PatientLevelPrediction (version 4.3.10)

pfi: pfi

Description

Calculate the permutation feature importance for a PLP model.

Usage

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.