R/ate-bootstrap.R

Defines functions calcBootATE

### ate-bootstrap.R --- 
##----------------------------------------------------------------------
## Author: Brice Ozenne
## Created: apr 11 2018 (17:05) 
## Version: 
## Last-Updated: Dec  6 2020 (08:47) 
##           By: Thomas Alexander Gerds
##     Update #: 340
##----------------------------------------------------------------------
## 
### Commentary: 
## 
### Change Log:
##----------------------------------------------------------------------
## 
### Code:

## * calcBootATE
## generate a boot object for the ate function that will be used to compute CI and p.values
calcBootATE <- function(args, n.obs, fct.pointEstimate, name.estimate,
                        handler, B, seed, mc.cores, cl,
                        verbose){

                                        # {{{ prepare arguments
    
    ## hard copy of the dataset before bootstrap
    ls.data <- list(object.event = NULL,
                    object.treatment = NULL,
                    object.censor = NULL)
    ls.package <- list(object.event = NULL,
                       object.treatment = NULL,
                       object.censor = NULL)
    for(iModel in c("object.event","object.treatment","object.censor")){ ## iModel <- "object.event"

        if(inherits(args[[iModel]]$call$data,"name")){
            data.tempo <- eval(args[[iModel]]$call$data)
            if(inherits(data.tempo,"function")){
                stop("The dataset in argument \'",iModel,"\' has the same name has an existing R function.\n",
                     "This creates confusion when we try to extract it from the call. Please rename the dataset.\n")
            }else{
                ls.data[[iModel]] <- data.table::as.data.table(data.tempo)
            }
        }else{
            ls.data[[iModel]] <- args[[iModel]]$call$data
        }
        
        if(inherits(args[[iModel]]$call$formula,"name")){
            formula.tempo <- eval(args[[iModel]]$call$formula)
            if(inherits(formula.tempo,"function")){
                stop("The formula in argument \'",iModel,"\' has the same name has an existing R function.\n",
                     "This creates confusion when we try to extract it from the call. Please rename the formula.\n")
            }else{
                args[[iModel]]$call$formula <- formula.tempo
            }
        }

        ## package to be exported to cluster
        if(inherits(args[[iModel]]$call[[1]],"name")){
            iSource <- utils::find(deparse(args[[iModel]]$call[[1]]))
            iFound <- grepl("package:",iSource)
            if(any(iFound)){
                ls.package[[iModel]] <- gsub("package:","",iSource[iFound])
            }else{
                NULL
            }
        }else{
            if(!is.null(environment(args[[iModel]]$call[[1]]))){
                ls.package[[iModel]] <- utils::packageName(environment(args[[iModel]]$call[[1]]))
            }else{
                ls.package[[iModel]] <- NULL
            }
        }
    }

    ## packages and functions to be exported to the cluster
    add.Package <- unique(c("riskRegression","prodlim","data.table","parallel","survival",unlist(ls.package)))
    add.Fct <- c("SurvResponseVar","predictRisk.coxphTD","predictRisk.CSCTD","ATE_COMPARISONS")
    
    ## if cluster already defined by the user
    no.cl <- is.null(cl)
    if( (no.cl[[1]] == FALSE) && (mc.cores[[1]] == 1) ){ ## i.e. the user has not initialized the number of cores
        mc.cores <- length(cl)
    }
    
    ## seed
    if (!missing(seed)){
        set.seed(seed)
    }
    bootseeds <- sample(1:1000000,size=B,replace=FALSE)
    ## allArgs <- c("warperBootATE","data","n.obs","fct.pointEstimate",
    ## "object","treatment","contrasts","times","cause","landmark",
    ## "n.contrasts","levels","TD","name.estimate","formula","dots")

                                        # }}}

                                        # {{{ warper
    warperBootATE <- function(index, args, fct.pointEstimate, name.estimate){
        
        ## models for the conditional mean
        for(iModel in c("object.event","object.treatment","object.censor")){
            if(!is.null(args[[iModel]])){
                args[[iModel]]$call$data <- ls.data[[iModel]][index] ## resample dataset
                args[[iModel]] <- try(eval(args[[iModel]]$call),silent=TRUE) ## refit  model
                if ("try-error" %in% class(args[[iModel]])){
                    iBoot <- c(paste0("Failed to fit model ",iModel," on the bootstrap sample", sep = ""),
                               args[[iModel]]
                               )
                    class(iBoot) <- "try-error"
                    return(iBoot)
                }
            }
        }

        ## compute ate
        if(is.data.frame(args$mydata)){
            args$mydata <- args$mydata[index,]
        }else{
            args$mydata <- args$mydata[index]
        }
        if(any(args$data.index != 1:NROW(args$mydata))){
            args$data.index <- which(index %in% args$data.index)
        }
        iBoot <- try(do.call(fct.pointEstimate, args), silent = TRUE)
        ## export
        if(inherits(iBoot,"try-error")){ ## error handling
            out <- rep(NA, length(name.estimate), name.estimate)
            attr(out,"error") <- iBoot
            return(out)        
        }else{
            return(c(iBoot$meanRisk$estimate,iBoot$diffRisk$estimate,iBoot$ratioRisk$estimate))
        }
    }
                                        # }}}
    ## bootstrap
    if(no.cl && mc.cores == 1){
        if(verbose){pb <- txtProgressBar(max = B, style = 3,width=30)}
        boots <- lapply(1:B, FUN = function(b){
            if(verbose>0){setTxtProgressBar(pb, b)}
            set.seed(bootseeds[[b]])
            warperBootATE(index = sample(1:n.obs, size = n.obs, replace = TRUE),
                          args = args,
                          fct.pointEstimate = fct.pointEstimate,
                          name.estimate = name.estimate)                                      
        })
        if(verbose>0){close(pb)}
    
    }else if (handler=="foreach"){ # 
                                        # {{{ foreach
        if(no.cl){
            if(verbose>0){
                cl <- parallel::makeCluster(mc.cores, outfile = "")
            }else{
                cl <- parallel::makeCluster(mc.cores)
            }                
        }           
        doParallel::registerDoParallel(cl)
        ## progress bar 
        if(verbose){pb <- txtProgressBar(max = B, style = 3,width=30)}
        b <- NULL ## [:forCRANcheck:] foreach
        boots <- foreach::`%dopar%`(foreach::foreach(b = 1:B, .packages = add.Package, .export = add.Fct), { ## b <- 1
            if(verbose>0){setTxtProgressBar(pb, b)}
            set.seed(bootseeds[[b]])
            warperBootATE(index = sample(1:n.obs, size = n.obs, replace = TRUE),
                          args = args,
                          fct.pointEstimate = fct.pointEstimate,
                          name.estimate = name.estimate)                                      
        })
        if(verbose>0){close(pb)}
        if(no.cl){parallel::stopCluster(cl)}
                                        # }}}
    }else if(handler=="mclapply"){
                                        # {{{ mclapply
        boots <- parallel::mclapply(1:B, mc.cores = mc.cores, FUN = function(b){
            set.seed(bootseeds[[b]])
            warperBootATE(index = sample(1:n.obs, size = n.obs, replace = TRUE),
                          args = args,
                          fct.pointEstimate = fct.pointEstimate,
                          name.estimate = name.estimate)                                      
        })
                                        # }}}
    }

    ## output
    M.bootEstimate <- do.call(rbind,boots)
    if(all(is.na(M.bootEstimate))){
        stop(paste0("Error in all bootstrap samples: ", attr(boots[[1]],"error")[1]))
    }
    colnames(M.bootEstimate) <- name.estimate

    return(list(boot = M.bootEstimate,
                bootseeds = bootseeds))
}

######################################################################
### ate-bootstrap.R ends here

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riskRegression documentation built on Jan. 13, 2021, 11:12 a.m.