safe_selection: safe_selection

In PheVis: Automatic Phenotyping of Electronic Health Record at Visit Resolution

safe_selection

Description

Select the variables from dataframe by removing the rare variables and apply 'SAFE' on it.

Usage

safe_selection(
  df,
  var_surrogate,
  surrogate_quali,
  threshold = 0.05,
  alpha = 0.5,
  remove_var_surrogate = TRUE,
  bool_weight = FALSE,
  ...
)

Arguments

df	dataframe
var_surrogate	variables used for building the surrogates
surrogate_quali	surrogate with 3 values (0 and 1 the extremes and 3 middle patients)
threshold	rareness threshold (default = 0.05).
alpha	glmnet parameter (default is 0.5 elastic net)
remove_var_surrogate	does the glmnet algorithm should learn on features in var_surrogate (default is TRUE).
bool_weight	Should the glmnet function be weighted to balance the extrema populations (default is FALSE).
...	arguments to pass to pretty_cv.glmnet

Value

A list

• glmnet_model - A list of three elements: the cv.glmnet fitted model, the coefficients of non zero variables and the vector of non zero coefficient variables.

• important_var - A vector with the variables used for the surrogate and the non zero variables.

• surrogate_quali - The surrogate_quali argument.

PheVis documentation built on Oct. 20, 2023, 9:08 a.m.