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Plot for PredictionSurv

Source: R/autoplot.R
autoplot.PredictionSurv.Rd

Generates plots for PredictionSurv, depending on argument type:

  • "calib" (default): Calibration plot comparing the average predicted survival distribution to a Kaplan-Meier prediction, this is not a comparison of a stratified crank or lp prediction. object must have distr prediction. geom_line() is used for comparison split between the prediction (Pred) and Kaplan-Meier estimate (KM). In addition labels are added for the x (T) and y (S(T)) axes.

  • "dcalib": Distribution calibration plot. A model is D-calibrated if X% of deaths occur before the X/100 quantile of the predicted distribution, e.g. if 50% of observations die before their predicted median survival time. A model is D-calibrated if the resulting plot lies on x = y.

  • "preds": Matplots the survival curves for all predictions

Usage

# S3 method for PredictionSurv
autoplot(
  object,
  type = "calib",
  task = NULL,
  row_ids = NULL,
  times = NULL,
  xyline = TRUE,
  cuts = 11L,
  theme = theme_minimal(),
  extend_quantile = FALSE,
  ...
)

Arguments

object

(PredictionSurv).

type

(character(1))
Name of the column giving the type of censoring. Default is 'right' censoring.

task

(TaskSurv)
If type = "calib" then task is passed to $predict in the Kaplan-Meier learner.

row_ids

(integer())
If type = "calib" then row_ids is passed to $predict in the Kaplan-Meier learner.

times

(numeric())
If type = "calib" then times is the values on the x-axis to plot over, if NULL uses all times from task.

xyline

(logical(1))
If TRUE (default) plots the x-y line for type = "dcalib".

cuts

(integer(1))
Number of cuts in (0,1) to plot dcalib over, default is 11.

theme

(ggplot2::theme())
The ggplot2::theme_minimal() is applied by default to all plots.

extend_quantile

(logical(1))
If TRUE then dcalib will impute NAs from predicted quantile function with the maximum observed outcome time, e.g. if the last predicted survival probability is greater than 0.1, then the last predicted cdf is smaller than 0.9 so F^1(0.9) = NA, this would be imputed with max(times). Default is FALSE.

...

(any): Additional arguments, currently unused.

References

Haider H, Hoehn B, Davis S, Greiner R (2020). “Effective Ways to Build and Evaluate Individual Survival Distributions.” Journal of Machine Learning Research, 21(85), 1-63. https://jmlr.org/papers/v21/18-772.html.

Examples

library(mlr3)
library(mlr3proba)
library(mlr3viz)

learn = lrn("surv.coxph")
task = tsk("unemployment")
p = learn$train(task, row_ids = 1:300)$predict(task, row_ids = 301:400)

# calibration by comparison of average prediction to Kaplan-Meier
autoplot(p, type = "calib", task = task, row_ids = 301:400)


# Distribution-calibration (D-Calibration)
autoplot(p, type = "dcalib")
#> Warning: All aesthetics have length 1, but the data has 11 rows.
#> ℹ Did you mean to use `annotate()`?
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_line()`).


# Predictions
autoplot(p, type = "preds")