plot_evaluation {Coxmos}

plot_evaluation

Description

Generates a comprehensive evaluation of the performance of a given Coxmos evaluation object from eval_Coxmos_models(), offering both statistical tests and visual plots for assessment.

Usage

plot_evaluation(
  eval_results,
  evaluation = "AUC",
  pred.attr = "mean",
  y.min = NULL,
  type = "both",
  round_times = FALSE,
  decimals = 2,
  title = NULL,
  title_size_text = 15,
  legend_title = "Method",
  legend_size_text = 12,
  x_axis_size_text = 10,
  y_axis_size_text = 10,
  label_x_axis_size = 10,
  label_y_axis_size = 10
)

Arguments

eval_results

Coxmos evaluation object from eval_Coxmos_models().

evaluation

Character. Perform the evaluation using the "AUC" or "Brier" metric (default: "AUC").

pred.attr

Character. Way to evaluate the metric selected. Must be one of the following: "mean" or "median" (default: "mean").

y.min

Numeric. Minimum Y value for establish the Y axis value. If y.min = NULL, automatic detection is performed (default: NULL).

type

Character. Plot type. Must be one of the following: "both", "line" or "mean". In other case, "both" will be selected (default: "both").

round_times

Logical. Whether times x value should be rounded (default: FALSE).

decimals

Numeric. Number of decimals to use in round times. Must be a value greater or equal zero (default = 2).

title

Character. Plot title (default: NULL).

title_size_text

Numeric. Text size for legend title (default: 15).

legend_title

Character. Legend title (default: "Method").

legend_size_text

Numeric. Text size for legend title (default: 12).

x_axis_size_text

Numeric. Text size for x axis (default: 10).

y_axis_size_text

Numeric. Text size for y axis (default: 10).

label_x_axis_size

Numeric. Text size for x label axis (default: 10).

label_y_axis_size

Numeric. Text size for y label axis (default: 10).

Details

The plot_evaluation function is designed to facilitate a rigorous evaluation of the performance of models, specifically in the context of survival analysis. This function is tailored to work with a Coxmos evaluation object, which encapsulates the results of survival models. The primary objective is to provide both statistical and visual insights into the model's performance.

The function offers flexibility in the evaluation metric, allowing users to choose between the Area Under the Curve (AUC) and the Brier score. The chosen metric is then evaluated based on either its mean or median value, as specified by the "pred.attr" parameter. The resulting plots can be tailored to display continuous performance over time or aggregated mean performance, based on the "type" parameter.

A salient feature of this function is its ability to conduct statistical tests to compare the performance across different methods. Supported tests include the t-test, ANOVA, Wilcoxon rank-sum test, and Kruskal-Wallis test. These tests provide a quantitative measure of the differences in performance, aiding in the objective assessment of the models.

The visual outputs are generated using the 'ggplot2' package, ensuring high-quality and interpretable plots. The function also offers extensive customization options for the plots, including axis labels, title, and text sizes, ensuring that the outputs align with the user's preferences and the intended audience's expectations.

Value

A list of lst_eval_results length. Each element is a list of three elements. lst_plots: A list of two plots. The evaluation over the time, and the extension adding the mean or median on the right. lst_plot_comparisons: A list of comparative boxplots by t.test, anova, wilcoxon, kruscal. df: Data.frame of evaluation result.

Author(s)

Pedro Salguero Garcia. Maintainer: [e-mail]

Examples

data("X_proteomic")
data("Y_proteomic")
set.seed(123)
index_train <- caret::createDataPartition(Y_proteomic$event, p = .5, list = FALSE, times = 1)
X_train <- X_proteomic[index_train,1:50]
Y_train <- Y_proteomic[index_train,]
X_test <- X_proteomic[-index_train,1:50]
Y_test <- Y_proteomic[-index_train,]
coxEN.model <- coxEN(X_train, Y_train, x.center = TRUE, x.scale = TRUE)
eval_results <- eval_Coxmos_models(lst_models = list("coxEN" = coxEN.model), X_test = X_test,
Y_test = Y_test)
plot_eval_results <- plot_evaluation(eval_results)