phecap_run_feature_extraction: Run Surrogate-Assisted Feature Extraction (SAFE)

In PheCAP: High-Throughput Phenotyping with EHR using a Common Automated Pipeline

Description

Run surrogate-assisted feature extraction (SAFE) using unlabeled data and subsampling.

Usage

phecap_run_feature_extraction(
  data, surrogates,
  subsample_size = 1000L, num_subsamples = 200L,
  dropout_proportion = 0, frequency_cutoff = 0.5,
  start_seed = 45600L, verbose = 0L)

Arguments

data	An object of class PhecapData, obtained by calling PhecapData(...)
surrogates	A list of objects of class PhecapSurrogate, obtained by something like list(PhecapSurrogate(...), PhecapSurrogate(...))
subsample_size	An integer scalar giving the size of each subsample
num_subsamples	The number of subsamples drawn for each surrogate
dropout_proportion	A scalar between 0 and 1. If it is positive, for each predictor a random subset of observations will be set to zero
frequency_cutoff	A scalar between 0 and 1. Variables selected in at least this proportion of the subsamples are the variables finally selected
start_seed	in the i-th subsample, the seed is set to start_seed + i
verbose	print progress every verbose subsample if verbose is positive, or remain quiet if verbose is zero

Details

In this unlabeled setting, the extremes of each surrogate are used to define cases and controls. The variables selected are those selected in at least half (or the proportion specified) of the subsamples.

Value

An object of class PhecapFeatureExtraction, with components

selected	the names of selected features
frequency	the proportion of being selected for each feature

See Also

See PheCAP-package for code examples.

PheCAP documentation built on Sept. 17, 2020, 5:07 p.m.