R/HAL9001.R

In riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks

#' @title Fitting HAL for use with predictRisk
#'
#' @description Fit HAL models via a formula and a data set for use with \code{\link{predictRisk}}.
#' @name Hal9001
#'
#' @param formula A formula.
#' @param data The data on which to fit the model. 
#' @param lambda The tuning parameters for HAL. If set to NULL, then it the parameters are chosen for you.
#' @param \dots Additional arguments that are passed on to the hal_fit.
#' @export
Hal9001 <- function(formula,data,lambda=NULL,...){
    requireNamespace("hal9001")
    strata.num = start = status = NULL
    EHF = prodlim::EventHistory.frame(formula,data,unspecialsDesign = TRUE,specials = NULL)
    stopifnot(attr(EHF$event.history,"model")[[1]] == "survival")
    # blank Cox object needed for predictions
    data = data.frame(cbind(EHF$event.history,EHF$design))
    bl_cph <- coxph(Surv(time,status)~1,data=data,x=1,y=1)
    bl_obj <- coxModelFrame(bl_cph)[]
    bl_obj[,strata.num:=0]
    data.table::setorder(bl_obj, strata.num,stop,start,-status)
    hal_fit <- do.call(hal9001::fit_hal,list(X = EHF$design,
                                             Y = EHF$event.history,
                                             family = "cox",
                                             return_lasso = TRUE,
                                             yolo = FALSE,
                                             lambda = lambda,
                                             ...))
    # to be able to calculate the baseline hazard function we need
    # to evaluate the linear predictor with the training covariates
    data.table::set(bl_obj,j = "eXb",value= as.numeric(predict(hal_fit,new_data=EHF$design)))
    out = list(fit = hal_fit,
               surv_info = bl_obj,
               call = match.call(),
               terms = terms(formula))
    class(out) = "Hal9001"
    out
}