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#' @title compare_models_visual
#' @description FUNCTION_DESCRIPTION
#' @param models PARAM_DESCRIPTION
#' @param seq sequence for which the calibrated predictions should be plotted, Default: NULL
#' @return OUTPUT_DESCRIPTION
#' @details DETAILS
#' @seealso
#' \code{\link[ggplot2]{ggplot}},\code{\link[ggplot2]{geom_line}},\code{\link[ggplot2]{aes}},\code{\link[ggplot2]{ylim}},\code{\link[ggplot2]{theme}},\code{\link[ggplot2]{labs}},\code{\link[ggplot2]{scale_color_brewer}}
#' \code{\link[reshape2]{melt}}
#' @rdname compare_models_visual
#' @importFrom ggplot2 ggplot geom_line aes ylim theme labs scale_color_brewer
#' @importFrom reshape2 melt
compare_models_visual <- function(models, seq=NULL){
max <- max(models$original_values$predicted)
min <- min(models$original_values$predicted)
#default: if no seq is given, evaluate from min to max value of original input score
if(is.null(seq)){
step_size <- (max-min)/100 #evaluate 100 scores
seq <- seq(min, max, step_size)
}
predictions <- predict_calibratR(models, seq, nCores=1)
predictions$original <- NULL
L1 <- NULL
value <- NULL
plot1 <- ggplot2::ggplot(cbind(seq,reshape2::melt(predictions)))+
ggplot2::geom_line(ggplot2::aes(x=seq, y=value, colour=L1), size=1)+
ggplot2::ylim(0, 1)+
ggplot2::theme(legend.position = "bottom")+
ggplot2::labs(title="Comparison of Calibration models", x = "original ML score", y = "calibrated prediction")+
ggplot2::scale_color_brewer(palette = "Paired", name=NULL)
return(plot1)
}
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