Nothing
ShowStructure <- function(module = c("ALL", "DM", "MF", "MS", "NA"), description = TRUE, plot = TRUE) {
if (!is.logical(description)) stop(sQuote(description), " is not a logical value")
if (!is.logical(plot)) stop(sQuote(plot), " is not a logical value")
if (!description & !plot) stop(sQuote("description"), " and ", sQuote("plot"), " are both equal to FALSE")
module <- match.arg(module)
cglasso.graph <- graph_from_literal("datacggm" -- "rcggm",
"datacggm" -- "is.datacggm",
"datacggm" -- "dim.datacggm",
"datacggm" -- "nobs/nresp/npred",
"datacggm" -- "rowNames/colNames",
"datacggm" -- "dimnames.datacggm",
"datacggm" -- "print.datacggm",
"datacggm" -- "summary.datacggm",
"datacggm" -- "getMatrix",
"datacggm" -- "ColMeans/ColVars",
"datacggm" -- "lower/upper",
"datacggm" -- "event",
"datacggm" -- "hist.datacggm",
"datacggm" -- "qqcnorm",
"datacggm" -- "cglasso",
"cglasso" -- "print.cglasso",
"cglasso" -- "plot.cglasso",
"cglasso" -- "coef.cglasso",
"cglasso" -- "fitted.cglasso",
"cglasso" -- "residuals.cglasso",
"cglasso" -- "QFun",
"cglasso" -- "AIC.cglasso/BIC.cglasso",
"cglasso" -- "summary.cglasso",
"cglasso" -- "select_cglasso",
"cglasso" -- "predict.cglasso",
"cglasso" -- "impute",
"cglasso" -- "cggm",
"cglasso" -- "to_graph",
"cggm" -- "print.cggm",
"cggm" -- "coef.cglasso",
"cggm" -- "fitted.cglasso",
"cggm" -- "residuals.cglasso",
"cggm" -- "predict.cggm",
"cggm" -- "impute",
"cggm" -- "QFun",
"cggm" -- "plot.cggm",
"cggm" -- "AIC.cglasso/BIC.cglasso",
"cggm" -- "summary.cglasso",
"cggm" -- "to_graph",
"QFun" -- "print.QFun",
"QFun" -- "AIC.cglasso/BIC.cglasso",
"AIC.cglasso/BIC.cglasso" -- "print.GoF",
"AIC.cglasso/BIC.cglasso" -- "plot.GoF",
"AIC.cglasso/BIC.cglasso" -- "summary.cglasso",
"AIC.cglasso/BIC.cglasso" -- "select_cglasso",
"AIC.cglasso/BIC.cglasso" -- "plot.cglasso",
"to_graph" -- "is.cglasso2igraph",
"to_graph" -- "getGraph",
"to_graph" -- "print.cglasso2igraph",
"to_graph" -- "plot.cglasso2igraph")
data.manipulation <- list(
"datacggm" = "Create a Dataset from a Conditional Gaussian Graphical", " Model with Censored and/or Missing Values",
"is.datacggm" = "Is an Object of Class datacggm?",
"print.datacggm" = "Print Method for a datacggm Object",
"summary.datacggm" = "Summarizing Objects of Class datacggm",
"dim.datacggm" = "Dimensions of a datacggm Object",
"nobs/nresp/npred" = "Extract the Number of Observations/Responses/Predictors", " from a datacggm Object",
"dimnames.datacggm" = "Dimnames of a datacggm Object",
"rowNames/colNames" = "Row and Column Names of a datacggm Object",
"getMatrix" = "Retrieve Matrices Y and X from a datacggm Object",
"event" = "Status Indicator Matrix from a datacggm Object",
"lower/upper" = "Lower and Upper Limits from a datacggm Object",
"ColMeans/ColVars" = "Form Column Means and Vars of a datacggm Object",
"rcggm" = "Simulate from a Conditional Gaussian Graphical Model", " with Censored and/or Missing Values",
"hist.datacggm" = "Histogram for a datacggm Object",
"qqcnorm" = "Quantile-Quantile Plots for a datacggm Object")
class(data.manipulation) <- "simple.list"
model.fitting <- list(
"cglasso" = "Conditional Graphical Lasso Estimator",
"print.cglasso" = "Print Method for a cglasso Object",
"plot.cglasso" = "Plot Method for a cglasso Object",
"coef.cglasso" = "Extract Model Coefficients",
"fitted.cglasso" = "Extract Model Fitted Values",
"residuals.cglasso" = "Extract Model Residuals",
"predict.cglasso" = "Predict Method for cglasso Fits",
"impute" = "Imputation of Missing and Censored Values",
"cggm" = "Post-Hoc Maximum Likelihood Refitting", " of a Conditional Graphical Lasso",
"print.cggm" = "Print Method for a cggm Object",
"plot.cggm" = "Plot Method for a cggm Object",
"predict.cggm" = "Predict Method for cggm Fits")
class(model.fitting) <- "simple.list"
model.selection <- list(
"QFun" = "Extract Q-Function",
"print.QFun" = "Print Method for a QFun Object",
"AIC.cglasso/BIC.cglasso" = "Goodness-of-fit Functions",
"print.GoF" = "Print Method for a GoF Object",
"summary.cglasso" = "Summarizing cglasso and cggm Fits",
"plot.GoF" = "Plot Method for a GoF Object",
"select_cglasso" = "Model Selection for Conditional", " Graphical Lasso Estimator")
class(model.selection) <- "simple.list"
network.analysis <- list(
"to_graph" = "Create Graphs from cglasso or cggm Objects",
"is.cglasso2igraph" = "Is an Object of Class cglasso2igraph?",
"print.cglasso2igraph" = "Print Method for a cglasso2igraph Object",
"getGraph" = "Retrieve Graphs from a cglasso2igraph Object",
"plot.cglasso2igraph" = "Plot Method for a cglasso2igraph Object")
class(network.analysis) <- "simple.list"
cglasso.description <- list(
"Data Manipulation" = data.manipulation,
"Model Fitting" = model.fitting,
"Model Selection" = model.selection,
"Network Analysis" = network.analysis)
# n <- length(data.manipulation) + length(model.fitting) + length(model.selection) + length(network.analysis)
data.manipulation.nm <- setdiff(names(data.manipulation), "")
model.fitting.nm <- setdiff(names(model.fitting), "")
model.selection.nm <- setdiff(names(model.selection), "")
network.analysis.nm <- setdiff(names(network.analysis), "")
n <- length(data.manipulation.nm) + length(model.fitting.nm) + length(model.selection.nm) + length(network.analysis.nm)
V(cglasso.graph)$size <- 2
V(cglasso.graph)$label.cex <- 0.8
V(cglasso.graph)$label.dist <- 0.9
V(cglasso.graph)[data.manipulation.nm]$label.color <- "red4"
V(cglasso.graph)[model.fitting.nm]$label.color <- "blue4"
V(cglasso.graph)[model.selection.nm]$label.color <- "darkgoldenrod4"
V(cglasso.graph)[network.analysis.nm]$label.color <- "darkolivegreen"
V(cglasso.graph)[data.manipulation.nm]$color <- "red4"
V(cglasso.graph)[model.fitting.nm]$color <- "blue4"
V(cglasso.graph)[model.selection.nm]$color <- "darkgoldenrod4"
V(cglasso.graph)[network.analysis.nm]$color <- "darkolivegreen"
V(cglasso.graph)[data.manipulation.nm]$frame.color <- "red4"
V(cglasso.graph)[model.fitting.nm]$frame.color <- "blue4"
V(cglasso.graph)[model.selection.nm]$frame.color <- "darkgoldenrod4"
V(cglasso.graph)[network.analysis.nm]$frame.color <- "darkolivegreen"
V(cglasso.graph)$label.degree <- rep(- pi / 4, n)
# data manipulation module
V(cglasso.graph)["datacggm"]$label.degree <- - 0.1
V(cglasso.graph)["datacggm"]$label.dist <- + 2.1
V(cglasso.graph)["is.datacggm"]$label.degree <- 0
V(cglasso.graph)["is.datacggm"]$label.dist <- - 2.4
V(cglasso.graph)["dimnames.datacggm"]$label.degree <- 0
V(cglasso.graph)["dimnames.datacggm"]$label.dist <- 3.7
V(cglasso.graph)["print.datacggm"]$label.degree <- 0
V(cglasso.graph)["print.datacggm"]$label.dist <- 3
V(cglasso.graph)["nobs/nresp/npred"]$label.degree <- 0
V(cglasso.graph)["nobs/nresp/npred"]$label.dist <- 3.5
V(cglasso.graph)["summary.datacggm"]$label.degree <- 0
V(cglasso.graph)["summary.datacggm"]$label.dist <- - 3.5
V(cglasso.graph)["dim.datacggm"]$label.degree <- 0
V(cglasso.graph)["dim.datacggm"]$label.dist <- - 2.8
V(cglasso.graph)["ColMeans/ColVars"]$label.degree <- 0
V(cglasso.graph)["ColMeans/ColVars"]$label.dist <- - 3.5
V(cglasso.graph)["rcggm"]$label.degree <- 0
V(cglasso.graph)["rcggm"]$label.dist <- - 1.5
V(cglasso.graph)["getMatrix"]$label.degree <- 0
V(cglasso.graph)["getMatrix"]$label.dist <- - 2.0
V(cglasso.graph)["event"]$label.degree <- 0
V(cglasso.graph)["event"]$label.dist <- - 1.5
V(cglasso.graph)["qqcnorm"]$label.degree <- 0.3
V(cglasso.graph)["qqcnorm"]$label.dist <- + 1.7
V(cglasso.graph)["hist.datacggm"]$label.degree <- 0
V(cglasso.graph)["hist.datacggm"]$label.dist <- - 2.5
# Model fitting
V(cglasso.graph)["cglasso"]$label.degree <- 0
V(cglasso.graph)["cglasso"]$label.dist <- - 1.7
V(cglasso.graph)["plot.cglasso"]$label.degree <- 3.4
V(cglasso.graph)["plot.cglasso"]$label.dist <- + 2
V(cglasso.graph)["impute"]$label.degree <- + 2.5
V(cglasso.graph)["impute"]$label.dist <- 1.0
V(cglasso.graph)["fitted.cglasso"]$label.degree <- 0
V(cglasso.graph)["fitted.cglasso"]$label.dist <- - 2.5
V(cglasso.graph)["predict.cglasso"]$label.degree <- 0
V(cglasso.graph)["predict.cglasso"]$label.dist <- - 2.8
V(cglasso.graph)["print.cglasso"]$label.degree <- + pi / 4
V(cglasso.graph)["plot.cggm"]$label.degree <- 0
V(cglasso.graph)["plot.cggm"]$label.dist <- + 2.0
V(cglasso.graph)["predict.cggm"]$label.degree <- 0
V(cglasso.graph)["predict.cggm"]$label.dist <- + 2.5
V(cglasso.graph)["print.cggm"]$label.degree <- 0
V(cglasso.graph)["print.cggm"]$label.dist <- 2.2
V(cglasso.graph)["cggm"]$label.degree <- - 0.2
V(cglasso.graph)["cggm"]$label.dist <- + 1.5
# Model selection
V(cglasso.graph)["plot.GoF"]$label.degree <- 0
V(cglasso.graph)["plot.GoF"]$label.dist <- + 2.0
V(cglasso.graph)["print.GoF"]$label.degree <- 0
V(cglasso.graph)["print.GoF"]$label.dist <- + 2.0
V(cglasso.graph)["print.QFun"]$label.degree <- 0
V(cglasso.graph)["print.QFun"]$label.dist <- + 2.0
V(cglasso.graph)["AIC.cglasso/BIC.cglasso"]$label.degree <- 0.1
V(cglasso.graph)["AIC.cglasso/BIC.cglasso"]$label.dist <- + 4.3
V(cglasso.graph)["QFun"]$label.degree <- 0.4
V(cglasso.graph)["QFun"]$label.dist <- + 1.3
V(cglasso.graph)["summary.cglasso"]$label.degree <- 0
V(cglasso.graph)["summary.cglasso"]$label.dist <- + 3.0
# Network analysis
V(cglasso.graph)["to_graph"]$label.degree <- 3.4
V(cglasso.graph)["to_graph"]$label.dist <- + 1.8
V(cglasso.graph)["is.cglasso2igraph"]$label.degree <- 0
V(cglasso.graph)["is.cglasso2igraph"]$label.dist <- + 3.0
V(cglasso.graph)["plot.cglasso2igraph"]$label.degree <- 0
V(cglasso.graph)["plot.cglasso2igraph"]$label.dist <- - 3.5
V(cglasso.graph)["print.cglasso2igraph"]$label.degree <- 0
V(cglasso.graph)["print.cglasso2igraph"]$label.dist <- - 3.5
V(cglasso.graph)["getGraph"]$label.degree <- + pi / 4
V(cglasso.graph)["lower/upper"]$label.degree <- + pi / 4
V(cglasso.graph)["datacggm"]$label.font <- 2
V(cglasso.graph)["cglasso"]$label.font <- 2
V(cglasso.graph)["cggm"]$label.font <- 2
V(cglasso.graph)["QFun"]$label.font <- 2
V(cglasso.graph)["AIC.cglasso/BIC.cglasso"]$label.font <- 2
V(cglasso.graph)["to_graph"]$label.font <- 2
legend.txt <- c("Data manipulation", "Model fitting", "Model selection", "Network analysis")
legend.col <- c("red4", "blue4", "darkgoldenrod4", "darkolivegreen")
if (module == "DM") {
vids <- which(is.element(names(V(cglasso.graph)), data.manipulation.nm))
legend.txt <- legend.txt[1L]
legend.col <- legend.col[1L]
cglasso.description <- cglasso.description["Data Manipulation"]
}
if (module == "MF") {
vids <- which(is.element(names(V(cglasso.graph)), model.fitting.nm))
legend.txt <- legend.txt[2L]
legend.col <- legend.col[2L]
cglasso.description <- cglasso.description["Model Fitting"]
}
if (module == "MS") {
vids <- which(is.element(names(V(cglasso.graph)), model.selection.nm))
legend.txt <- legend.txt[3L]
legend.col <- legend.col[3L]
cglasso.description <- cglasso.description["Model Selection"]
}
if (module == "NA") {
vids <- which(is.element(names(V(cglasso.graph)), network.analysis.nm))
legend.txt <- legend.txt[4L]
legend.col <- legend.col[4L]
cglasso.description <- cglasso.description["Network Analysis"]
}
if (module != "ALL")
cglasso.graph <- induced_subgraph(cglasso.graph, vids = vids)
if (plot) {
cglasso.graph$layout <- layout_(cglasso.graph, with_kk())
plot(cglasso.graph, main = "cglasso Package")
legend(x = -1.6, y = -0.75,
legend = legend.txt,
text.col = legend.col,
cex = 0.8,
bty = "n",
border = NULL)
}
if (description) {
print.listof(cglasso.description)
cat("NOTE: use", sQuote("?method.class"), "to get the documentation pages\n\n")
}
out <- list(description = cglasso.description, graph = cglasso.graph)
invisible(out)
}
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