library(JointNets) graphics.off() par(ask = FALSE) par(mfrow = c(1, 1))
## (simulation) simulate samples of two groups simulationresult = simulation(n=c(100,100)) AUC_result = AUC(simulationresult, gm_method = "diffee", lambdas = seq(0.1,2,0.05)) truth = simulationresult$simulatedgraphs
## (learning) compute results for diffee result = diffee(simulationresult$simulatedsamples[[1]], simulationresult$simulatedsamples[[2]], 0.45)
## (visualiation) plot estimated graph layout = layout_nicely(returngraph(result)) plot(result,layout = layout) plot(truth, subID = 0, layout = layout)
data(nip_37_data) label = colnames(nip_37_data[[1]]) result = simule( nip_37_data, lambda = 0.13, epsilon = 0.5, covType = "kendall" ) graph = returngraph(result) layout = layout_nicely(graph, dim = 2) par(mfrow = c(1, 1)) plot(result, type = "task", layout = layout) plot(result, type = "share", layout = layout) plot(result, type = "taskspecific", subID = 1, layout = layout) plot(result, type = "taskspecific", subID = 2, layout = layout)
plot_gui()
## (evaluation) evaluate diffee performance { cat(paste("AUC score: ", AUC_result$auc)) cat("\n") cat("F1 score difference: ") cat(F1(result,truth)$difference) cat("\n") plot(AUC_result$fPM,AUC_result$tPM, xlab = "False Positive Rate", ylab = "True Positive Rate", main = "ROC") lines(AUC_result$fPM[order(AUC_result$fPM)], AUC_result$tPM[order(AUC_result$fPM)], xlim=range(AUC_result$fPM), ylim=range(AUC_result$tPM)) }
## (application) classification using QDA split = train_valid_test_split(simulationresult$simulatedsamples,c(0.6,0.2,0.2),1000) train = split[["train"]] valid = split[["valid"]] test = split[["test"]] v_seeking_length = 200 lambda_range = seq(0.1,0.3,length.out = 50) result = QDA_eval(train,valid,test,lambda_range, v_seeking_length, method = "diffee") result[["best test accuracy"]]
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