Function `model_performance`

calculates the prediction error for chosen survival model.

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

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

... | other parameters |

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.

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/.

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)#>