Function model_performance calculates the prediction error for chosen survival model.

model_performance(explainer, type = "BS", ...)

## Arguments

explainer a model to be explained, preprocessed by the 'survxai::explain' function character - type of the response to be calculated Currently following options are implemented: 'BS' for Expected Brier Score other parameters

## Details

For type = "BS" prediction error is the time dependent estimates of the population average Brier score. At a given time point t, the Brier score for a single observation is the squared difference between observed survival status and a model based prediction of surviving time t.

## References

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL http://www.jstatsoft.org/v50/i11/.

## Examples

   library(survxai)
library(rms)
data("pbcTrain")
data("pbcTest")
cph_model <- cph(Surv(years, status)~., 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))
mp_cph <- model_performance(surve_cph)#> No covariates  specified: Kaplan-Meier for censoring times used for weighting.