Plot Predict Parts for Survival Models

Source: R/plot_predict_parts_survival.R

This function plots objects of class "predict_parts_survival" - local explanations for survival models created using the predict_parts() function.

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

Arguments

x

an object of class "predict_parts_survival" to be plotted

...

additional parameters passed to the plot.surv_shap or plot.surv_lime functions

Value

An object of the class ggplot.

Plot options

plot.surv_shap

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

  • ... - additional objects of class surv_shap 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

  • max_vars - maximum number of variables to be plotted (least important variables are ignored)

  • 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_lime

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

  • type - character, either "coefficients" or "local_importance", selects the type of plot

  • show_survival_function - logical, if the survival function of the explanations should be plotted next to the barplot

  • ... - other parameters currently ignored

  • 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

  • max_vars - maximum number of variables to be plotted (least important variables are ignored)

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

See also

Other functions for plotting 'predict_parts_survival' objects: plot.surv_lime(), plot.surv_shap()

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!  

p_parts_shap <- predict_parts(exp, veteran[1, -c(3, 4)], type = "survshap")
plot(p_parts_shap)


p_parts_lime <- predict_parts(exp, veteran[1, -c(3, 4)], type = "survlime")
plot(p_parts_lime)

# }