plot.surv_ceteris_paribus_explainer: Plot for ceteris_paribus object

In MI2DataLab/survxai: Visualization of the Local and Global Survival Model Explanations

Plot for ceteris_paribus object

Description

Function plot for ceteris_paribus object visualise estimated survival curve of mean probabilities in chosen time points. Black lines on each plot correspond to survival curve for our new observation specified in the ceteris_paribus function.

Usage

## S3 method for class 'surv_ceteris_paribus_explainer'
plot(
  x,
  ...,
  selected_variable = NULL,
  scale_type = "factor",
  scale_col = NULL,
  ncol = 1
)

Arguments

x	object of class "surv_ceteris_paribus_explainer"
...	arguments to be passed to methods, such as graphical parameters for function geom_step.
selected_variable	name of variable we want to draw ceteris paribus plot
scale_type	type of scale of colors, either "discrete" or "gradient"
scale_col	vector containing values of low and high ends of the gradient, when "gradient" type of scale was chosen
ncol	number of columns for faceting

Examples

library(survxai)
library(rms)
data("pbcTest")
data("pbcTrain")
predict_times <- function(model, data, times){
                  prob <- rms::survest(model, data, times = times)$surv
                  return(prob)
                  }
cph_model <- cph(Surv(years, status)~sex + bili + stage, data=pbcTrain, surv=TRUE, x = TRUE, y=TRUE)
surve_cph <- explain(model = cph_model, data = pbcTest[,-c(1,5)],
             y = Surv(pbcTest$years, pbcTest$status), predict_function = predict_times)
cp_cph <- ceteris_paribus(surve_cph, pbcTest[1,-c(1,5)])
plot(cp_cph)

MI2DataLab/survxai documentation built on Sept. 10, 2022, 12:03 a.m.