pfi: Permutation Feature Importance

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

Permutation Feature Importance

Description

Calculate the permutation feature importance (pfi) 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, ...)

Details

The function permutes the each covariate/features repeats times and calculates the mean AUC change caused by the permutation.

Value

A dataframe with the covariateIds and the pfi (change in AUC caused by permuting the covariate) value

Examples

 
library(dplyr)
# simulate some data
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=1000)
# now fit a model
saveLoc <- file.path(tempdir(), "pfi")
plpResult <- runPlp(plpData, outcomeId = 3, saveDirectory = saveLoc)
population <- createStudyPopulation(plpData, outcomeId = 3)
pfi(plpResult, population, plpData, repeats = 1, cores = 1)
# compare to model coefficients
plpResult$model$covariateImportance %>% dplyr::filter(.data$covariateValue != 0)
# clean up
unlink(saveLoc, recursive = TRUE)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.