en_kfold_model: en_kfold_model function

In clmacleod/highlandr: Random Useful Functions

en_kfold_model function

Description

run kfold cross validated enr models (DOCUMENTATION COMING- CURRENT DOCUMENTATION INCORRECT)

Usage

en_kfold_model(
  ddata,
  response_var,
  iter = 10,
  k = 10,
  num_alpha = 20,
  seed = 123,
  fit_met = "accuracy",
  loo = FALSE,
  up_dn_samp = "none",
  eq_wt = FALSE,
  type_meas = "deviance",
  na_rm = TRUE,
  lr_cutoff = c(0.5),
  accuracy_modeling = FALSE
)

Arguments

ddata	data frame containing the data to be modeled
response_var	string identifying the name of the outcome variable
iter	the number of iterations to use
k	the number of folds to use
num_alpha	an integer of the number of alphas to consider. this will be split across 0 to 1. for example if '5' is given then alphas will go from 0 to 1 and will be num_alpha/iteration (i.e. 0, .2, .4, .6, .8, 1)
seed	the seed value for allowing results to be reproduced
fit_met	string indicating the fit metric to be used to evaluate model performance. options are c(accuracy, auroc, logloss, f1, ppv, npv, sens, spec, bal_acc)
loo	boolean indicating whether 'leave one out' cross validation should be used
up_dn_samp	string indicating whether unbalanced classes should be balanced by having the smaller class upsampled to be the same size as the larger class or vice versa. can take the form 'upsamp', 'downsamp', and 'none' (default)
eq_wt	boolean indicating whether the 0/1 classes should be balanced with weights. you may want to use this if there is a bad class imbalance
type_meas	the 'type measure' which is passed to cv.glmnet that governs its training penalty when tuning lambda. this should match arguments expected in cv.glmnet
na_rm	boolean indicating whether missing values should be removed. default is TRUE
lr_cutoff	vetor of cutoff values to test/tune for optimization. the default is 'c(.5)' which is to say 'equal distance from all classes' which is typical in standard analyses
accuracy_modeling	switch determining if we need to break ties between optimal solutions

Examples

en_kfold_model()

clmacleod/highlandr documentation built on April 17, 2025, 3:30 a.m.