summary.libsl: 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 libsl
summary(object, newdata=NULL, ROC.precision=seq(.01,.99,.01), digits=7, ...)

Value

No return value for this S3 method.

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.

Examples

Run this code

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")