R/gam_iterate_gbm.R

In ericdunipace/CoarsePosteriorSummary: Interpretable Model Summaries Using the Wasserstein Distance

#' Run the p-Wasserstein projections for the GAM example
#'
#' @param formula an R formula object suitable for \link[gam]{gam} or \link[qgam]{qgam}
#' @param y prediction matrix
#' @param x covariates
#' @param extract new data for prediction
#' @param time vector of times
#' @param nT number of timepoints
#' @param which.gam "gam" or "qgam" for the models?
#' @param ... not currently used
#'
#' @return
#' @export
gam_iterate <- function(formula, y, x, extract, time, nT, which.gam = c("gam","qgam"),...) {

  covar_vals_raw <- list()
  covar_vals <- array(NA, dim = c(nT, 7, ncol(y)))
  which.gam <- match.arg(which.gam)
  args <- list(formula = formula,
               data = NULL,
               qu = 0.5,
               ...)
  if(which.gam == "qgam") names(args)[1] <- "form"
  argn <- lapply(names(args), as.name)
  names(argn) <- names(args)
  fun.call <- as.call(c(list(as.name(which.gam)), argn))
  # foreach(s = 1:ncol(y)) %doPar% {
  # }
  for(s in 1:ncol(y)) {
    args$data   <- data.frame(y = y[,s], x, time = time)

    gamFit   <- eval(fun.call, envir = args)
    covar_vals_raw[[1]] <- predict(gamFit, newdata = extract, type = "terms")
    covar_vals[,,s] <- covar_vals_raw[[1]][1:nT,2:8]
    covar_vals[,5,s] <- covar_vals[,5,s] + covar_vals_raw[[1]][1:nT,1]
    covar_vals[,6,s] <- rowSums(covar_vals_raw[[1]][extract$Resection=="Sub",])
    covar_vals[,7,s] <- rowSums(covar_vals_raw[[1]][extract$Resection=="Gross",])
  }
  return(covar_vals)
}

#' Title
#'
#' @param gammX gam model
#' @param times event times
#'
#' @return
#' @export
gamm_interp_data_gbm <- function(gammX, times) {
  nT <- length(unique(times))
  # gammX       <- preDF$tx.test[,-1]
  gammX       <- as.data.frame(gammX)
  colnames(gammX) <- c("Age", "Gender", "KPS", "MGMT", "ResectionBiopsy", "ResectionSub","ResectionTotal")
  gammX$Res   <- factor(apply(gammX[,5:7],1,which.max), levels = 1:3, labels = c("Biopsy","Sub","Gross"))
  gammX       <- gammX[,c(1:4,8)]
  gammT       <- rep(times, nrow(gammX))
  colnames(gammX) <- c("Age", "Gender", "KPS", "MGMT", "Resection")
  gammXmod    <- data.frame(model.matrix(~. + Age:Resection -1, data = gammX))
  gammXmod$Resection<- gammX$Resection
  gammXmod$ResectionBiopsy <- gammXmod$ResectionSub <- gammXmod$ResectionGross <- NULL
  extend_extract <- length(levels(gammX$Resection))
  extract_terms <- data.frame(matrix(1, nrow= nT, ncol=ncol(gammX)))
  colnames(extract_terms) <- colnames(gammX)
  extract_terms[(nT+1): (nT*extend_extract), c("Age","Gender","KPS","MGMT")] <- 0
  extract_terms$Resection <- factor(rep(levels(gammX$Resection), each = nT))
  extract_terms$time <- times
  return(list(gammX = gammX, times = gammT, extract_terms = extract_terms))
}