##
## wod_4hist: Observation influence estimation by bootstrap , returns the matrix with all measures
##
##
## Function wod_4hist
##
## performance metric : concordance index
## Arguments: data : SurvivalDataSet object
## nruns : number of runs of the bootstrap
## layer5_model : the layer5 predictor
## Output : vector with length equal to the number of data rows , each position with the estimated influence on concordance
##
#' @importFrom survival coxph
wod_4hist_k_p <- function( obs_index, surv.object, covariate.data , nruns , m ) {
actual_data <- cbind( covariate.data , surv.object[,1], surv.object[,2] )
time_index <- ncol(actual_data) - 1
status_index <- ncol(actual_data)
data_length <- nrow(actual_data)
obs_influences <- rep(x = 0 , data_length)
obs_max_influences <- rep(x= 0 , data_length)
concs_vector <- rep(x = 0, nruns )
## calculate the baseline concordance, according to the baseline (all observations)
#
cox_object <- coxph( survival::Surv(actual_data[,time_index], as.integer(actual_data[,status_index]) ) ~ . , data = data.frame(actual_data[,-c(time_index,status_index)]) )
baseline_concordance <- cox_object$concordance[1]/(cox_object$concordance[1] + cox_object$concordance[2] )
cat("baseline:" , baseline_concordance )
concordance_sum <- 0
for ( i in 1:nruns ){
boot_datax <- getBootstrap_K(data = actual_data[-obs_index,], )
actual_conc <- coxph.call(boot_data = boot_datax , time_index = time_index, status_index = status_index )
if (is.null(actual_conc)) {
for( ix in 1:10) {
cat('Error: ', ix)
boot_datax <- getBootstrap_K(data = actual_data[-obs_index,], )
actual_conc <- coxph.call(boot_data = boot_datax , time_index = time_index, status_index = status_index )
if (!is.null(actual_conc)) {
break
}
}
}
if (is.null(actual_conc)) {
next
}
concordance_run <- actual_conc - baseline_concordance
concs_vector[i] <- concordance_run
}
# cat( "Antidote Bootstrap: " , row_index , "out of", data_length , " complete \n")
return(concs_vector)
}
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