summary.libsl {survivalSL}

Summaries of a Learner

Description

Return predictive performances of a model or algorithm obtained by a library of the class libsl.

Usage

## S3 method for class 'libsl'
summary(object, newdata=NULL, ROC.precision=seq(.01,.99,.01), digits=7, ...)

Arguments

object

An object returned by a library of the class libsl.

newdata

An optional data frame containing the new sample for validation with covariate values, follow-up times, and event status. The default value is NULL, the calibration plot is performed from the same subjects of the training sample.

ROC.precision

An optional argument with the percentiles (between 0 and 1) of the prognostic variable used for computing each point of the time dependent ROC curve. 0 (min) and 1 (max) are not allowed. By default, the precision is seq(.01,.99,.01).

digits

An optional integer for the number of digits to print when printing numeric values.

...

Additional arguments affecting the summary which are passed from libsl by default. It concerns the argument times, failures, and pro.time.

Details

The following metrics are returned: "brier" for the Brier score at the prognostic time pro.time, "ibs" for the Integrated Brier score up to the last observed time of event, "ibll" for the Integrated Binomial Log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "ribs" for the restricted Integrated Brier score up to the prognostic time pro.time, "ribll" for the restricted Integrated Binomial Log-likelihood Log-likelihood up to the last observed time of event, "bll" for the binomial Log-likelihood, "auc" for the area under the time-dependent ROC curve up to the prognostic time pro.time.

Value

No return value for this S3 method.

See Also

LIB_AFTgamma, LIB_AFTggamma, LIB_AFTllogis, LIB_AFTweibull, LIB_PHexponential, LIB_PHgompertz.

Examples

data(dataDIVAT2)

# The training of the Weibull model with the first 400 patients
model <- LIB_PHgompertz(times="times", failures="failures", data=dataDIVAT2[1:400,],
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"))

# The prognostic capacities from the same training sample
# (up to 4 years forseveral indicators)
summary(model, pro.time=4)

# The prognostic capacities from a validation of the next 150 patients
# (up to 4 years for several indicators)
 summary(model, pro.time=4, newdata=dataDIVAT2[401:550,], times="times",
 failures="failures")