#'
#' Best Fit Prediction
#'
#' Predicts the survival/reliability of the given times for the model
#'
#' @param best_model: The rank of model to be used in the plot. If best_model = 2, the second best model will be used in the plot. Can be the name of the distribution of the model
#' @param conf_int: If \code{TRUE} then the confidence intervals will be calculated.
#' @param m_flexsurvreg: list containing at least the alement "models" that has a list of \code{flexsurvreg} models. Or an object of class \code{flexsurvreg}
#' @param pred_times: the list of times to predict to.
#' @param ...: further argumentrs for \code{summary()}
#' @return
#' @details Returns a data.frame with the predicted survival/reliability for range of life times (resulting from a \code{multi_surv_reg()}). If the object is a model itself then the function does not search for the best model.
#' @export
#' @examples
#'
predict_best_model = function(m_flexsurvreg
, pred_times = c(50,100,120,150,200)*1000
, conf_int = FALSE
, best_model = 1
, ...
){
if('multi_surv_reg' %in% class(m_flexsurvreg)){
summary(m_flexsurvreg$models[[best_model]],
t=pred_times,
type="survival",
ci=conf_int
, ...)
} else if( any(c('flexsurvreg', 'light_flexsurvreg', 'flexsurvreg_faultless', 'flexsurvreg_NULL' ) %in% class(m_flexsurvreg[[best_model]]))) {
summary(m_flexsurvreg[[best_model]], t=pred_times,type="survival",ci=conf_int, ...)
} else {
summary(m_flexsurvreg, t=pred_times,type="survival",ci=conf_int, ...)
}
}
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