vignettes/PosteriorCoxModelPredictors.R

In csetraynor/iclust2prog: Prognostic modeling of IntClust-2 (iclust2prog)

devtools::document()
# library(iclust2prog)
library(glmnet)
library(purrr)
data("ic2dat")

set.seed(9666)
mc_samp <- bootstraps(ic2dat, strata = "status", times = 100)

iclust2_features <- extract_features(iclust2_glmnet)
iclust2_features$feature <- my_replace(iclust2_features$feature)
colnames(ic2dat) <- my_replace(colnames(ic2dat))

mc_samp$mod_iclust2 <- pmap(list(mc_samp$splits), function(data) {
  mod_fit(x = data, form = iclust2_features, iter = 1)
})

mc_samp$CoxTab_iclust2 <- pmap(list(mc_samp$mod_iclust2), function(mod) {
  par.table(mod, log_scale = TRUE)
})

mc_samp$GeneTab_iclust2 <- pmap(list(mc_samp$CoxTab_iclust2), function(tab) {
  get_geneTable(tab)
})

coeff_Tab <- get_coeff_Tab(mc_samp)

gene_mod <- coeff_Tab %>% dplyr::mutate(kras = rowSums(.[c("NGF", "GPR4")])) %>%
  dplyr::rename(lef1 = MAP1B) %>% dplyr::select(splits, id, kras, lef1)


int_gene <- tidyposterior::perf_mod(gene_mod, seed = 6507, iter = 5000)

int_gene_tab <- tidy(int_gene) %>% dplyr::group_by(model) %>% dplyr::summarise(HR = exp(mean(posterior)),
                                                                               lower = exp(quantile(posterior, 0.05)), upper = exp(quantile(posterior,
                                                                                                                                            0.95)))
as.data.frame(int_gene_tab) %>% dplyr::mutate_all(my_round)