## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set(fig.width = 8, fig.height = 8, out.height = "80%", out.width = "80%", dpi = 300) counter <- 0 ## ----setup, warning=FALSE, include = FALSE------------------------------------ library(survival) ## ----data--------------------------------------------------------------------- library(modgo) data("Cleveland", package = "modgo") ## ----basic_arguments---------------------------------------------------------- # Specifying dichotomous and ordinal categorical variables binary_variables <- c("Sex","HighFastBloodSugar","CAD","ExInducedAngina") categorical_variables <- c("Chestpaintype","RestingECG") nrep <- 500 plot_variables <- c("Age", "STDepression", binary_variables[c(1,3)], categorical_variables) ## ----default_test------------------------------------------------------------- test <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, nrep = nrep) ## ----correlation_default,echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plots for a default *modgo* run.")---- corr_plots(test, variables = plot_variables) counter <- counter + 1 ## ----distr_default,echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plots for a default *modgo* run.")---- distr_plots(test, variables = plot_variables) counter <- counter + 1 ## ----------------------------------------------------------------------------- Variables <- c("Age") thresh_left <- c(65) thresh_right <- c(NA) thresholds <- data.frame(Variables, thresh_left, thresh_right) print(as.matrix(thresholds)) test_thresh <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, thresh_var = thresholds, nrep = nrep, thresh_force = TRUE) ## ----echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plot for Age > 65 threshold *modgo* run")---- corr_plots(test_thresh, variables = plot_variables) counter <- counter + 1 ## ----echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plot for Age > 65 threshold *modgo* run")---- distr_plots(test_thresh, variables = plot_variables) counter <- counter + 1 ## ----------------------------------------------------------------------------- #Create named vector perturb_vector <- c(0.9,0.7) names(perturb_vector) <- c("RestingBP","Cholsterol") test_pertru <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, pertr_vec = perturb_vector, nrep = nrep) ## ----echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plot for Pertrubation Expansion *modgo* run")---- corr_plots(test_pertru, variables = c(plot_variables, names(perturb_vector))) counter <- counter + 1 ## ----echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plot for Pertrubation Expansion *modgo* run")---- distr_plots(test_pertru, variables = c(plot_variables, names(perturb_vector))) counter <- counter + 1 ## ----GLD_run------------------------------------------------------------------ test_GLD <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, generalized_mode = TRUE, nrep = nrep) ## ----correlation_GLD, echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plots for Generalized Lambda Distribtion *modgo* run")---- corr_plots(test_GLD, variables = plot_variables) counter <- counter + 1 ## ----distr_GLD, echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plot for Generalized Lambda Distribtion *modgo* run")---- distr_plots(test_GLD, variables = plot_variables) counter <- counter + 1 ## ----arguments_GLD_def_model-------------------------------------------------- Variables <- c("Age","STDepression") Model <- c("rprs", "star-rmfmkl") model_matrix <- cbind(Variables, Model) ## ----GLD_run_def_model-------------------------------------------------------- test_GLD_define_model <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, generalized_mode = TRUE, generalized_mode_model = model_matrix, nrep = nrep) ## ----correlation_GLD_def_model, echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plots for Generalized Lambda Distribtion *modgo* run with specified GLD models")---- corr_plots(test_GLD_define_model, variables = plot_variables) counter <- counter + 1 ## ----distr_GLD_def_model,echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plots for Generalized Lambda Distribtion *modgo* run with specified GLD models")---- distr_plots(test_GLD_define_model, variables = plot_variables) counter <- counter + 1 ## ----GLD_run_def_model_set_lambdas-------------------------------------------- gener_lambdas_matrix <- generalizedMatrix(data = Cleveland, generalized_mode_model = model_matrix, bin_variables = binary_variables) test_GLD_define_model_set_lambdas <- modgo(data = Cleveland, bin_variables = binary_variables, categ_variables = categorical_variables, generalized_mode = TRUE, generalized_mode_lmbds = gener_lambdas_matrix, nrep = nrep) ## ----GLD_run_no_data_set------------------------------------------------------ # Necessary arguments gener_lambdas_matrix <- generalizedMatrix(data = Cleveland, generalized_mode_model = model_matrix, bin_variables = binary_variables) sigma <- cor(Cleveland) variables_names <- colnames(sigma) sample_size <- 100 test_GLD_no_data_set <- modgo(data = NULL, variables = variables_names, bin_variables = binary_variables, categ_variables = categorical_variables, sigma = sigma, generalized_mode = TRUE, generalized_mode_lmbds = gener_lambdas_matrix, n_samples = sample_size, nrep = nrep) ## ----correlation_GLD_run_no_data_set, echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plots for Generalized Lambda Distribtion *modgo* run without providing a data set")---- corr_plots(test_GLD_no_data_set, variables = plot_variables) counter <- counter + 1 ## ----survival_data_set-------------------------------------------------------- # cancer prepare data("cancer", package = "survival") cancer <- na.omit(cancer) cancer$sex <- cancer$sex - 1 cancer$status <- cancer$status - 1 time_var_cancer <- "time" status_var_cancer <- "status" bin_var_cancer <- c("status", "sex") cat_var_list_cancer <- c("ph.ecog") plot_variables_surv <- colnames(cancer)[1:6] ## ----survival_data_set_run---------------------------------------------------- # Survival run test_surv <- modgo_survival(data = cancer, surv_method = 1, bin_variables = bin_var_cancer, categ_variables = cat_var_list_cancer, event_variable = status_var_cancer, time_variable = time_var_cancer, generalized_mode_model_no_event = "rmfmkl", generalized_mode_model_event = "rprs") ## ----survival_data_set_corr, echo=FALSE, fig.cap=paste0("Figure ",counter,": Correlation plots for modgo_survival run")---- corr_plots(test_surv, variables = plot_variables_surv) counter <- counter + 1 ## ----survival_data_set_distr, echo=FALSE, fig.cap=paste0("Figure ",counter,": Distribution plots for modgo_survival run")---- distr_plots(test_surv, variables = plot_variables_surv) counter <- counter + 1 ## ----survival_data_set_coxplots, fig.cap=paste0("Figure ",counter,": Survival fit curves plot for modgo_survival run")---- data_set_info <- c(rep("Original", dim(test_surv$original_data)[1]), rep("Simulated", dim(test_surv$simulated_data[[1]])[1])) combine_data_set <- rbind(test_surv$original_data, test_surv$simulated_data[[1]]) combine_data_set <- cbind(combine_data_set, data_set_info) fit <- survfit(Surv(time, status) ~ data_set_info, data=combine_data_set) plot(fit, fun = "F", col=1:2) legend(700, 1, c("Original data set", "Simulated data set"), lty=c(1,1), col=c(1,2), bty='n', lwd=2)