Plot Model Performance for Survival Models

Source: R/plot_model_performance_survival.R

This function is a wrapper for plotting model_performance objects created for survival models using the model_performance() function.

# S3 method for model_performance_survival
plot(x, ...)

Arguments

x

an object of class "model_performance_survival" to be plotted

...

additional parameters passed to the plot.surv_model_performance or plot.surv_model_performance_rocs function

Value

An object of the class ggplot.

Plot options

plot.surv_model_performance

  • x - an object of class "surv_model_performance" to be plotted

  • ... - additional objects of class "surv_model_performance" to be plotted together

  • metrics - character, names of metrics to be plotted (subset of C/D AUC", "Brier score" for metrics_type %in% c("time_dependent", "functional") or subset of "C-index","Integrated Brier score", "Integrated C/D AUC" for metrics_type == "scalar"), by default (NULL) all metrics of a given type are plotted

  • metrics_type - character, either one of c("time_dependent","functional") for functional metrics or "scalar" for scalar metrics

  • title - character, title of the plot

  • subtitle - character, subtitle of the plot, 'default' automatically generates "created for XXX, YYY models", where XXX and YYY are the explainer labels

  • facet_ncol - number of columns for arranging subplots

  • colors - character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue")

  • rug - character, one of "all", "events", "censors", "none" or NULL. Which times to mark on the x axis in geom_rug().

  • rug_colors - character vector containing two colors (containing either hex codes "#FF69B4", or names "blue"). The first color (red by default) will be used to mark event times, whereas the second (grey by default) will be used to mark censor times.

plot.surv_model_performance_rocs

  • x - an object of class "surv_model_performance_rocs" to be plotted

  • ... - additional objects of class "surv_model_performance_rocs" to be plotted together

  • title - character, title of the plot

  • subtitle - character, subtitle of the plot, 'default' automatically generates "created for XXX, YYY models", where XXX and YYY are the explainer labels

  • colors - character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue")

  • facet_ncol - number of columns for arranging subplots

See also

Other functions for plotting 'model_performance_survival' objects: plot.surv_model_performance_rocs(), plot.surv_model_performance()

Examples

# \donttest{
library(survival)
library(survex)

model <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran)
exp <- explain(model)
#> Preparation of a new explainer is initiated 
#>   -> model label       :  rfsrc (  default  ) 
#>   -> data              :  137  rows  6  cols (  extracted from the model  ) 
#>   -> target variable   :  137  values ( 128 events and 9 censored , censoring rate = 0.066 ) (  extracted from the model  ) 
#>   -> times             :  50 unique time points , min = 1.5 , median survival time = 80 , max = 999 
#>   -> times             :  (  generated from y as uniformly distributed survival quantiles based on Kaplan-Meier estimator  ) 
#>   -> predict function  :  sum over the predict_cumulative_hazard_function will be used (  default  ) 
#>   -> predict survival function  :  stepfun based on predict.rfsrc()$survival will be used (  default  ) 
#>   -> predict cumulative hazard function  :  stepfun based on predict.rfsrc()$chf will be used (  default  ) 
#>   -> model_info        :  package randomForestSRC , ver. 3.2.3 , task survival (  default  ) 
#>   A new explainer has been created!  

m_perf <- model_performance(exp)
plot(m_perf, metrics_type = "functional")
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_line()`).


m_perf_roc <- model_performance(exp, type = "roc", times = c(100, 300))
plot(m_perf_roc)

# }