View source: R/plot_predict_parts_survival.R
plot.predict_parts_survival | R Documentation |
This function plots objects of class "predict_parts_survival"
- local explanations
for survival models created using the predict_parts()
function.
## S3 method for class 'predict_parts_survival'
plot(x, ...)
x |
an object of class |
... |
additional parameters passed to the |
An object of the class ggplot
.
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")
Other functions for plotting 'predict_parts_survival' objects:
plot.surv_lime()
,
plot.surv_shap()
library(survival)
library(survex)
model <- randomForestSRC::rfsrc(Surv(time, status) ~ ., data = veteran)
exp <- explain(model)
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)
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