This functions plots objects of class surv_lime - LIME explanations of survival models
created using predict_parts(..., type="survlime") function.
# S3 method for surv_lime
plot(
x,
type = "local_importance",
show_survival_function = TRUE,
...,
title = "SurvLIME",
subtitle = "default",
max_vars = 7,
colors = NULL
)Arguments
- x
an object of class
"surv_lime"to be plotted- type
character, either "coefficients" or "local_importance" (default), 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")
Value
An object of the class ggplot.
See also
Other functions for plotting 'predict_parts_survival' objects:
plot.predict_parts_survival(),
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_lime <- predict_parts(exp, veteran[1, -c(3, 4)], type = "survlime")
plot(p_parts_lime)
# }