph[[i]]@x <- runif(length(dn$graph[[i]]@x), 0.1, 2.0) + } + + e_ident <- exposure(dn, valued = TRUE) + e_wr <- exposure(dn, valued = TRUE, + link_fun = "wells-riley", + link_pars = list(beta = 1.2)) + + # Output must have the same shape as identity case + expect_equal(dim(e_ident), dim(e_wr)) + + # Wells-Riley output is bounded in [0, 1] + expect_true(all(e_wr >= 0)) + expect_true(all(e_wr <= 1 + 1e-10)) +}) From ebed4bd56465e098fdcc0b4e1ced6521ed88d74e Mon Sep 17 00:00:00 2001 From: aoliveram Date: Fri, 17 Apr 2026 17:40:20 -0700 Subject: [PATCH 15/18] test(exposure): continuous-weight stochastic tests + warn on out-of-range weights (M3, #78) Adds four test_that blocks to tests/testthat/test-stochastic-exposure.R covering each link kernel under seconds-scale weights, the degree-denominator fix, and zero-weight self-loop handling. Small code changes in R/stats.R: (1) warn when stochastic mode sees post-kernel weights outside [0, 1] -- the sampler silently saturated before; (2) denominator now counts non-zero-weight neighbours instead of every stored entry, so link kernels that zero out some edges no longer inflate the normaliser. No NAMESPACE / Rd changes. 652 tests pass. Co-Authored-By: Claude Opus 4.7 --- R/stats.R | 24 +++++- tests/testthat/test-stochastic-exposure.R | 100 +++++++++++++++++++++- 2 files changed, 118 insertions(+), 6 deletions(-) diff --git a/R/stats.R b/R/stats.R index 586f68b..b55b66b 100644 --- a/R/stats.R +++ b/R/stats.R @@ -605,10 +605,28 @@ NULL # Calculate normalization and apply stochastic filter if (mode == "stochastic") { - # Denominator: Based on Degree (Potential connections as binary) - # We treat the graph as binary (weights=1) for the normalization + # Stochastic mode interprets edge weights as Bernoulli probabilities. + # Values outside [0, 1] saturate the sampler (>1 always fires, <0 + # never fires), which is almost never what the user intended when + # weights come from raw quantities (e.g. seconds of contact). Warn + # and let the caller decide whether to apply a `link_fun`. + if (length(graph@x) && + (any(graph@x < 0, na.rm = TRUE) || + any(graph@x > 1, na.rm = TRUE))) { + warning("Stochastic exposure expects edge weights in [0, 1] ", + "(Bernoulli probabilities). Found values outside this ", + "range; the sampler will saturate. Consider applying a ", + "-link_fun- such as \"wells-riley\" or \"linear\" that ", + "maps weights into [0, 1].") + } + + # Denominator: count non-zero-weight neighbours. Using `graph@x != 0` + # instead of `rep(1, ...)` keeps the denominator aligned with the + # kernel output -- structurally-stored zeros (e.g. from a link kernel + # that maps some weights to 0, or from user-supplied 0 weights) do + # not inflate the degree. graph_binary <- graph - graph_binary@x <- rep(1, length(graph@x)) + graph_binary@x <- as.numeric(graph@x != 0) norm <- as.vector(graph_binary %*% attrs) + 1e-20 # Numerator: Stochastic Filter (Bernoulli -> Binary) diff --git a/tests/testthat/test-stochastic-exposure.R b/tests/testthat/test-stochastic-exposure.R index 88a8d5c..6bc9b44 100644 --- a/tests/testthat/test-stochastic-exposure.R +++ b/tests/testthat/test-stochastic-exposure.R @@ -45,19 +45,113 @@ test_that("rdiffnet wrapper with stochastic exposure", { test_that("rdiffnet wrapper with stochastic exposure (multiple behaviors)", { set.seed(1231) # 2 behaviors - diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", + diffnet <- rdiffnet(n=20, t=5, seed.graph="small-world", exposure.mode="stochastic", seed.p.adopt = list(0.2, 0.2), stop.no.diff = FALSE) expect_s3_class(diffnet, "diffnet") - + # Calculate exposure to check dimensions # Note: We must use the same mode to get consistent dimensions/behavior expo <- exposure(diffnet, mode="stochastic") - + # Check dimensions of exposure: n x t x 2 expect_equal(dim(expo), c(20, 5, 2)) expect_true(all(expo >= 0)) expect_true(all(expo <= 1 + 1e-10)) }) + +# ---- Continuous (non-Bernoulli) edge weights ----------------------------- +# Fixture: graph with floating-point weights resembling seconds of contact. +mk_seconds_graph <- function(seed = 17, n = 10L) { + set.seed(seed) + g <- rgraph_er(n, t = 1, p = 0.6) + g@x <- runif(length(g@x), min = 30, max = 3600) # 30s .. 1h + cumadopt <- matrix(0, nrow = n, ncol = 1) + cumadopt[1:4, 1] <- 1 + list(g = list(g), cumadopt = cumadopt, n = n) +} + +test_that("Each link_fun maps seconds-scale weights into valid probabilities", { + fx <- mk_seconds_graph() + + # Identity: raw seconds > 1 warns and saturates the sampler; the + # normalised exposure still ends up in [0, 1]. + expect_warning( + e_id <- exposure(fx$g, fx$cumadopt, valued = TRUE, mode = "stochastic"), + "weights in \\[0, 1\\]" + ) + expect_true(all(e_id >= 0) && all(e_id <= 1 + 1e-10)) + + set.seed(101) + e_wr <- exposure(fx$g, fx$cumadopt, valued = TRUE, mode = "stochastic", + link_fun = "wells-riley", + link_pars = list(beta = 1 / 1800)) + expect_true(all(e_wr >= 0) && all(e_wr <= 1 + 1e-10)) + + set.seed(101) + e_lin <- exposure(fx$g, fx$cumadopt, valued = TRUE, mode = "stochastic", + link_fun = "linear", + link_pars = list(beta = 1 / 5000)) + expect_true(all(e_lin >= 0) && all(e_lin <= 1 + 1e-10)) + + set.seed(101) + e_sig <- exposure(fx$g, fx$cumadopt, valued = TRUE, mode = "stochastic", + link_fun = "sigmoid", + link_pars = list(h = 600, scale = 300)) + expect_true(all(e_sig >= 0) && all(e_sig <= 1 + 1e-10)) +}) + +test_that("Out-of-range warning fires only when raw weights leave [0, 1]", { + fx <- mk_seconds_graph() + + # Kernel maps into [0, 1]: no warning. + expect_warning( + exposure(fx$g, fx$cumadopt, valued = TRUE, mode = "stochastic", + link_fun = "wells-riley", + link_pars = list(beta = 1 / 1800)), + regexp = NA + ) + + # Caller pre-normalises: no warning. + set.seed(99); n <- fx$n + g_ok <- rgraph_er(n, t = 1, p = 0.6); g_ok@x <- runif(length(g_ok@x)) + expect_warning( + exposure(list(g_ok), fx$cumadopt, valued = TRUE, mode = "stochastic"), + regexp = NA + ) +}) + +test_that("Stochastic denominator excludes zero-weight stored neighbours", { + # Node 1 has 4 stored edges to adopters; 3 weigh 0, 1 weighs 1. + # Old behaviour: exposure[1] = 1/4; corrected: 1/1 = 1. + n <- 5L + A <- matrix(0, n, n); A[1, 2:5] <- 1 + G <- methods::as(A, "CsparseMatrix") + stopifnot(length(G@x) == 4) + G@x <- c(0, 0, 0, 1) # three stored zeros, one stored one + + cumadopt <- matrix(0, n, 1); cumadopt[2:5, 1] <- 1 + + set.seed(123) + e <- exposure(list(G), cumadopt, valued = TRUE, mode = "stochastic", + self = TRUE) + + expect_equal(e[1, 1], 1) +}) + +test_that("Zero-weight self-loops do not blow up the exposure", { + set.seed(7); n <- 6L + g <- rgraph_er(n, t = 1, p = 0.6) + g[1, 1] <- 1; g[1, 1] <- 0 # force a stored 0 on the diagonal + cumadopt <- matrix(0, n, 1); cumadopt[1, 1] <- 1 + + e <- exposure(list(g), cumadopt, valued = TRUE, mode = "stochastic", + self = FALSE, + link_fun = "wells-riley", + link_pars = list(beta = 0.5)) + expect_false(anyNA(e)) + expect_true(all(is.finite(e))) + expect_true(all(e >= 0) && all(e <= 1 + 1e-10)) +}) From c4ae6657d82f0fc54094a80bdfccbb83e7cbc68f Mon Sep 17 00:00:00 2001 From: aoliveram Date: Fri, 17 Apr 2026 18:01:14 -0700 Subject: [PATCH 16/18] refactor(exposure): user link_fun is single-arg only (M2 follow-up, #78) Drop the dual signature for user-supplied link_fun. The helper now always calls `link_fun(W@x)` with a single argument; parameters are expected to be baked into the closure. `link_pars` remains relevant only for the named kernels ("linear", "sigmoid", "wells-riley"). Simpler API, simpler docs, simpler tests. Co-Authored-By: Claude Opus 4.7 --- R/stats.R | 32 ++++++++---------- man/exposure.Rd | 17 +++++----- tests/testthat/test-exposure-link-fun.R | 43 ++++++------------------- 3 files changed, 30 insertions(+), 62 deletions(-) diff --git a/R/stats.R b/R/stats.R index b55b66b..833bea9 100644 --- a/R/stats.R +++ b/R/stats.R @@ -281,16 +281,15 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { #' \code{"identity"} (default, no transformation), \code{"linear"} #' (\eqn{\min(\beta w, 1)}), \code{"sigmoid"} #' (\eqn{\mathrm{plogis}((w - h)/\mathrm{scale})}), and \code{"wells-riley"} -#' (\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied function -#' with either signature \code{function(w)} (parameters baked into a -#' closure) or \code{function(w, pars)} (parameters passed through -#' \code{link_pars}); it must return a vector of the same length as -#' \code{w}. When \code{link_fun} is not \code{"identity"}, -#' \code{valued} is forced to \code{TRUE} (with a warning if the user set -#' it to \code{FALSE}). +#' (\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied +#' single-argument function \code{function(w)} with its parameters +#' baked into the closure; it must return a vector of the same length +#' as \code{w}. When \code{link_fun} is not \code{"identity"}, +#' \code{valued} is forced to \code{TRUE} (with a warning if the user +#' set it to \code{FALSE}). #' @param link_pars Named list with the scalar parameters required by -#' \code{link_fun}. See the section "Link / kernel functions" for the -#' required names per kernel. +#' the named kernels (\code{"linear"}, \code{"sigmoid"}, +#' \code{"wells-riley"}). Ignored when \code{link_fun} is a function. #' @details #' Exposure is calculated as follows: #' @@ -530,22 +529,17 @@ dgr.array <- function(graph, cmode, undirected, self, valued) { NULL # Link / kernel function applied to edge weights before exposure is computed. -# `link_fun` may be one of the named kernels listed below or a user-supplied -# function. User functions may have either signature: -# - `function(w)` — parameters baked into a closure, -# - `function(w, pars)` — parameters passed via `link_pars`. -# Either way, the function must return a vector of the same length as `w`, -# where `w` is the non-zero entries of a dgCMatrix (`@x`). +# `link_fun` is either one of the named kernels listed below or a +# user-supplied single-argument function `function(w)` that returns a vector +# of the same length as `w` (the non-zero entries of a dgCMatrix, `@x`). +# Parameters for user functions are expected to be baked into the closure. .apply_link_kernel <- function(W, link_fun, link_pars) { if (is.null(link_fun) || (is.character(link_fun) && identical(link_fun, "identity"))) return(W) if (is.function(link_fun)) { - fmls <- formals(link_fun) - single_arg <- !is.null(fmls) && length(fmls) == 1L && - !("..." %in% names(fmls)) - new_x <- if (single_arg) link_fun(W@x) else link_fun(W@x, link_pars) + new_x <- link_fun(W@x) if (length(new_x) != length(W@x)) stop("Custom -link_fun- must return a vector of the same length as ", "its input (the non-zero edge weights).") diff --git a/man/exposure.Rd b/man/exposure.Rd index 339e98a..1b9599f 100644 --- a/man/exposure.Rd +++ b/man/exposure.Rd @@ -57,17 +57,16 @@ matrix will be the lagged exposure as specified (see examples).} \code{"identity"} (default, no transformation), \code{"linear"} (\eqn{\min(\beta w, 1)}), \code{"sigmoid"} (\eqn{\mathrm{plogis}((w - h)/\mathrm{scale})}), and \code{"wells-riley"} -(\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied function -with either signature \code{function(w)} (parameters baked into a -closure) or \code{function(w, pars)} (parameters passed through -\code{link_pars}); it must return a vector of the same length as -\code{w}. When \code{link_fun} is not \code{"identity"}, -\code{valued} is forced to \code{TRUE} (with a warning if the user set -it to \code{FALSE}).} +(\eqn{1 - \exp(-\beta w)}). Alternatively, a user-supplied +single-argument function \code{function(w)} with its parameters +baked into the closure; it must return a vector of the same length +as \code{w}. When \code{link_fun} is not \code{"identity"}, +\code{valued} is forced to \code{TRUE} (with a warning if the user +set it to \code{FALSE}).} \item{link_pars}{Named list with the scalar parameters required by -\code{link_fun}. See the section "Link / kernel functions" for the -required names per kernel.} +the named kernels (\code{"linear"}, \code{"sigmoid"}, +\code{"wells-riley"}). Ignored when \code{link_fun} is a function.} \item{...}{Further arguments passed to \code{\link{struct_equiv}} (only used when \code{alt.graph="se"}).} diff --git a/tests/testthat/test-exposure-link-fun.R b/tests/testthat/test-exposure-link-fun.R index 8cdf6dc..0531f10 100644 --- a/tests/testthat/test-exposure-link-fun.R +++ b/tests/testthat/test-exposure-link-fun.R @@ -77,50 +77,25 @@ test_that("link_fun = 'wells-riley' applies 1 - exp(-beta * w)", { }) # ---- User-supplied function ---------------------------------------------- -test_that("link_fun can be a user-supplied function(w, pars)", { +test_that("link_fun accepts a single-argument user function with baked pars", { fx <- mk_fixture() - - my_kernel <- function(w, pars) 1 - exp(-pars$beta * w) - - g_wr <- fx$g - g_wr[[1]]@x <- 1 - exp(-0.9 * g_wr[[1]]@x) - e_manual <- exposure(g_wr, fx$cumadopt, valued = TRUE) - - e_user <- exposure(fx$g, fx$cumadopt, valued = TRUE, - link_fun = my_kernel, link_pars = list(beta = 0.9)) - expect_equal(e_user, e_manual) -}) - -test_that("link_fun accepts a single-argument closure with baked pars", { - fx <- mk_fixture() - beta <- 0.3 - closure_kernel <- function(w) 1 - exp(-beta * w) + # A fully-specified closure -- no link_pars needed. + random_func <- function(x) 1 - exp(-0.3 * x) g_ref <- fx$g - g_ref[[1]]@x <- 1 - exp(-beta * g_ref[[1]]@x) + g_ref[[1]]@x <- 1 - exp(-0.3 * g_ref[[1]]@x) e_manual <- exposure(g_ref, fx$cumadopt, valued = TRUE) - # No link_pars supplied at all: should still work. - e_closure <- exposure(fx$g, fx$cumadopt, valued = TRUE, - link_fun = closure_kernel) - expect_equal(e_closure, e_manual) - - # Dot-args functions are called with both args (pars travel into ...). - dots_kernel <- function(...) { - a <- list(...) - 1 - exp(-beta * a[[1]]) - } - e_dots <- exposure(fx$g, fx$cumadopt, valued = TRUE, - link_fun = dots_kernel) - expect_equal(e_dots, e_manual) + e_user <- exposure(fx$g, fx$cumadopt, valued = TRUE, + link_fun = random_func) + expect_equal(e_user, e_manual) }) test_that("user-supplied link_fun must return same-length output", { fx <- mk_fixture() - bad_kernel <- function(w, pars) w[-1L] + bad_kernel <- function(w) w[-1L] expect_error( - exposure(fx$g, fx$cumadopt, valued = TRUE, - link_fun = bad_kernel, link_pars = list()), + exposure(fx$g, fx$cumadopt, valued = TRUE, link_fun = bad_kernel), "same length" ) }) From c6d84e94d6bdf1c32d37d12565665069ee878a4e Mon Sep 17 00:00:00 2001 From: aoliveram Date: Mon, 20 Apr 2026 11:44:17 -0700 Subject: [PATCH 17/18] feat: logit adoption model (M4) Adds adoption_model = c("threshold", "logit") and adoption_pars arguments to rdiffnet(). In logit mode, node i adopts at step t with probability plogis(beta0 + beta_expo * exposure_i), drawn from runif() each step. Threshold stays the default so fixed-seed calls that omit the new argument remain bit-identical. 15 new tests cover backward compatibility, parameter validation, saturation / suppression extremes, and multi-behaviour runs. Full suite: 665 pass, 0 fail. --- R/rdiffnet.r | 29 ++++++++- man/rdiffnet.Rd | 13 +++- tests/testthat/test-rdiffnet-logit.R | 97 ++++++++++++++++++++++++++++ 3 files changed, 135 insertions(+), 4 deletions(-) create mode 100644 tests/testthat/test-rdiffnet-logit.R diff --git a/R/rdiffnet.r b/R/rdiffnet.r index f15407a..fed1eca 100644 --- a/R/rdiffnet.r +++ b/R/rdiffnet.r @@ -28,6 +28,13 @@ #' @param stop.no.diff Logical scalar. When \code{TRUE}, the function will return #' with error if there was no diffusion. Otherwise it throws a warning. #' @param disadopt Function of disadoption, with current exposition, cumulative adoption, and time as possible inputs. +#' @param adoption_model Character scalar. Either \code{"threshold"} (default; +#' adopter iff \code{exposure >= threshold}) or \code{"logit"} (adopter with +#' probability \code{plogis(beta0 + beta_expo * exposure)}). +#' @param adoption_pars Named list. Required when +#' \code{adoption_model = "logit"}: must supply \code{beta0} (intercept) +#' and \code{beta_expo} (slope on exposure). Ignored when +#' \code{adoption_model = "threshold"}. #' @return A random \code{\link{diffnet}} class object. #' @family simulation functions #' @details @@ -408,9 +415,18 @@ rdiffnet <- function( name = "A diffusion network", behavior = "Random contagion", stop.no.diff = TRUE, - disadopt = NULL + disadopt = NULL, + adoption_model = c("threshold", "logit"), + adoption_pars = NULL ) { + adoption_model <- match.arg(adoption_model) + if (adoption_model == "logit") { + if (is.null(adoption_pars$beta0) || is.null(adoption_pars$beta_expo)) + stop("-adoption_pars- must supply both -beta0- and -beta_expo- ", + "when -adoption_model- = \"logit\".") + } + # Checking options for (arg in names(default_rewire.args)) if (!length(rewire.args[[arg]])) @@ -567,8 +583,15 @@ rdiffnet <- function( for (q in 1:num_of_behaviors) { - # 3.2 Identifying who adopts based on the threshold - whoadopts <- which( (expo[,,q] >= thr[,q]) & is.na(toa[,q])) + # 3.2 Identifying who adopts under the configured adoption model + if (adoption_model == "logit") { + e_q <- as.vector(expo[, , q]) + p <- stats::plogis(adoption_pars$beta0 + + adoption_pars$beta_expo * e_q) + whoadopts <- which((stats::runif(length(p)) < p) & is.na(toa[, q])) + } else { + whoadopts <- which((expo[, , q] >= thr[, q]) & is.na(toa[, q])) + } # 3.3 Updating the cumadopt cumadopt[whoadopts, i:t, q] <- 1L diff --git a/man/rdiffnet.Rd b/man/rdiffnet.Rd index 1a27d23..71a100b 100644 --- a/man/rdiffnet.Rd +++ b/man/rdiffnet.Rd @@ -22,7 +22,9 @@ rdiffnet( name = "A diffusion network", behavior = "Random contagion", stop.no.diff = TRUE, - disadopt = NULL + disadopt = NULL, + adoption_model = c("threshold", "logit"), + adoption_pars = NULL ) } \arguments{ @@ -72,6 +74,15 @@ threshold for each node.} with error if there was no diffusion. Otherwise it throws a warning.} \item{disadopt}{Function of disadoption, with current exposition, cumulative adoption, and time as possible inputs.} + +\item{adoption_model}{Character scalar. Either \code{"threshold"} (default; +adopter iff \code{exposure >= threshold}) or \code{"logit"} (adopter with +probability \code{plogis(beta0 + beta_expo * exposure)}).} + +\item{adoption_pars}{Named list. Required when +\code{adoption_model = "logit"}: must supply \code{beta0} (intercept) +and \code{beta_expo} (slope on exposure). Ignored when +\code{adoption_model = "threshold"}.} } \value{ A random \code{\link{diffnet}} class object. diff --git a/tests/testthat/test-rdiffnet-logit.R b/tests/testthat/test-rdiffnet-logit.R new file mode 100644 index 0000000..0b2df6f --- /dev/null +++ b/tests/testthat/test-rdiffnet-logit.R @@ -0,0 +1,97 @@ +context("rdiffnet logit adoption model") +library(netdiffuseR) + +test_that("threshold (default) path is unchanged", { + # Compare a threshold run under fixed seed against itself on a second + # call with adoption_model explicitly set to "threshold". + set.seed(2026) + dn_default <- rdiffnet(n = 25, t = 6, seed.graph = "small-world", + seed.p.adopt = 0.1, stop.no.diff = FALSE) + + set.seed(2026) + dn_threshold <- rdiffnet(n = 25, t = 6, seed.graph = "small-world", + seed.p.adopt = 0.1, stop.no.diff = FALSE, + adoption_model = "threshold") + + expect_identical(dn_default$toa, dn_threshold$toa) +}) + +test_that("logit mode runs and returns a diffnet", { + set.seed(2026) + dn <- rdiffnet(n = 40, t = 6, seed.graph = "small-world", + seed.p.adopt = 0.05, stop.no.diff = FALSE, + adoption_model = "logit", + adoption_pars = list(beta0 = -2, beta_expo = 6)) + + expect_s3_class(dn, "diffnet") + expect_equal(dim(dn$cumadopt), c(40, 6)) + expect_equal(length(dn$toa), 40) +}) + +test_that("logit mode requires both beta0 and beta_expo", { + expect_error( + rdiffnet(n = 20, t = 4, seed.graph = "small-world", + adoption_model = "logit", adoption_pars = list(beta0 = 0), + stop.no.diff = FALSE), + "beta0.*beta_expo|beta_expo" + ) + expect_error( + rdiffnet(n = 20, t = 4, seed.graph = "small-world", + adoption_model = "logit", adoption_pars = list(beta_expo = 1), + stop.no.diff = FALSE), + "beta0" + ) + expect_error( + rdiffnet(n = 20, t = 4, seed.graph = "small-world", + adoption_model = "logit", + stop.no.diff = FALSE), + "beta0" + ) +}) + +test_that("saturating beta0 drives near-universal adoption", { + # Very large intercept -> plogis(beta0 + ...) ~ 1 for all exposures. + set.seed(99) + dn <- rdiffnet(n = 60, t = 8, seed.graph = "small-world", + seed.p.adopt = 0.05, stop.no.diff = FALSE, + adoption_model = "logit", + adoption_pars = list(beta0 = 50, beta_expo = 0)) + # Everyone has adopted by some t <= T + expect_true(all(!is.na(dn$toa))) +}) + +test_that("very negative beta0 + beta_expo = 0 suppresses diffusion", { + set.seed(99) + expect_warning( + dn <- rdiffnet(n = 30, t = 6, seed.graph = "small-world", + seed.p.adopt = 0.05, stop.no.diff = FALSE, + adoption_model = "logit", + adoption_pars = list(beta0 = -50, beta_expo = 0)), + "No diffusion" + ) + # All adopters come from the seed round (toa == 1) + expect_true(all(dn$toa[!is.na(dn$toa)] == 1L)) +}) + +test_that("logit works with multiple behaviors", { + set.seed(2026) + dn <- rdiffnet(n = 40, t = 6, seed.graph = "small-world", + seed.p.adopt = list(0.05, 0.05), + stop.no.diff = FALSE, + adoption_model = "logit", + adoption_pars = list(beta0 = -1, beta_expo = 4)) + + expect_s3_class(dn, "diffnet") + # Multi-behavior diffnet stores cumadopt as a list of length num_behaviors + expect_type(dn$cumadopt, "list") + expect_length(dn$cumadopt, 2L) + expect_equal(dim(dn$toa), c(40, 2)) +}) + +test_that("unknown adoption_model raises match.arg error", { + expect_error( + rdiffnet(n = 20, t = 4, seed.graph = "small-world", + adoption_model = "probit", stop.no.diff = FALSE), + "should be one of" + ) +}) From cd3cc8d1ad549e7fa73dd3f1f5e41c98385d0c1f Mon Sep 17 00:00:00 2001 From: aoliveram Date: Tue, 21 Apr 2026 21:55:38 -0700 Subject: [PATCH 18/18] rename(rdiffnet): adoption_model values threshold/logit -> deterministic/stochastic (M4 follow-up, #78) Renames the two values accepted by adoption_model in rdiffnet() so the vocabulary matches the existing exposure.mode (which already uses deterministic/stochastic). No behavior change: - adoption_model = c("deterministic", "stochastic"), default "deterministic" (was "threshold") - "stochastic" keeps the same plogis(beta0 + beta_expo * exposure) Bernoulli draw previously called "logit" - Error message, roxygen doc and man page updated to match - Test file renamed test-rdiffnet-logit.R -> test-rdiffnet-stochastic.R; all 15 existing expectations updated to use the new names Full suite: 665 passed, 0 failed. --- R/rdiffnet.r | 16 ++++----- man/rdiffnet.Rd | 10 +++--- ...net-logit.R => test-rdiffnet-stochastic.R} | 36 +++++++++---------- 3 files changed, 31 insertions(+), 31 deletions(-) rename tests/testthat/{test-rdiffnet-logit.R => test-rdiffnet-stochastic.R} (69%) diff --git a/R/rdiffnet.r b/R/rdiffnet.r index fed1eca..353bc1f 100644 --- a/R/rdiffnet.r +++ b/R/rdiffnet.r @@ -28,13 +28,13 @@ #' @param stop.no.diff Logical scalar. When \code{TRUE}, the function will return #' with error if there was no diffusion. Otherwise it throws a warning. #' @param disadopt Function of disadoption, with current exposition, cumulative adoption, and time as possible inputs. -#' @param adoption_model Character scalar. Either \code{"threshold"} (default; -#' adopter iff \code{exposure >= threshold}) or \code{"logit"} (adopter with +#' @param adoption_model Character scalar. Either \code{"deterministic"} (default; +#' adopter iff \code{exposure >= threshold}) or \code{"stochastic"} (adopter with #' probability \code{plogis(beta0 + beta_expo * exposure)}). #' @param adoption_pars Named list. Required when -#' \code{adoption_model = "logit"}: must supply \code{beta0} (intercept) +#' \code{adoption_model = "stochastic"}: must supply \code{beta0} (intercept) #' and \code{beta_expo} (slope on exposure). Ignored when -#' \code{adoption_model = "threshold"}. +#' \code{adoption_model = "deterministic"}. #' @return A random \code{\link{diffnet}} class object. #' @family simulation functions #' @details @@ -416,15 +416,15 @@ rdiffnet <- function( behavior = "Random contagion", stop.no.diff = TRUE, disadopt = NULL, - adoption_model = c("threshold", "logit"), + adoption_model = c("deterministic", "stochastic"), adoption_pars = NULL ) { adoption_model <- match.arg(adoption_model) - if (adoption_model == "logit") { + if (adoption_model == "stochastic") { if (is.null(adoption_pars$beta0) || is.null(adoption_pars$beta_expo)) stop("-adoption_pars- must supply both -beta0- and -beta_expo- ", - "when -adoption_model- = \"logit\".") + "when -adoption_model- = \"stochastic\".") } # Checking options @@ -584,7 +584,7 @@ rdiffnet <- function( for (q in 1:num_of_behaviors) { # 3.2 Identifying who adopts under the configured adoption model - if (adoption_model == "logit") { + if (adoption_model == "stochastic") { e_q <- as.vector(expo[, , q]) p <- stats::plogis(adoption_pars$beta0 + adoption_pars$beta_expo * e_q) diff --git a/man/rdiffnet.Rd b/man/rdiffnet.Rd index 71a100b..ce15355 100644 --- a/man/rdiffnet.Rd +++ b/man/rdiffnet.Rd @@ -23,7 +23,7 @@ rdiffnet( behavior = "Random contagion", stop.no.diff = TRUE, disadopt = NULL, - adoption_model = c("threshold", "logit"), + adoption_model = c("deterministic", "stochastic"), adoption_pars = NULL ) } @@ -75,14 +75,14 @@ with error if there was no diffusion. Otherwise it throws a warning.} \item{disadopt}{Function of disadoption, with current exposition, cumulative adoption, and time as possible inputs.} -\item{adoption_model}{Character scalar. Either \code{"threshold"} (default; -adopter iff \code{exposure >= threshold}) or \code{"logit"} (adopter with +\item{adoption_model}{Character scalar. Either \code{"deterministic"} (default; +adopter iff \code{exposure >= threshold}) or \code{"stochastic"} (adopter with probability \code{plogis(beta0 + beta_expo * exposure)}).} \item{adoption_pars}{Named list. Required when -\code{adoption_model = "logit"}: must supply \code{beta0} (intercept) +\code{adoption_model = "stochastic"}: must supply \code{beta0} (intercept) and \code{beta_expo} (slope on exposure). Ignored when -\code{adoption_model = "threshold"}.} +\code{adoption_model = "deterministic"}.} } \value{ A random \code{\link{diffnet}} class object. diff --git a/tests/testthat/test-rdiffnet-logit.R b/tests/testthat/test-rdiffnet-stochastic.R similarity index 69% rename from tests/testthat/test-rdiffnet-logit.R rename to tests/testthat/test-rdiffnet-stochastic.R index 0b2df6f..a28073c 100644 --- a/tests/testthat/test-rdiffnet-logit.R +++ b/tests/testthat/test-rdiffnet-stochastic.R @@ -1,26 +1,26 @@ -context("rdiffnet logit adoption model") +context("rdiffnet stochastic adoption model") library(netdiffuseR) -test_that("threshold (default) path is unchanged", { - # Compare a threshold run under fixed seed against itself on a second - # call with adoption_model explicitly set to "threshold". +test_that("deterministic (default) path is unchanged", { + # Compare a deterministic run under fixed seed against itself on a second + # call with adoption_model explicitly set to "deterministic". set.seed(2026) dn_default <- rdiffnet(n = 25, t = 6, seed.graph = "small-world", seed.p.adopt = 0.1, stop.no.diff = FALSE) set.seed(2026) - dn_threshold <- rdiffnet(n = 25, t = 6, seed.graph = "small-world", - seed.p.adopt = 0.1, stop.no.diff = FALSE, - adoption_model = "threshold") + dn_det <- rdiffnet(n = 25, t = 6, seed.graph = "small-world", + seed.p.adopt = 0.1, stop.no.diff = FALSE, + adoption_model = "deterministic") - expect_identical(dn_default$toa, dn_threshold$toa) + expect_identical(dn_default$toa, dn_det$toa) }) -test_that("logit mode runs and returns a diffnet", { +test_that("stochastic mode runs and returns a diffnet", { set.seed(2026) dn <- rdiffnet(n = 40, t = 6, seed.graph = "small-world", seed.p.adopt = 0.05, stop.no.diff = FALSE, - adoption_model = "logit", + adoption_model = "stochastic", adoption_pars = list(beta0 = -2, beta_expo = 6)) expect_s3_class(dn, "diffnet") @@ -28,22 +28,22 @@ test_that("logit mode runs and returns a diffnet", { expect_equal(length(dn$toa), 40) }) -test_that("logit mode requires both beta0 and beta_expo", { +test_that("stochastic mode requires both beta0 and beta_expo", { expect_error( rdiffnet(n = 20, t = 4, seed.graph = "small-world", - adoption_model = "logit", adoption_pars = list(beta0 = 0), + adoption_model = "stochastic", adoption_pars = list(beta0 = 0), stop.no.diff = FALSE), "beta0.*beta_expo|beta_expo" ) expect_error( rdiffnet(n = 20, t = 4, seed.graph = "small-world", - adoption_model = "logit", adoption_pars = list(beta_expo = 1), + adoption_model = "stochastic", adoption_pars = list(beta_expo = 1), stop.no.diff = FALSE), "beta0" ) expect_error( rdiffnet(n = 20, t = 4, seed.graph = "small-world", - adoption_model = "logit", + adoption_model = "stochastic", stop.no.diff = FALSE), "beta0" ) @@ -54,7 +54,7 @@ test_that("saturating beta0 drives near-universal adoption", { set.seed(99) dn <- rdiffnet(n = 60, t = 8, seed.graph = "small-world", seed.p.adopt = 0.05, stop.no.diff = FALSE, - adoption_model = "logit", + adoption_model = "stochastic", adoption_pars = list(beta0 = 50, beta_expo = 0)) # Everyone has adopted by some t <= T expect_true(all(!is.na(dn$toa))) @@ -65,7 +65,7 @@ test_that("very negative beta0 + beta_expo = 0 suppresses diffusion", { expect_warning( dn <- rdiffnet(n = 30, t = 6, seed.graph = "small-world", seed.p.adopt = 0.05, stop.no.diff = FALSE, - adoption_model = "logit", + adoption_model = "stochastic", adoption_pars = list(beta0 = -50, beta_expo = 0)), "No diffusion" ) @@ -73,12 +73,12 @@ test_that("very negative beta0 + beta_expo = 0 suppresses diffusion", { expect_true(all(dn$toa[!is.na(dn$toa)] == 1L)) }) -test_that("logit works with multiple behaviors", { +test_that("stochastic works with multiple behaviors", { set.seed(2026) dn <- rdiffnet(n = 40, t = 6, seed.graph = "small-world", seed.p.adopt = list(0.05, 0.05), stop.no.diff = FALSE, - adoption_model = "logit", + adoption_model = "stochastic", adoption_pars = list(beta0 = -1, beta_expo = 4)) expect_s3_class(dn, "diffnet")