Calculate the permutation feature importance for a PLP model.
pfi(
plpResult,
population,
plpData,
repeats = 1,
covariates = NULL,
cores = NULL,
log = NULL,
logthreshold = "INFO"
)Arguments
- plpResult
An object of type
runPlp- population
The population created using createStudyPopulation() who will have their risks predicted
- plpData
An object of type
plpData- the patient level prediction data extracted from the CDM.- repeats
The number of times to permute each covariate
- covariates
A vector of covariates to calculate the pfi for. If NULL it uses all covariates included in the model.
- cores
Number of cores to use when running this (it runs in parallel)
- log
A location to save the log for running pfi
- logthreshold
The log threshold (e.g., INFO, TRACE, ...)
Value
A dataframe with the covariateIds and the pfi (change in AUC caused by permuting the covariate) value
Details
The function permutes the each covariate/features <repeats> times and calculates the mean AUC change caused by the permutation.