From 5f0c24468a39c7ad66b59adaa92e6f8f3a3559c3 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Tue, 5 Nov 2024 17:33:38 +0100 Subject: [PATCH 01/11] introducing easybgm to JASP --- DESCRIPTION | 2 +- R/bayesiannetworkanalysis.R | 222 ++++++++++++++++++--------- inst/qml/BayesianNetworkAnalysis.qml | 131 ++++++++++++---- 3 files changed, 246 insertions(+), 109 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 029063c..d93af7c 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ License: GPL (>= 2) Encoding: UTF-8 Imports: bootnet, - BDgraph, + easybgm, corpcor, dplyr, foreach, diff --git a/R/bayesiannetworkanalysis.R b/R/bayesiannetworkanalysis.R index 509a4c0..a1babe0 100644 --- a/R/bayesiannetworkanalysis.R +++ b/R/bayesiannetworkanalysis.R @@ -20,7 +20,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { # MissingValues needed for the .networkAnalysisReadData function in the frequentist network module: options[["missingValues"]] <- "listwise" # Unfortunately BDgraph does not work with pairwise missing values - dataset <- .networkAnalysisReadData(dataset, options) + dataset <- .networkAnalysisReadData(dataset, options) # from networkanalysis.R mainContainer <- .bayesianNetworkAnalysisSetupMainContainerAndTable(jaspResults, dataset, options) .bayesianNetworkAnalysisErrorCheck(mainContainer, dataset, options) @@ -77,7 +77,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { dataset <- Reduce(rbind.data.frame, dataset) if (options[["groupingVariable"]] != "") { - # these cannot be chained unfortunately + # these cannot be chained unfortunately - this will be changed to bgmCompare soon groupingVariableName <- options[["groupingVariable"]] dfGroup <- data.frame(groupingVariable) colnames(dfGroup) <- groupingVariableName @@ -104,12 +104,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { # List that contains state or is empty: networkList <- list( - network = mainContainer[["networkState"]]$object, + network = mainContainer[["networkState"]]$object, # stores the results centrality = mainContainer[["centralityState"]]$object, layout = mainContainer[["layoutState"]]$object ) - if (length(options[["variables"]]) <= 2L) + if (length(options[["variables"]]) <= 2L) # returns an empty table if there are less than 3 variables return(networkList) if (is.null(networkList[["network"]])) @@ -155,7 +155,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { centralitySamples <- centrality(network = network, options = options) # Compute centrality measures for each posterior sample: - nSamples <- nrow(network$samplesPosterior) + nSamples <- nrow(network$samplesPosterior[[1]]) # centralitySamples is in wide format, so to select all samples (without cols representing the variables) we need 3:(nSamples+2) posteriorMeans <- apply(centralitySamples[, 3:(nSamples+2)], MARGIN = 1, mean) @@ -178,7 +178,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { .bayesianNetworkAnalysisComputeLayout <- function(networks, dataset, options) { - # Reformat networks to fit averageLayout: + # Reformat networks to fit averageLayout: #CHANGES THE LAYOUT DEPENDING HOW MANY NETWORKS ARE ESIMTATED BASED ON SPLIT weightMatrices <- list() for (i in seq_along(networks)) { weightMatrices[[i]] <- networks[[i]]$graph @@ -193,67 +193,141 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { networks <- vector("list", length(dataset)) - # For every dataset (given the splitting variable) estimate a network: + # For every dataset (given the splitting variable) estimate a network: KEEP THIS AS IS FOR NOW for (nw in seq_along(dataset)) { - + #ADD CONDITIONAL STATEMENTS HERE FOR EACH OF THE THREE OPTIONS # When method = "gcgm" a vector with binary values is needed: - nonContVariables <- NULL + # if estimator is gcgm + if (options[["estimator"]] == "ggm") { + + # Estimate mixed network + jaspBase::.setSeedJASP(options) + easybgmFit <- try(easybgm::easybgm(data = as.data.frame(apply(dataset[[nw]], 2, as.numeric)), + type = "continuous", + package = "BDgraph" , + iter = as.numeric(options[["iter"]]), + save = TRUE, + centrality = FALSE, + burnin = as.numeric(options[["burnin"]]), + g.start = options[["initialConfiguration"]], + df.prior = as.numeric(options[["dfprior"]]), + g.prior = as.numeric(options[["gprior"]]))) + + if (isTryError(easybgmFit)) { + message <- .extractErrorMessage(easybgmFit) + .quitAnalysis(gettextf("The analysis failed with the following error message:\n%s", message)) + } + + + + # Extract results + easybgmResult <- list() + + easybgmResult$graphWeights <- easybgmFit$graph_weights + easybgmResult$inclusionProbabilities <- easybgmFit$inc_probs + easybgmResult$BF <- easybgmFit$inc_BF + easybgmResult$structure <- easybgmFit$structure + easybgmResult$estimates <- as.matrix(easybgmFit$parameters) + easybgmResult$graph <- easybgmResult$estimates*easybgmResult$structure + easybgmResult$sampleGraphs <- easybgmFit$sample_graph + easybgmResult$samplesPosterior <- list(easybgmFit$samples_posterior) + + networks[[nw]] <- easybgmResult + + } + + # if estimator is gcgm if (options[["estimator"]] == "gcgm") { nonContVariables <- c() for (var in options[["variables"]]) { # A 1 indicates noncontinuous variables: - if (length(levels(factor(dataset[[nw]][[var]]))) <= 8) { + if (length(levels(factor(dataset[[nw]][[var]]))) <= 8) { # if the number of categories is less than 8, then they are treated as non-cont. Does this make sense? nonContVariables <- c(nonContVariables, 1) } else { nonContVariables <- c(nonContVariables, 0) } } + # Estimate mixed network + jaspBase::.setSeedJASP(options) + easybgmFit <- try(easybgm::easybgm(data = as.data.frame(apply(dataset[[nw]], 2, as.numeric)), + type = "mixed", + package = "BDgraph", + not_cont = nonContVariables, + iter = as.numeric(options[["iter"]]), + save = TRUE, + centrality = FALSE, + burnin = as.numeric(options[["burnin"]]), + g.start = options[["initialConfiguration"]], + df.prior = as.numeric(options[["dfprior"]]), + g.prior = as.numeric(options[["gprior"]]))) + + + if (isTryError(easybgmFit)) { + message <- .extractErrorMessage(easybgmFit) + .quitAnalysis(gettextf("The analysis failed with the following error message:\n%s", message)) + } + + + # Extract results + easybgmResult <- list() + + easybgmResult$graphWeights <- easybgmFit$graph_weights + easybgmResult$inclusionProbabilities <- easybgmFit$inc_probs + easybgmResult$BF <- easybgmFit$inc_BF + easybgmResult$structure <- easybgmFit$structure + easybgmResult$estimates <- as.matrix(easybgmFit$parameters) + easybgmResult$graph <- easybgmResult$estimates*easybgmResult$structure + easybgmResult$sampleGraphs <- easybgmFit$sample_graph + easybgmResult$samplesPosterior <- list(easybgmFit$samples_posterior) + + networks[[nw]] <- easybgmResult } - jaspBase::.setSeedJASP(options) - - # Estimate network: - bdgraphFit <- try(BDgraph::bdgraph(data = as.data.frame(dataset[[nw]]), - method = options[["estimator"]], - not.cont = nonContVariables, - algorithm = "rjmcmc", - iter = as.numeric(options[["iter"]]), - save = TRUE, - burnin = as.numeric(options[["burnin"]]), - g.start = options[["initialConfiguration"]], - df.prior = as.numeric(options[["dfprior"]]), - g.prior = as.numeric(options[["gprior"]]))) - - if (isTryError(bdgraphFit)) { - message <- .extractErrorMessage(bdgraphFit) - if (startsWith(message, "system is computationally singular")) { - .quitAnalysis(gettext("Could not invert the posterior rate matrix. Check if the covariance matrix of your data is singular. For example, check for any pairs of variables with extreme correlations (> .95).")) - } else { - .quitAnalysis(gettextf("BDgraph failed with the following error message:\n%s", message)) + + # if estimator is ordinal Markov random field + if (options[["estimator"]] == "omrf") { + for (var in options[["variables"]]) { + # Check if variables are binary or ordinal: + if (!is.factor(dataset[[nw]][[var]])) { + .quitAnalysis(gettext("Some of the variables you have entered for analysis are not binary or ordinal. Please make sure that all variables are binary or ordinal or change the estimator to gcgm.")) + } else { + # Estimate mixed network + jaspBase::.setSeedJASP(options) + easybgmFit <- try(easybgm::easybgm(data = as.data.frame(dataset[[nw]]), + type = "ordinal", + package = "bgms", + iter = as.numeric(options[["iter"]]), + save = TRUE, + centrality = FALSE, + burnin = as.numeric(options[["burnin"]]), + inclusion_probability = as.numeric(options[["gprior"]]), + interaction_scale = as.numeric(options[["interactionScale"]]), + edge_prior = as.character(options[["edgePrior"]]))) + + } + } + + if (isTryError(easybgmFit)) { + message <- .extractErrorMessage(easybgmFit) + .quitAnalysis(gettextf("The analysis failed with the following error message:\n%s", message)) } - } - # Extract results: - bdgraphResult <- list() - - bdgraphResult$graphWeights <- bdgraphFit$graph_weights - bdgraphResult$inclusionProbabilities <- as.matrix(BDgraph::plinks(bdgraphFit)) - bdgraphResult$inclusionProbabilities <- bdgraphResult$inclusionProbabilities + t(bdgraphResult$inclusionProbabilities) - bdgraphResult$BF <- bdgraphResult$inclusionProbabilities / (1 - bdgraphResult$inclusionProbabilities) / (as.numeric(options[["gprior"]]) / (1 - as.numeric(options[["gprior"]]))) - bdgraphResult$structure <- 1*(bdgraphResult$inclusionProbabilities > 0.5) - bdgraphResult$estimates <- pr2pc(bdgraphFit$K_hat); diag(bdgraphResult$estimates) <- 0 - bdgraphResult$graph <- bdgraphResult$estimates*bdgraphResult$structure - bdgraphResult$sampleGraphs <- bdgraphFit$sample_graphs - - # TODO: remove by default and place in the centrality function: - bdgraphResult$samplesPosterior <- extractposterior(bdgraphFit, - as.data.frame(dataset[[nw]]), - options[["estimator"]], - nonContVariables, - options)[[1]] - - networks[[nw]] <- bdgraphResult + + # Extract results + easybgmResult <- list() + + easybgmResult$graphWeights <- easybgmFit$graph_weights + easybgmResult$inclusionProbabilities <- easybgmFit$inc_probs + easybgmResult$BF <- easybgmFit$inc_BF + easybgmResult$structure <- easybgmFit$structure + easybgmResult$estimates <- as.matrix(easybgmFit$parameters) + easybgmResult$graph <- easybgmResult$estimates*easybgmResult$structure + easybgmResult$sampleGraphs <- easybgmFit$sample_graph + easybgmResult$samplesPosterior <- list(easybgmFit$samples_posterior) + + networks[[nw]] <- easybgmResult + } } return(networks) @@ -327,7 +401,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { if (is.null(plotContainer)) { plotContainer <- createJaspContainer(dependencies = c("labelAbbreviation", "labelAbbreviationLength", - "legend", "variableNamesShown")) # position = 5 + "legend", "variableNamesShown")) # position = 5 mainContainer[["plotContainer"]] <- plotContainer } @@ -513,9 +587,9 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { } if (length(unique(centralitySummary$type)) > 1) { - g <- g + ggplot2::facet_grid(type ~ measure, scales = "free") + g <- g + ggplot2::facet_grid(type ~ measure, scales = "free") } else { - g <- g + ggplot2::facet_grid(~ measure, scales = "free") + g <- g + ggplot2::facet_grid(~ measure, scales = "free") } g <- g + ggplot2::theme_bw() @@ -569,11 +643,11 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { dataComplexity <- dplyr::mutate(dataComplexity, complexityWeight = complexityWeight/sum(complexityWeight)) plot <- ggplot2::ggplot(dataComplexity, ggplot2::aes(x = complexity, y = complexityWeight)) + - jaspGraphs::geom_point() + - ggplot2::ylab("Posterior Probability") + - ggplot2::xlab("Number of edges") + - jaspGraphs::geom_rangeframe() + - jaspGraphs::themeJaspRaw(legend.position = c(.85, 0.25)) + jaspGraphs::geom_point() + + ggplot2::ylab("Posterior Probability") + + ggplot2::xlab("Number of edges") + + jaspGraphs::geom_rangeframe() + + jaspGraphs::themeJaspRaw(legend.position = c(.85, 0.25)) complexityPlotContainer[[v]]$plotObject <- plot @@ -595,12 +669,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { title <- if (nGraphs == 1L) gettext("Structure Plot") else gettext("Structure Plots") structurePlotContainer <- createJaspContainer(title = title, dependencies = c("posteriorStructurePlot", - "layout", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", "cut", "showDetails", "nodePalette", - "legendSpecificPlotNumber", "estimator", - "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", - "keepLayoutTheSame", "layoutX", "layoutY", - "manualColorGroups", "groupColors", "colorGroupVariables", "groupAssigned", "manualColor", - "legendToPlotRatio", "edgeLabels", "edgeLabelCex", "edgeLabelPosition" + "layout", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", "cut", "showDetails", "nodePalette", + "legendSpecificPlotNumber", "estimator", + "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", + "keepLayoutTheSame", "layoutX", "layoutY", + "manualColorGroups", "groupColors", "colorGroupVariables", "groupAssigned", "manualColor", + "legendToPlotRatio", "edgeLabels", "edgeLabelCex", "edgeLabelPosition" )) plotContainer[["structurePlotContainer"]] <- structurePlotContainer @@ -737,12 +811,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { title <- if (nGraphs == 1L) gettext("Edge Evidence Plot") else gettext("Edge Evidence Plots") evidencePlotContainer <- createJaspContainer(title = title, position = 3, dependencies = c("evidencePlot", - "layout", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", "cut", "showDetails", "nodePalette", - "legendSpecificPlotNumber", "edgeInclusion", "edgeExclusion", "edgeAbsence", - "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", - "keepLayoutTheSame", "layoutX", "layoutY", "edgeInclusionCriteria", - "manualColorGroups", "groupColors", "colorGroupVariables", "groupAssigned", "manualColor", - "legendToPlotRatio" + "layout", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", "cut", "showDetails", "nodePalette", + "legendSpecificPlotNumber", "edgeInclusion", "edgeExclusion", "edgeAbsence", + "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", + "keepLayoutTheSame", "layoutX", "layoutY", "edgeInclusionCriteria", + "manualColorGroups", "groupColors", "colorGroupVariables", "groupAssigned", "manualColor", + "legendToPlotRatio" )) plotContainer[["evidencePlotContainer"]] <- evidencePlotContainer @@ -1144,10 +1218,10 @@ centrality <- function(network, measures = c("closeness", "betweenness", "streng if (options[["credibilityInterval"]]) { # Compute centrality for each posterior sample: - for (i in seq_len(nrow(network$samplesPosterior))) { + for (i in seq_len(nrow(network$samplesPosterior[[1]]))) { # TODO: this should call centralityTable rather than centralityPlot - graph <- qgraph::centralityPlot(vectorToMatrix(network$samplesPosterior[i, ], as.numeric(nrow(network$estimates)), bycolumn = TRUE), + graph <- qgraph::centralityPlot(vectorToMatrix(network$samplesPosterior[[1]][i, ], as.numeric(nrow(network$estimates)), bycolumn = TRUE), include = measures, verbose = FALSE, print = FALSE, @@ -1158,7 +1232,7 @@ centrality <- function(network, measures = c("closeness", "betweenness", "streng # see https://github.com/jasp-stats/jasp-test-release/issues/2298 if (nrow(graph$data) != nrow(centralityOutput) && "Strength" %in% measures && - all(abs(network$samplesPosterior[i, ]) <= .Machine$double.eps)) { + all(abs(network$samplesPosterior[[1]][i, ]) <= .Machine$double.eps)) { idx <- centralityOutput$measure %in% graph$data$measure value <- numeric(nrow(centralityOutput)) diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index a8d4205..22eb853 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -27,10 +27,10 @@ Form VariablesForm { AvailableVariablesList { name: "allVariablesList" } - AssignedVariablesList { name: "variables"; title: qsTr("Dependent Variables"); allowedColumns: ["scale"]; id: networkVariables} + AssignedVariablesList { name: "variables"; title: qsTr("Dependent Variables"); allowedColumns: ["ordinal", "scale"]; allowTypeChange: true; id: networkVariables} AssignedVariablesList { name: "groupingVariable"; title: qsTr("Split"); singleVariable: true; allowedColumns: ["nominal"] } } - + DropDown { id: estimator @@ -39,7 +39,8 @@ Form Layout.columnSpan: 2 values: [ { value: "ggm", label: "ggm" }, - { value: "gcgm", label: "gcgm" } + { value: "gcgm", label: "gcgm" }, + { value: "omrf", label: "omrf" } ] } @@ -47,8 +48,8 @@ Form { title: qsTr("Plots") CheckBox { name: "networkPlot"; label: qsTr("Network plot") } - CheckBox { - name: "evidencePlot"; + CheckBox { + name: "evidencePlot"; label: qsTr("Edge evidence plot") IntegerField { name: "edgeInclusionCriteria"; @@ -61,12 +62,12 @@ Form CheckBox { name: "edgeExclusion"; label: qsTr("Evidence for exclusion"); checked: true } CheckBox { name: "edgeAbsence"; label: qsTr("Absence of evidence"); checked: true } } - CheckBox { - name: "centralityPlot"; label: qsTr("Centrality plot") - CheckBox { + CheckBox { + name: "centralityPlot"; label: qsTr("Centrality plot") + CheckBox { name: "credibilityInterval"; label: qsTr("Credibility interval 95%"); - checked: false + checked: false } } } @@ -75,7 +76,7 @@ Form { title: qsTr("Tables") CheckBox { name: "weightsMatrixTable"; label: qsTr("Weights matrix") } - CheckBox { + CheckBox { name: "edgeEvidenceTable"; label: qsTr("Edge evidence probability table") RadioButtonGroup { name: "evidenceType"; @@ -87,38 +88,100 @@ Form } CheckBox { name: "centralityTable"; label: qsTr("Centrality table") } } - - Section + + Section { title: qsTr("Sampling Options") Layout.columnSpan: 2 IntegerField { name: "burnin"; label: qsTr("Burn in: "); value: "5000" ; min: 0; max: iter.value / 2; fieldWidth: 100; id: burnin } IntegerField { name: "iter"; label: qsTr("Iterations: "); value: "10000" ; min: burnin.value * 2; fieldWidth: 100; id: iter } - + SetSeed{} } - Section - { - title: qsTr("Prior") - - FormulaField { name: "gprior"; label: qsTr("Prior edge inclusion (g prior): "); value: "0.5" ; min: 0.001; max: 1; Layout.columnSpan: 2 } - - DropDown - { - id: initialConfiguration - name: "initialConfiguration" - label: qsTr("Initial configuration prior edge inclusion (g start):") - Layout.columnSpan: 2 - values: [ - { value: "empty", label: "empty" }, - { value: "full", label: "full" } - ] - } +Section { + title: qsTr("Prior Specification") + Layout.fillWidth: true - IntegerField { name: "dfprior"; label: qsTr("Degrees of freedom of G-Wishart prior (df prior): "); value: "3" ; min: 3; Layout.columnSpan: 2 } + Column { + spacing: 15 + anchors.fill: parent - } + Group { + title: qsTr("Network/Edge Priors") + Layout.fillWidth: true + + Column { + spacing: 10 + Layout.fillWidth: true + + FormulaField { + name: "gprior" + label: qsTr("Prior edge inclusion probability:") + value: "0.5" + min: 0.001 + max: 1 + Layout.fillWidth: true + preferredWidth: 300 + } + + DropDown { + id: edgePrior + name: "edgePrior" + label: qsTr("Edge prior for the omrf:") + Layout.fillWidth: true + preferredWidth: 300 + values: [ + { value: "Bernoulli", label: "Bernoulli" }, + { value: "Beta-Bernoulli", label: "Beta-Bernoulli" }, + { value: "Stochastic-Block", label: "Stochastic-Block" } + ] + } + + DropDown { + id: initialConfiguration + name: "initialConfiguration" + label: qsTr("Initial configuration prior edge inclusion (for ggm and gcgm):") + Layout.fillWidth: true + preferredWidth: 300 + values: [ + { value: "empty", label: "empty" }, + { value: "full", label: "full" } + ] + } + } + } + + Group { + title: qsTr("Edge Weight Priors") + Layout.fillWidth: true + + Column { + spacing: 10 + Layout.fillWidth: true + + IntegerField { + name: "dfprior" + label: qsTr("Degrees of freedom of G-Wishart prior (for ggm and gcgm):") + value: 3 + min: 3 + Layout.fillWidth: true + preferredWidth: 300 + } + + FormulaField { + name: "interactionScale" + label: qsTr("Scale of the Cauchy distribution (for omrf):") + value: "2.5" + min: 0.1 + max: 10 + Layout.fillWidth: true + preferredWidth: 300 + } + } + } + } +} Section @@ -246,7 +309,7 @@ Form RadioButton { value: "inNodes"; label: qsTr("In plot"); checked: true } RadioButton { value: "inLegend"; label: qsTr("In legend") } } - + RadioButtonGroup { name: "legend" @@ -292,7 +355,7 @@ Form CheckBox { name: "expectedInfluence"; label: qsTr("Expected influence"); checked: true } } } - + Section { title: qsTr("Network structure selection") From 775dfabea6acbe70f412ab3e55261a643854d54b Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Wed, 6 Nov 2024 12:35:18 +0100 Subject: [PATCH 02/11] update renv --- DESCRIPTION | 2 +- NAMESPACE | 2 +- renv.lock | 2088 ++++++++++++++++++++++++++++++++++++++++----------- 3 files changed, 1646 insertions(+), 446 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d93af7c..65e6717 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -32,4 +32,4 @@ Remotes: jasp-stats/jaspBase, jasp-stats/jaspGraphs Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.1 +RoxygenNote: 7.3.2 diff --git a/NAMESPACE b/NAMESPACE index 5a38cb2..eff559d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,7 +1,7 @@ # Generated by roxygen2: do not edit by hand -export(NetworkAnalysis) export(BayesianNetworkAnalysis) +export(NetworkAnalysis) importFrom(jaspBase,.extractErrorMessage) importFrom(jaspBase,.hasErrors) importFrom(jaspBase,.quitAnalysis) diff --git a/renv.lock b/renv.lock index 7d0a1d3..6ac3d0b 100644 --- a/renv.lock +++ b/renv.lock @@ -13,315 +13,655 @@ "Package": "BDgraph", "Version": "2.73", "Source": "Repository", + "Repository": "CRAN", "Requirements": [ - 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mainContainer <- createJaspContainer(dependencies = c("variables", "groupingVariable", "estimator", + mainContainer <- createJaspContainer(dependencies = c("variables", "groupingVariable", "model", "burnin", "iter", "gprior", "dfprior")) jaspResults[["mainContainer"]] <- mainContainer } @@ -197,8 +197,8 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { for (nw in seq_along(dataset)) { #ADD CONDITIONAL STATEMENTS HERE FOR EACH OF THE THREE OPTIONS # When method = "gcgm" a vector with binary values is needed: - # if estimator is gcgm - if (options[["estimator"]] == "ggm") { + # if model is gcgm + if (options[["model"]] == "ggm") { # Estimate mixed network jaspBase::.setSeedJASP(options) @@ -236,13 +236,13 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { } - # if estimator is gcgm - if (options[["estimator"]] == "gcgm") { + # if model is gcgm + if (options[["model"]] == "gcgm") { nonContVariables <- c() for (var in options[["variables"]]) { # A 1 indicates noncontinuous variables: - if (length(levels(factor(dataset[[nw]][[var]]))) <= 8) { # if the number of categories is less than 8, then they are treated as non-cont. Does this make sense? + if (is.factor(dataset[[nw]][[var]])) { nonContVariables <- c(nonContVariables, 1) } else { nonContVariables <- c(nonContVariables, 0) @@ -285,12 +285,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { } - # if estimator is ordinal Markov random field - if (options[["estimator"]] == "omrf") { + # if model is ordinal Markov random field + if (options[["model"]] == "omrf") { for (var in options[["variables"]]) { # Check if variables are binary or ordinal: if (!is.factor(dataset[[nw]][[var]])) { - .quitAnalysis(gettext("Some of the variables you have entered for analysis are not binary or ordinal. Please make sure that all variables are binary or ordinal or change the estimator to gcgm.")) + .quitAnalysis(gettext("Some of the variables you have entered for analysis are not binary or ordinal. Please make sure that all variables are binary or ordinal or change the model to gcgm.")) } else { # Estimate mixed network jaspBase::.setSeedJASP(options) @@ -405,7 +405,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { mainContainer[["plotContainer"]] <- plotContainer } - .networkAnalysisNetworkPlot (plotContainer, network, options, method = "Bayesian") + .bayesianNetworkAnalysisNetworkPlot (plotContainer, network, options, method = "Bayesian") .bayesianNetworkAnalysisEvidencePlot (plotContainer, network, options) .bayesianNetworkAnalysisPosteriorStructurePlot (plotContainer, network, options) .bayesianNetworkAnalysisCentralityPlot (plotContainer, network, options) @@ -583,6 +583,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { ggplot2::labs(x = NULL, y = NULL, fill = NULL) if (options[["credibilityInterval"]]) { + g <- g + ggplot2::geom_errorbar(ggplot2::aes(x = posteriorMeans, xmin = lower, xmax = upper), size = .5, width = 0.4) } @@ -670,7 +671,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { structurePlotContainer <- createJaspContainer(title = title, dependencies = c("posteriorStructurePlot", "layout", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", "cut", "showDetails", "nodePalette", - "legendSpecificPlotNumber", "estimator", + "legendSpecificPlotNumber", "model", "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", "keepLayoutTheSame", "layoutX", "layoutY", "manualColorGroups", "groupColors", "colorGroupVariables", "groupAssigned", "manualColor", @@ -1080,6 +1081,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { } table$setData(TBcolumns) } + + # add footnote on the infinities only show this message of the evidence type is BF10 or BF01 + + if (options$evidenceType %in% c("BF10", "BF01")){ + table$addFootnote("Bayes factors with values of infinity indicate that the posterior inclusion probability is either 1 or 0, meaning there is overwhelming evidence for edge inclusion or exclusion, respectively.") + } mainContainer[["edgeEvidenceTable"]] <- table } @@ -1248,3 +1255,208 @@ centrality <- function(network, measures = c("closeness", "betweenness", "streng return(centralityOutput) } + + +# A copy of .networkAnalysisNetworkPlot since we changed "estimator" to "model" +# for now it is the same function, but I will tidy it up later, since it makes no sense to have conditions like +# "model === mgm" + +.bayesianNetworkAnalysisNetworkPlot <- function(plotContainer, network, options, method = "frequentist") { + + if (!is.null(plotContainer[["networkPlotContainer"]]) || !options[["networkPlot"]]) + return() + + allNetworks <- network[["network"]] + nGraphs <- length(allNetworks) + + # we use an empty container without a name if there is only 1 graph. This container is hidden from the output but it + # enables us to use the same code for a single network plot and for a collection of network plots. + title <- if (nGraphs == 1L) "" else gettext("Network Plots") + + networkPlotContainer <- createJaspContainer(title = title, position = 51, dependencies = c( + "layout", "edgePalette", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", + "maxEdgeStrength", "minEdgeStrength", "cut", "details", "nodePalette", + "legendSpecificPlotNumber", "mgmVariableTypeShown", + "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", + "layoutNotUpdated", "layoutX", "layoutY", "networkPlot", + "manualColorGroups", "color", "colorGroupVariables", "group", "manualColor", + "legendToPlotRatio", "edgeLabels", "edgeLabelSize", "edgeLabelPosition" + )) + plotContainer[["networkPlotContainer"]] <- networkPlotContainer + + if (is.null(network[["network"]]) || plotContainer$getError()) { + networkPlotContainer[["dummyPlot"]] <- createJaspPlot(title = gettext("Network Plot")) + return() + } + + if (method == "Bayesian") { + layout <- network[["layout"]] # calculated in .bayesianNetworkAnalysisRun() + } else { + layout <- network[["layout"]][["layout"]] # calculated in .networkAnalysisRun() + } + + # ensure minimum/ maximum makes sense or ignore these parameters. + # TODO: message in general table if they have been reset. + minE <- options[["minEdgeStrength"]] + maxE <- options[["maxEdgeStrength"]] + + if (minE == 0) + minE <- NULL + if (maxE == 0 || (!is.null(minE) && maxE <= minE)) + maxE <- NULL + + groups <- NULL + nodeColor <- NULL + allLegends <- rep(FALSE, nGraphs) # no legends + + if (length(options[["colorGroupVariables"]]) > 1L) { + + assignedGroup <- vapply(options[["colorGroupVariables"]], `[[`, character(1L), "group") + + if (length(unique(assignedGroup)) > 1L) { + + # user has defined groups and there are variables in the groups + groupNames <- vapply(options[["manualColorGroups"]], `[[`, character(1L), "name") + groupColors <- vapply(options[["manualColorGroups"]], `[[`, character(1L), "color") + + nGroups <- length(groupNames) + + idx <- match(assignedGroup, groupNames) + + groups <- vector("list", nGroups) + names(groups) <- groupNames + for (i in seq_len(nGroups)) + groups[[i]] <- which(idx == i) + + nonEmpty <- lengths(groups) > 0L + groups <- groups[nonEmpty] + + if (options[["manualColor"]]) + nodeColor <- groupColors[nonEmpty] + } + } + + # defaults + shape <- "circle" + edgeColor <- NULL + if (options[["model"]] == "mgm") { + + idx <- integer(length(options[["variables"]])) + nms <- c("mgmContinuousVariables", "mgmCategoricalVariables", "mgmCountVariables") + for (i in seq_along(nms)) + idx[options[["variables"]] %in% options[[nms[[i]]]]] <- i + # idx[i] is 1 if variable[i] %in% mgmContinuousVariables, 2 if in mgmCategoricalVariables, etc. + + ll <- lengths(options[c("mgmContinuousVariables", "mgmCategoricalVariables", "mgmCountVariables")]) + + if (options[["mgmVariableTypeShown"]] == "nodeShape") { + # gaussian, categorical, poisson + opts <- c("circle", "square", "triangle") + shape <- opts[idx] + + } else if (options[["mgmVariableTypeShown"]] == "nodeColor") { + # gaussian, categorical, poisson + opts <- c("#00a9e6", "#fb8b00", "#00ba63") + nodeColor <- opts[idx] + + } + } + + # TODO: footnote if legend off and nodenames used + if (options[["variableNamesShown"]] == "inNodes") { + nodeNames <- NULL + + if (method == "Bayesian") { + if (nGraphs == 1) { + labels <- colnames(allNetworks$Network$graph) + } else { + labels <- .unv(colnames(allNetworks$`1`$graph)) + } + } else { + labels <- .unv(allNetworks[[1]][["labels"]]) + } + + } else { + + if (method == "Bayesian") { + if (nGraphs == 1) { + nodeNames <- .unv(colnames(allNetworks$Network$graph)) + } else { + nodeNames <- .unv(colnames(allNetworks$`1`$graph)) + } + } else { + nodeNames <- .unv(allNetworks[[1]][["labels"]]) + } + + labels <- seq_along(nodeNames) + + } + + if (method == "Bayesian") labels <- decodeColNames(labels) + + if (options[["labelAbbreviation"]]) + labels <- base::abbreviate(labels, options[["labelAbbreviationLength"]]) + + # do we need to draw legends? + if (!is.null(groups) || !is.null(nodeNames)) { + if (options[["legend"]] == "allPlots") { + + allLegends <- rep(TRUE, nGraphs) + + } else if (options[["legend"]] == "specificPlot") { + + if (options[["legendSpecificPlotNumber"]] > nGraphs) { + + allLegends[nGraphs] <- TRUE + + } else if (options[["legendSpecificPlotNumber"]] < 1L) { + + allLegends[1L] <- TRUE + + } else { + + allLegends[options[["legendSpecificPlotNumber"]]] <- TRUE + + } + } + } + + names(allLegends) <- names(allNetworks) # allows indexing by name + + basePlotSize <- 320 + legendMultiplier <- options[["legendToPlotRatio"]] * basePlotSize + height <- setNames(rep(basePlotSize, nGraphs), names(allLegends)) + width <- basePlotSize + allLegends * legendMultiplier + for (v in names(allNetworks)) + networkPlotContainer[[v]] <- createJaspPlot(title = v, width = width[v], height = height[v]) + + jaspBase::.suppressGrDevice({ + + for (v in names(allNetworks)) { + + networkToPlot <- allNetworks[[v]] + if (options[["model"]] == "mgm") { + edgeColor <- networkToPlot[["results"]][["edgecolor"]] + if (is.null(edgeColor)) # compatability issues + edgeColor <- networkToPlot[["results"]][["pairwise"]][["edgecolor"]] + } + + legend <- allLegends[[v]] + networkPlotContainer[[v]]$plotObject <- .networkAnalysisOneNetworkPlot( + network = networkToPlot, + options = options, + minE = minE, + maxE = maxE, + layout = layout, + groups = groups, + labels = labels, + legend = legend, + shape = shape, + nodeColor = nodeColor, + edgeColor = edgeColor, + nodeNames = nodeNames, + method = method + ) + } + }) +} diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 22eb853..9724dc1 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -33,9 +33,9 @@ Form DropDown { - id: estimator - name: "estimator" - label: qsTr("Estimator") + id: model + name: "model" + label: qsTr("Model") Layout.columnSpan: 2 values: [ { value: "ggm", label: "ggm" }, From 46133a37dff9be2eef82b85a44b82282078032d8 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Thu, 7 Nov 2024 12:49:39 +0100 Subject: [PATCH 04/11] added more prior options and fixed the issue with not reading in some of the prior options properly. --- R/bayesiannetworkanalysis.R | 11 ++++- inst/qml/BayesianNetworkAnalysis.qml | 72 ++++++++++++++++++++++++++-- 2 files changed, 77 insertions(+), 6 deletions(-) diff --git a/R/bayesiannetworkanalysis.R b/R/bayesiannetworkanalysis.R index dabf222..f6e20d3 100644 --- a/R/bayesiannetworkanalysis.R +++ b/R/bayesiannetworkanalysis.R @@ -41,7 +41,9 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { mainContainer <- jaspResults[["mainContainer"]] if (is.null(mainContainer)) { mainContainer <- createJaspContainer(dependencies = c("variables", "groupingVariable", "model", - "burnin", "iter", "gprior", "dfprior")) + "burnin", "iter", "gprior", "dfprior", "initialConfiguration", + "edgePrior", "interactionScale", "beta_alpha", "beta_beta", + "dirichlet_alpha", "threshold_alpha", "threshold_beta")) jaspResults[["mainContainer"]] <- mainContainer } .bayesianNetworkAnalysisMainTableMeta(mainContainer, dataset, options) @@ -303,7 +305,12 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { burnin = as.numeric(options[["burnin"]]), inclusion_probability = as.numeric(options[["gprior"]]), interaction_scale = as.numeric(options[["interactionScale"]]), - edge_prior = as.character(options[["edgePrior"]]))) + edge_prior = as.character(options[["edgePrior"]]), + threshold_alpha = as.numeric(options[["threshold_alpha"]]), + threshold_beta = as.numeric(options[["threshold_beta"]]), + beta_bernoulli_alpha = as.numeric(options[["beta_alpha"]]), + beta_bernoulli_beta = as.numeric(options[["beta_beta"]]), + dirichlet_alpha = as.numeric(options[["dirichlet_alpha"]]))) } } diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 9724dc1..c424b71 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -108,7 +108,7 @@ Section { anchors.fill: parent Group { - title: qsTr("Network/Edge Priors") + title: qsTr("Network Structure (Edge) Priors") Layout.fillWidth: true Column { @@ -138,6 +138,33 @@ Section { ] } + FormulaField { + name: "beta_alpha" + label: qsTr("Shape parameter 1:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } + + FormulaField { + name: "beta_beta" + label: qsTr("Shape parameter 2:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } + + FormulaField { + name: "dirichlet_alpha" + label: qsTr("Concentration parameter:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } + DropDown { id: initialConfiguration name: "initialConfiguration" @@ -149,11 +176,30 @@ Section { { value: "full", label: "full" } ] } + + + FormulaField { + name: "beta_bernoulli_alpha" + label: qsTr("Shape parameter 1:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } + + FormulaField { + name: "beta_bernoulli_beta" + label: qsTr("Shape parameter 2:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } } } Group { - title: qsTr("Edge Weight Priors") + title: qsTr("Parameter Priors") Layout.fillWidth: true Column { @@ -171,10 +217,28 @@ Section { FormulaField { name: "interactionScale" - label: qsTr("Scale of the Cauchy distribution (for omrf):") + label: qsTr("Scale of the Cauchy distribution for the edge weights (for omrf):") value: "2.5" min: 0.1 - max: 10 + Layout.fillWidth: true + preferredWidth: 300 + } + + + FormulaField { + name: "threshold_alpha" + label: qsTr("Threshold Shape parameter 1:") + value: "1" + min: 0.001 + Layout.fillWidth: true + preferredWidth: 300 + } + + FormulaField { + name: "threshold_beta" + label: qsTr("Threshold shape parameter 2:") + value: "1" + min: 0.001 Layout.fillWidth: true preferredWidth: 300 } From 2ea84cb35df687885a7607f1bffb1750e9348b66 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Tue, 12 Nov 2024 11:18:12 +0100 Subject: [PATCH 05/11] updated the options for the priors; excluded the CrI intervals for the centrality measures when model = ggm || gcgm; added a note that the omrf is slow. --- R/bayesiannetworkanalysis.R | 7 +++ inst/qml/BayesianNetworkAnalysis.qml | 77 +++++++++++++--------------- 2 files changed, 42 insertions(+), 42 deletions(-) diff --git a/R/bayesiannetworkanalysis.R b/R/bayesiannetworkanalysis.R index f6e20d3..9a9437c 100644 --- a/R/bayesiannetworkanalysis.R +++ b/R/bayesiannetworkanalysis.R @@ -63,6 +63,13 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { tb$addColumnInfo(name = "nonZero", title = gettext("Number of non-zero edges"), type = "string") tb$addColumnInfo(name = "Sparsity", title = gettext("Sparsity"), type = "number") + + if (options[["model"]] == "omrf") { + + # add footnote + tb$addFootnote("The omrf model may require a substantial amount of time to complete. This time increases with the number of variables and the number of iterations.") + } + mainContainer[["generalTable"]] <- tb } return() diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index c424b71..1289b57 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -62,14 +62,16 @@ Form CheckBox { name: "edgeExclusion"; label: qsTr("Evidence for exclusion"); checked: true } CheckBox { name: "edgeAbsence"; label: qsTr("Absence of evidence"); checked: true } } - CheckBox { - name: "centralityPlot"; label: qsTr("Centrality plot") - CheckBox { - name: "credibilityInterval"; - label: qsTr("Credibility interval 95%"); - checked: false - } - } + CheckBox { + name: "centralityPlot" + label: qsTr("Centrality plot") + CheckBox { + name: "credibilityInterval" + label: qsTr("Credibility interval 95%") + checked: false + visible: model.currentValue === "omrf" // Show only when model is "omrf" + } +} } Group @@ -115,20 +117,10 @@ Section { spacing: 10 Layout.fillWidth: true - FormulaField { - name: "gprior" - label: qsTr("Prior edge inclusion probability:") - value: "0.5" - min: 0.001 - max: 1 - Layout.fillWidth: true - preferredWidth: 300 - } - - DropDown { + DropDown { id: edgePrior name: "edgePrior" - label: qsTr("Edge prior for the omrf:") + label: qsTr("Edge prior:") Layout.fillWidth: true preferredWidth: 300 values: [ @@ -136,6 +128,18 @@ Section { { value: "Beta-Bernoulli", label: "Beta-Bernoulli" }, { value: "Stochastic-Block", label: "Stochastic-Block" } ] + visible: model.currentValue === "omrf" + } + + FormulaField { + name: "gprior" + label: qsTr("Prior edge inclusion probability:") + value: "0.5" + min: 0.001 + max: 1 + Layout.fillWidth: true + preferredWidth: 300 + visible: (model.currentValue === "ggm" || model.currentValue === "gcgm") || (model.currentValue === "omrf" && edgePrior.currentValue === "Bernoulli") } FormulaField { @@ -145,6 +149,7 @@ Section { min: 0.001 Layout.fillWidth: true preferredWidth: 300 + visible: (model.currentValue === "omrf") && (edgePrior.currentValue === "Beta-Bernoulli" || edgePrior.currentValue === "Stochastic-Block") } FormulaField { @@ -154,6 +159,7 @@ Section { min: 0.001 Layout.fillWidth: true preferredWidth: 300 + visible: (model.currentValue === "omrf") && (edgePrior.currentValue === "Beta-Bernoulli" || edgePrior.currentValue === "Stochastic-Block") } FormulaField { @@ -163,37 +169,20 @@ Section { min: 0.001 Layout.fillWidth: true preferredWidth: 300 + visible: (model.currentValue === "omrf") && (edgePrior.currentValue === "Stochastic-Block") } DropDown { id: initialConfiguration name: "initialConfiguration" - label: qsTr("Initial configuration prior edge inclusion (for ggm and gcgm):") + label: qsTr("Initial configuration prior edge inclusion:") Layout.fillWidth: true preferredWidth: 300 values: [ { value: "empty", label: "empty" }, { value: "full", label: "full" } ] - } - - - FormulaField { - name: "beta_bernoulli_alpha" - label: qsTr("Shape parameter 1:") - value: "1" - min: 0.001 - Layout.fillWidth: true - preferredWidth: 300 - } - - FormulaField { - name: "beta_bernoulli_beta" - label: qsTr("Shape parameter 2:") - value: "1" - min: 0.001 - Layout.fillWidth: true - preferredWidth: 300 + visible: model.currentValue === "ggm" || model.currentValue === "gcgm" } } } @@ -208,20 +197,22 @@ Section { IntegerField { name: "dfprior" - label: qsTr("Degrees of freedom of G-Wishart prior (for ggm and gcgm):") + label: qsTr("Degrees of freedom of G-Wishart prior:") value: 3 min: 3 Layout.fillWidth: true preferredWidth: 300 + visible: model.currentValue === "ggm" || model.currentValue === "gcgm" } FormulaField { name: "interactionScale" - label: qsTr("Scale of the Cauchy distribution for the edge weights (for omrf):") + label: qsTr("Scale of the Cauchy distribution for the edge weights:") value: "2.5" min: 0.1 Layout.fillWidth: true preferredWidth: 300 + visible: model.currentValue === "omrf" } @@ -232,6 +223,7 @@ Section { min: 0.001 Layout.fillWidth: true preferredWidth: 300 + visible: model.currentValue === "omrf" } FormulaField { @@ -241,6 +233,7 @@ Section { min: 0.001 Layout.fillWidth: true preferredWidth: 300 + visible: model.currentValue === "omrf" } } } From 6825ea38b4bed3e05b6337168765ee41e64b45d6 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Thu, 14 Nov 2024 17:14:29 +0100 Subject: [PATCH 06/11] (Corrected) Adressing review comments round 1: (1) adjusted the .networkAnalysisNetworkPlot function to handle estimator/model based on the selected method; (2) corrected the silly loop when estimating the omrf; (3) deleted somestimating the omrf; (3) deleted some redundant comments; (4) changed the footnote under the table. Round 2 with the rest of the changes will follow soon. --- R/bayesiannetworkanalysis.R | 252 ++++-------------------------------- R/networkanalysis.R | 12 +- 2 files changed, 32 insertions(+), 232 deletions(-) diff --git a/R/bayesiannetworkanalysis.R b/R/bayesiannetworkanalysis.R index 9a9437c..b916cab 100644 --- a/R/bayesiannetworkanalysis.R +++ b/R/bayesiannetworkanalysis.R @@ -67,7 +67,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { if (options[["model"]] == "omrf") { # add footnote - tb$addFootnote("The omrf model may require a substantial amount of time to complete. This time increases with the number of variables and the number of iterations.") + tb$addFootnote(gettext("The Ordinal Markov Random Field may require a substantial amount of time to complete. This time increases with the number of variables and the number of iterations.")) } mainContainer[["generalTable"]] <- tb @@ -187,7 +187,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { .bayesianNetworkAnalysisComputeLayout <- function(networks, dataset, options) { - # Reformat networks to fit averageLayout: #CHANGES THE LAYOUT DEPENDING HOW MANY NETWORKS ARE ESIMTATED BASED ON SPLIT + # Reformat networks to fit averageLayout: weightMatrices <- list() for (i in seq_along(networks)) { weightMatrices[[i]] <- networks[[i]]$graph @@ -202,11 +202,8 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { networks <- vector("list", length(dataset)) - # For every dataset (given the splitting variable) estimate a network: KEEP THIS AS IS FOR NOW for (nw in seq_along(dataset)) { - #ADD CONDITIONAL STATEMENTS HERE FOR EACH OF THE THREE OPTIONS # When method = "gcgm" a vector with binary values is needed: - # if model is gcgm if (options[["model"]] == "ggm") { # Estimate mixed network @@ -300,27 +297,27 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { # Check if variables are binary or ordinal: if (!is.factor(dataset[[nw]][[var]])) { .quitAnalysis(gettext("Some of the variables you have entered for analysis are not binary or ordinal. Please make sure that all variables are binary or ordinal or change the model to gcgm.")) - } else { - # Estimate mixed network - jaspBase::.setSeedJASP(options) - easybgmFit <- try(easybgm::easybgm(data = as.data.frame(dataset[[nw]]), - type = "ordinal", - package = "bgms", - iter = as.numeric(options[["iter"]]), - save = TRUE, - centrality = FALSE, - burnin = as.numeric(options[["burnin"]]), - inclusion_probability = as.numeric(options[["gprior"]]), - interaction_scale = as.numeric(options[["interactionScale"]]), - edge_prior = as.character(options[["edgePrior"]]), - threshold_alpha = as.numeric(options[["threshold_alpha"]]), - threshold_beta = as.numeric(options[["threshold_beta"]]), - beta_bernoulli_alpha = as.numeric(options[["beta_alpha"]]), - beta_bernoulli_beta = as.numeric(options[["beta_beta"]]), - dirichlet_alpha = as.numeric(options[["dirichlet_alpha"]]))) - } } + # Estimate mixed network + jaspBase::.setSeedJASP(options) + easybgmFit <- try(easybgm::easybgm(data = as.data.frame(dataset[[nw]]), + type = "ordinal", + package = "bgms", + iter = as.numeric(options[["iter"]]), + save = TRUE, + centrality = FALSE, + burnin = as.numeric(options[["burnin"]]), + inclusion_probability = as.numeric(options[["gprior"]]), + interaction_scale = as.numeric(options[["interactionScale"]]), + edge_prior = as.character(options[["edgePrior"]]), + threshold_alpha = as.numeric(options[["threshold_alpha"]]), + threshold_beta = as.numeric(options[["threshold_beta"]]), + beta_bernoulli_alpha = as.numeric(options[["beta_alpha"]]), + beta_bernoulli_beta = as.numeric(options[["beta_beta"]]), + dirichlet_alpha = as.numeric(options[["dirichlet_alpha"]]))) + + if (isTryError(easybgmFit)) { message <- .extractErrorMessage(easybgmFit) @@ -419,7 +416,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { mainContainer[["plotContainer"]] <- plotContainer } - .bayesianNetworkAnalysisNetworkPlot (plotContainer, network, options, method = "Bayesian") + .networkAnalysisNetworkPlot (plotContainer, network, options, method = "Bayesian") .bayesianNetworkAnalysisEvidencePlot (plotContainer, network, options) .bayesianNetworkAnalysisPosteriorStructurePlot (plotContainer, network, options) .bayesianNetworkAnalysisCentralityPlot (plotContainer, network, options) @@ -1269,208 +1266,3 @@ centrality <- function(network, measures = c("closeness", "betweenness", "streng return(centralityOutput) } - - -# A copy of .networkAnalysisNetworkPlot since we changed "estimator" to "model" -# for now it is the same function, but I will tidy it up later, since it makes no sense to have conditions like -# "model === mgm" - -.bayesianNetworkAnalysisNetworkPlot <- function(plotContainer, network, options, method = "frequentist") { - - if (!is.null(plotContainer[["networkPlotContainer"]]) || !options[["networkPlot"]]) - return() - - allNetworks <- network[["network"]] - nGraphs <- length(allNetworks) - - # we use an empty container without a name if there is only 1 graph. This container is hidden from the output but it - # enables us to use the same code for a single network plot and for a collection of network plots. - title <- if (nGraphs == 1L) "" else gettext("Network Plots") - - networkPlotContainer <- createJaspContainer(title = title, position = 51, dependencies = c( - "layout", "edgePalette", "layoutSpringRepulsion", "edgeSize", "nodeSize", "colorNodesBy", - "maxEdgeStrength", "minEdgeStrength", "cut", "details", "nodePalette", - "legendSpecificPlotNumber", "mgmVariableTypeShown", - "labelScale", "labelSize", "labelAbbreviation", "labelAbbreviationLength", - "layoutNotUpdated", "layoutX", "layoutY", "networkPlot", - "manualColorGroups", "color", "colorGroupVariables", "group", "manualColor", - "legendToPlotRatio", "edgeLabels", "edgeLabelSize", "edgeLabelPosition" - )) - plotContainer[["networkPlotContainer"]] <- networkPlotContainer - - if (is.null(network[["network"]]) || plotContainer$getError()) { - networkPlotContainer[["dummyPlot"]] <- createJaspPlot(title = gettext("Network Plot")) - return() - } - - if (method == "Bayesian") { - layout <- network[["layout"]] # calculated in .bayesianNetworkAnalysisRun() - } else { - layout <- network[["layout"]][["layout"]] # calculated in .networkAnalysisRun() - } - - # ensure minimum/ maximum makes sense or ignore these parameters. - # TODO: message in general table if they have been reset. - minE <- options[["minEdgeStrength"]] - maxE <- options[["maxEdgeStrength"]] - - if (minE == 0) - minE <- NULL - if (maxE == 0 || (!is.null(minE) && maxE <= minE)) - maxE <- NULL - - groups <- NULL - nodeColor <- NULL - allLegends <- rep(FALSE, nGraphs) # no legends - - if (length(options[["colorGroupVariables"]]) > 1L) { - - assignedGroup <- vapply(options[["colorGroupVariables"]], `[[`, character(1L), "group") - - if (length(unique(assignedGroup)) > 1L) { - - # user has defined groups and there are variables in the groups - groupNames <- vapply(options[["manualColorGroups"]], `[[`, character(1L), "name") - groupColors <- vapply(options[["manualColorGroups"]], `[[`, character(1L), "color") - - nGroups <- length(groupNames) - - idx <- match(assignedGroup, groupNames) - - groups <- vector("list", nGroups) - names(groups) <- groupNames - for (i in seq_len(nGroups)) - groups[[i]] <- which(idx == i) - - nonEmpty <- lengths(groups) > 0L - groups <- groups[nonEmpty] - - if (options[["manualColor"]]) - nodeColor <- groupColors[nonEmpty] - } - } - - # defaults - shape <- "circle" - edgeColor <- NULL - if (options[["model"]] == "mgm") { - - idx <- integer(length(options[["variables"]])) - nms <- c("mgmContinuousVariables", "mgmCategoricalVariables", "mgmCountVariables") - for (i in seq_along(nms)) - idx[options[["variables"]] %in% options[[nms[[i]]]]] <- i - # idx[i] is 1 if variable[i] %in% mgmContinuousVariables, 2 if in mgmCategoricalVariables, etc. - - ll <- lengths(options[c("mgmContinuousVariables", "mgmCategoricalVariables", "mgmCountVariables")]) - - if (options[["mgmVariableTypeShown"]] == "nodeShape") { - # gaussian, categorical, poisson - opts <- c("circle", "square", "triangle") - shape <- opts[idx] - - } else if (options[["mgmVariableTypeShown"]] == "nodeColor") { - # gaussian, categorical, poisson - opts <- c("#00a9e6", "#fb8b00", "#00ba63") - nodeColor <- opts[idx] - - } - } - - # TODO: footnote if legend off and nodenames used - if (options[["variableNamesShown"]] == "inNodes") { - nodeNames <- NULL - - if (method == "Bayesian") { - if (nGraphs == 1) { - labels <- colnames(allNetworks$Network$graph) - } else { - labels <- .unv(colnames(allNetworks$`1`$graph)) - } - } else { - labels <- .unv(allNetworks[[1]][["labels"]]) - } - - } else { - - if (method == "Bayesian") { - if (nGraphs == 1) { - nodeNames <- .unv(colnames(allNetworks$Network$graph)) - } else { - nodeNames <- .unv(colnames(allNetworks$`1`$graph)) - } - } else { - nodeNames <- .unv(allNetworks[[1]][["labels"]]) - } - - labels <- seq_along(nodeNames) - - } - - if (method == "Bayesian") labels <- decodeColNames(labels) - - if (options[["labelAbbreviation"]]) - labels <- base::abbreviate(labels, options[["labelAbbreviationLength"]]) - - # do we need to draw legends? - if (!is.null(groups) || !is.null(nodeNames)) { - if (options[["legend"]] == "allPlots") { - - allLegends <- rep(TRUE, nGraphs) - - } else if (options[["legend"]] == "specificPlot") { - - if (options[["legendSpecificPlotNumber"]] > nGraphs) { - - allLegends[nGraphs] <- TRUE - - } else if (options[["legendSpecificPlotNumber"]] < 1L) { - - allLegends[1L] <- TRUE - - } else { - - allLegends[options[["legendSpecificPlotNumber"]]] <- TRUE - - } - } - } - - names(allLegends) <- names(allNetworks) # allows indexing by name - - basePlotSize <- 320 - legendMultiplier <- options[["legendToPlotRatio"]] * basePlotSize - height <- setNames(rep(basePlotSize, nGraphs), names(allLegends)) - width <- basePlotSize + allLegends * legendMultiplier - for (v in names(allNetworks)) - networkPlotContainer[[v]] <- createJaspPlot(title = v, width = width[v], height = height[v]) - - jaspBase::.suppressGrDevice({ - - for (v in names(allNetworks)) { - - networkToPlot <- allNetworks[[v]] - if (options[["model"]] == "mgm") { - edgeColor <- networkToPlot[["results"]][["edgecolor"]] - if (is.null(edgeColor)) # compatability issues - edgeColor <- networkToPlot[["results"]][["pairwise"]][["edgecolor"]] - } - - legend <- allLegends[[v]] - networkPlotContainer[[v]]$plotObject <- .networkAnalysisOneNetworkPlot( - network = networkToPlot, - options = options, - minE = minE, - maxE = maxE, - layout = layout, - groups = groups, - labels = labels, - legend = legend, - shape = shape, - nodeColor = nodeColor, - edgeColor = edgeColor, - nodeNames = nodeNames, - method = method - ) - } - }) -} diff --git a/R/networkanalysis.R b/R/networkanalysis.R index a95e56a..40fa2a0 100644 --- a/R/networkanalysis.R +++ b/R/networkanalysis.R @@ -671,6 +671,14 @@ NetworkAnalysis <- function(jaspResults, dataset, options) { .networkAnalysisNetworkPlot <- function(plotContainer, network, options, method = "frequentist") { + + # Adjust options based on method + if (method == "frequentist") { + estimator <- options[["estimator"]] + } else if (method == "Bayesian") { + model <- options[["model"]] + } + if (!is.null(plotContainer[["networkPlotContainer"]]) || !options[["networkPlot"]]) return() @@ -747,7 +755,7 @@ NetworkAnalysis <- function(jaspResults, dataset, options) { # defaults shape <- "circle" edgeColor <- NULL - if (options[["estimator"]] == "mgm") { + if (method == "frequentist" && estimator == "mgm") { idx <- integer(length(options[["variables"]])) nms <- c("mgmContinuousVariables", "mgmCategoricalVariables", "mgmCountVariables") @@ -843,7 +851,7 @@ NetworkAnalysis <- function(jaspResults, dataset, options) { for (v in names(allNetworks)) { networkToPlot <- allNetworks[[v]] - if (options[["estimator"]] == "mgm") { + if (method == "frequentist" && estimator == "mgm") { edgeColor <- networkToPlot[["results"]][["edgecolor"]] if (is.null(edgeColor)) # compatability issues edgeColor <- networkToPlot[["results"]][["pairwise"]][["edgecolor"]] From 452e3cad264ba2bec0683ce63767b8b0619f96ab Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Mon, 18 Nov 2024 10:50:47 +0100 Subject: [PATCH 07/11] Adressing review comments round 2: implemented camelCase consistently, adjusted the arguments in functions, and some other small fixes. --- R/bayesiannetworkanalysis.R | 61 ++++++++++++------------ inst/qml/BayesianNetworkAnalysis.qml | 69 ++++++++++++++-------------- 2 files changed, 64 insertions(+), 66 deletions(-) diff --git a/R/bayesiannetworkanalysis.R b/R/bayesiannetworkanalysis.R index b916cab..2ebef00 100644 --- a/R/bayesiannetworkanalysis.R +++ b/R/bayesiannetworkanalysis.R @@ -41,9 +41,9 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { mainContainer <- jaspResults[["mainContainer"]] if (is.null(mainContainer)) { mainContainer <- createJaspContainer(dependencies = c("variables", "groupingVariable", "model", - "burnin", "iter", "gprior", "dfprior", "initialConfiguration", - "edgePrior", "interactionScale", "beta_alpha", "beta_beta", - "dirichlet_alpha", "threshold_alpha", "threshold_beta")) + "burnin", "iter", "gPrior", "dfPrior", "initialConfiguration", + "edgePrior", "interactionScale", "betaAlpha", "betaBeta", + "dirichletAlpha", "thresholdAlpha", "thresholdBeta")) jaspResults[["mainContainer"]] <- mainContainer } .bayesianNetworkAnalysisMainTableMeta(mainContainer, dataset, options) @@ -205,19 +205,18 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { for (nw in seq_along(dataset)) { # When method = "gcgm" a vector with binary values is needed: if (options[["model"]] == "ggm") { - - # Estimate mixed network + # Estimate network jaspBase::.setSeedJASP(options) - easybgmFit <- try(easybgm::easybgm(data = as.data.frame(apply(dataset[[nw]], 2, as.numeric)), + easybgmFit <- try(easybgm::easybgm(data = apply(dataset[[nw]], 2, as.numeric), type = "continuous", package = "BDgraph" , - iter = as.numeric(options[["iter"]]), + iter = options[["iter"]], save = TRUE, centrality = FALSE, - burnin = as.numeric(options[["burnin"]]), + burnin = options[["burnin"]], g.start = options[["initialConfiguration"]], - df.prior = as.numeric(options[["dfprior"]]), - g.prior = as.numeric(options[["gprior"]]))) + df.prior = options[["dfPrior"]], + g.prior = options[["gPrior"]])) if (isTryError(easybgmFit)) { message <- .extractErrorMessage(easybgmFit) @@ -248,25 +247,25 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { for (var in options[["variables"]]) { # A 1 indicates noncontinuous variables: - if (is.factor(dataset[[nw]][[var]])) { + if (is.factor(dataset[[nw]][[var]])) { nonContVariables <- c(nonContVariables, 1) } else { nonContVariables <- c(nonContVariables, 0) } } - # Estimate mixed network + # Estimate network jaspBase::.setSeedJASP(options) - easybgmFit <- try(easybgm::easybgm(data = as.data.frame(apply(dataset[[nw]], 2, as.numeric)), + easybgmFit <- try(easybgm::easybgm(data = apply(dataset[[nw]], 2, as.numeric), type = "mixed", package = "BDgraph", not_cont = nonContVariables, - iter = as.numeric(options[["iter"]]), + iter = options[["iter"]], save = TRUE, centrality = FALSE, - burnin = as.numeric(options[["burnin"]]), + burnin = options[["burnin"]], g.start = options[["initialConfiguration"]], - df.prior = as.numeric(options[["dfprior"]]), - g.prior = as.numeric(options[["gprior"]]))) + df.prior = options[["dfPrior"]], + g.prior = options[["gPrior"]])) if (isTryError(easybgmFit)) { @@ -295,27 +294,27 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { if (options[["model"]] == "omrf") { for (var in options[["variables"]]) { # Check if variables are binary or ordinal: - if (!is.factor(dataset[[nw]][[var]])) { + if (!is.factor(dataset[[nw]][[var]])) { .quitAnalysis(gettext("Some of the variables you have entered for analysis are not binary or ordinal. Please make sure that all variables are binary or ordinal or change the model to gcgm.")) } } - # Estimate mixed network + # Estimate network jaspBase::.setSeedJASP(options) - easybgmFit <- try(easybgm::easybgm(data = as.data.frame(dataset[[nw]]), + easybgmFit <- try(easybgm::easybgm(data = dataset[[nw]], type = "ordinal", package = "bgms", - iter = as.numeric(options[["iter"]]), + iter = options[["iter"]], save = TRUE, centrality = FALSE, - burnin = as.numeric(options[["burnin"]]), - inclusion_probability = as.numeric(options[["gprior"]]), - interaction_scale = as.numeric(options[["interactionScale"]]), - edge_prior = as.character(options[["edgePrior"]]), - threshold_alpha = as.numeric(options[["threshold_alpha"]]), - threshold_beta = as.numeric(options[["threshold_beta"]]), - beta_bernoulli_alpha = as.numeric(options[["beta_alpha"]]), - beta_bernoulli_beta = as.numeric(options[["beta_beta"]]), - dirichlet_alpha = as.numeric(options[["dirichlet_alpha"]]))) + burnin = options[["burnin"]], + inclusion_probability = options[["gPrior"]], + interaction_scale = options[["interactionScale"]], + edge_prior = options[["edgePrior"]], + threshold_alpha = options[["thresholdAlpha"]], + threshold_beta = options[["thresholdBeta"]], + beta_bernoulli_alpha = options[["betaAlpha"]], + beta_bernoulli_beta = options[["betaBeta"]], + dirichlet_alpha = options[["dirichletAlpha"]])) @@ -1096,7 +1095,7 @@ BayesianNetworkAnalysis <- function(jaspResults, dataset, options) { # add footnote on the infinities only show this message of the evidence type is BF10 or BF01 if (options$evidenceType %in% c("BF10", "BF01")){ - table$addFootnote("Bayes factors with values of infinity indicate that the posterior inclusion probability is either 1 or 0, meaning there is overwhelming evidence for edge inclusion or exclusion, respectively.") + table$addFootnote("Bayes factors with values of infinity indicate that the estimated posterior inclusion probability is either 1 or 0. Please see the help file for more information.") } mainContainer[["edgeEvidenceTable"]] <- table } diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 1289b57..8e17643 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -95,16 +95,15 @@ Form { title: qsTr("Sampling Options") Layout.columnSpan: 2 - IntegerField { name: "burnin"; label: qsTr("Burn in: "); value: "5000" ; min: 0; max: iter.value / 2; fieldWidth: 100; id: burnin } - IntegerField { name: "iter"; label: qsTr("Iterations: "); value: "10000" ; min: burnin.value * 2; fieldWidth: 100; id: iter } + IntegerField { name: "burnin"; label: qsTr("Burn in: "); value: 1000 ; min: 0; max: iter.value / 2; fieldWidth: 100; id: burnin } + IntegerField { name: "iter"; label: qsTr("Iterations: "); value: 10000 ; min: burnin.value * 2; fieldWidth: 100; id: iter } SetSeed{} } -Section { + Section { title: qsTr("Prior Specification") Layout.fillWidth: true - Column { spacing: 15 anchors.fill: parent @@ -126,46 +125,46 @@ Section { values: [ { value: "Bernoulli", label: "Bernoulli" }, { value: "Beta-Bernoulli", label: "Beta-Bernoulli" }, - { value: "Stochastic-Block", label: "Stochastic-Block" } + { value: "Stochastic-Block", label: "Stochastic Block" } ] visible: model.currentValue === "omrf" } - FormulaField { - name: "gprior" - label: qsTr("Prior edge inclusion probability:") - value: "0.5" - min: 0.001 - max: 1 - Layout.fillWidth: true - preferredWidth: 300 - visible: (model.currentValue === "ggm" || model.currentValue === "gcgm") || (model.currentValue === "omrf" && edgePrior.currentValue === "Bernoulli") - } - - FormulaField { - name: "beta_alpha" + DoubleField { + name: "gPrior" + label: qsTr("Prior edge inclusion probability:") + value: 0.5 + min: 0.001 + max: 1 + Layout.fillWidth: true + preferredWidth: 300 + visible: (model.currentValue === "ggm" || model.currentValue === "gcgm") || (model.currentValue === "omrf" && edgePrior.currentValue === "Bernoulli") + } + + DoubleField { + name: "betaAlpha" label: qsTr("Shape parameter 1:") - value: "1" + value: 1 min: 0.001 Layout.fillWidth: true preferredWidth: 300 visible: (model.currentValue === "omrf") && (edgePrior.currentValue === "Beta-Bernoulli" || edgePrior.currentValue === "Stochastic-Block") } - FormulaField { - name: "beta_beta" + DoubleField { + name: "betaBeta" label: qsTr("Shape parameter 2:") - value: "1" + value: 1 min: 0.001 Layout.fillWidth: true preferredWidth: 300 visible: (model.currentValue === "omrf") && (edgePrior.currentValue === "Beta-Bernoulli" || edgePrior.currentValue === "Stochastic-Block") } - FormulaField { - name: "dirichlet_alpha" + DoubleField { + name: "dirichletAlpha" label: qsTr("Concentration parameter:") - value: "1" + value: 1 min: 0.001 Layout.fillWidth: true preferredWidth: 300 @@ -196,7 +195,7 @@ Section { Layout.fillWidth: true IntegerField { - name: "dfprior" + name: "dfPrior" label: qsTr("Degrees of freedom of G-Wishart prior:") value: 3 min: 3 @@ -205,10 +204,10 @@ Section { visible: model.currentValue === "ggm" || model.currentValue === "gcgm" } - FormulaField { + DoubleField { name: "interactionScale" label: qsTr("Scale of the Cauchy distribution for the edge weights:") - value: "2.5" + value: 2.5 min: 0.1 Layout.fillWidth: true preferredWidth: 300 @@ -216,20 +215,20 @@ Section { } - FormulaField { - name: "threshold_alpha" - label: qsTr("Threshold Shape parameter 1:") - value: "1" + DoubleField { + name: "thresholdAlpha" + label: qsTr("Threshold shape parameter 1:") + value: 1 min: 0.001 Layout.fillWidth: true preferredWidth: 300 visible: model.currentValue === "omrf" } - FormulaField { - name: "threshold_beta" + DoubleField { + name: "thresholdBeta" label: qsTr("Threshold shape parameter 2:") - value: "1" + value: 1 min: 0.001 Layout.fillWidth: true preferredWidth: 300 From c4dc529e2c06eab9afe7924b3ba81d2178407a7a Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Tue, 19 Nov 2024 14:16:56 +0100 Subject: [PATCH 08/11] made the model names more informative --- inst/qml/BayesianNetworkAnalysis.qml | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 8e17643..1da3523 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -24,11 +24,18 @@ import JASP.Widgets 1.0 Form { - VariablesForm +VariablesForm { AvailableVariablesList { name: "allVariablesList" } - AssignedVariablesList { name: "variables"; title: qsTr("Dependent Variables"); allowedColumns: ["ordinal", "scale"]; allowTypeChange: true; id: networkVariables} - AssignedVariablesList { name: "groupingVariable"; title: qsTr("Split"); singleVariable: true; allowedColumns: ["nominal"] } + AssignedVariablesList { name: "variables"; + title: qsTr("Dependent Variables"); + allowedColumns: ["ordinal", "scale"]; + allowTypeChange: true; + id: networkVariables} + AssignedVariablesList { name: "groupingVariable"; + title: qsTr("Split"); + singleVariable: true; + allowedColumns: ["nominal"] } } DropDown @@ -38,9 +45,9 @@ Form label: qsTr("Model") Layout.columnSpan: 2 values: [ - { value: "ggm", label: "ggm" }, - { value: "gcgm", label: "gcgm" }, - { value: "omrf", label: "omrf" } + { value: "ggm", label: "ggm (continuous)" }, + { value: "gcgm", label: "gcgm (mixed)" }, + { value: "omrf", label: "omrf (binary/ordinal)" } ] } @@ -70,8 +77,8 @@ Form label: qsTr("Credibility interval 95%") checked: false visible: model.currentValue === "omrf" // Show only when model is "omrf" - } -} + } + } } Group From 5f333dae7a50da6e5a23efa631bda76c55f90f21 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Fri, 22 Nov 2024 17:26:48 +0100 Subject: [PATCH 09/11] Updating renv.lock --- renv.lock | 2109 ++++++++++++----------------------------------------- 1 file changed, 455 insertions(+), 1654 deletions(-) diff --git a/renv.lock b/renv.lock index 6ac3d0b..eeba594 100644 --- a/renv.lock +++ b/renv.lock @@ -1,6 +1,7 @@ + { "R": { - "Version": "4.4.1", + "Version": "4.4.2", "Repositories": [ { "Name": "CRAN", @@ -13,655 +14,315 @@ "Package": "BDgraph", "Version": "2.73", "Source": "Repository", - "Repository": "CRAN", "Requirements": [ - "ggplot2", "igraph", - "pROC" - ], - "Hash": "6b03ac69d6246adfec6f87fff9b0150c" - }, - "BFpack": { - "Package": "BFpack", - "Version": "1.3.0", - "Source": "Repository", - "Repository": "CRAN", - "Requirements": [ - "Bergm", - "MASS", - "QRM", - "R", - "bain", - "ergm", - "extraDistr", - "lme4", - "mvtnorm", - "pracma", - "sandwich", - "stats", - 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"Repository": "CRAN", "Requirements": [ + "methods", "RColorBrewer", - "Rcpp", - "methods" - ], - "Hash": "dddb6538141ed1e2435cb63c6c748d16" + "Rcpp" + ] }, "xfun": { "Package": "xfun", "Version": "0.49", "Source": "Repository", - "Repository": "CRAN", "Requirements": [ "R", "grDevices", "stats", "tools" - ], - "Hash": "8687398773806cfff9401a2feca96298" + ] }, "xml2": { "Package": "xml2", "Version": "1.3.6", "Source": "Repository", - "Repository": "CRAN", "Requirements": [ "R", "cli", "methods", "rlang" - ], - "Hash": "1d0336142f4cd25d8d23cd3ba7a8fb61" + ] }, "yaml": { "Package": "yaml", "Version": "2.3.10", "Source": "Repository", - "Repository": "CRAN", - "Hash": "51dab85c6c98e50a18d7551e9d49f76c" + "Requirements": [] }, "zip": { "Package": "zip", "Version": "2.3.1", "Source": "Repository", - "Repository": "CRAN", - "Hash": "fcc4bd8e6da2d2011eb64a5e5cc685ab" - }, - "zoo": { - "Package": "zoo", - "Version": "1.8-12", - "Source": "Repository", - "Repository": "CRAN", - "Requirements": [ - "R", - "grDevices", - "graphics", - "lattice", - "stats", - "utils" - ], - "Hash": "5c715954112b45499fb1dadc6ee6ee3e" + "Requirements": [] } } } From cf7ef3088dc269df90b40c8896d8f6e8880da2f6 Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Thu, 28 Nov 2024 16:05:59 +0100 Subject: [PATCH 10/11] resolve conflict --- inst/qml/BayesianNetworkAnalysis.qml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 1da3523..4d2c25e 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -70,8 +70,7 @@ VariablesForm CheckBox { name: "edgeAbsence"; label: qsTr("Absence of evidence"); checked: true } } CheckBox { - name: "centralityPlot" - label: qsTr("Centrality plot") + name: "centralityPlot"; id: centralityPlot; label: qsTr("Centrality plot") CheckBox { name: "credibilityInterval" label: qsTr("Credibility interval 95%") From 86ef220b5e7d9eac2093729674e5cc5cf40c840c Mon Sep 17 00:00:00 2001 From: Nikola Sekulovski Date: Thu, 28 Nov 2024 16:17:41 +0100 Subject: [PATCH 11/11] resolving conflict: second try --- inst/qml/BayesianNetworkAnalysis.qml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/inst/qml/BayesianNetworkAnalysis.qml b/inst/qml/BayesianNetworkAnalysis.qml index 4d2c25e..acfab80 100644 --- a/inst/qml/BayesianNetworkAnalysis.qml +++ b/inst/qml/BayesianNetworkAnalysis.qml @@ -69,15 +69,15 @@ VariablesForm CheckBox { name: "edgeExclusion"; label: qsTr("Evidence for exclusion"); checked: true } CheckBox { name: "edgeAbsence"; label: qsTr("Absence of evidence"); checked: true } } - CheckBox { - name: "centralityPlot"; id: centralityPlot; label: qsTr("Centrality plot") - CheckBox { - name: "credibilityInterval" - label: qsTr("Credibility interval 95%") - checked: false - visible: model.currentValue === "omrf" // Show only when model is "omrf" + CheckBox { + name: "centralityPlot"; id: centralityPlot; label: qsTr("Centrality plot") + CheckBox { + name: "credibilityInterval"; + label: qsTr("Credibility interval 95%"); + checked: false; + visible: model.currentValue === "omrf"; // Show only when model is "omrf" } - } + } } Group