en_kfold_accuracy: en_kfold_accuracy function

In clmacleod/highlandr: Random Useful Functions

en_kfold_accuracy function

Description

This function calculates the cross validated accuracy of an enr model

Usage

en_kfold_accuracy(
  ddata,
  response_var,
  mod_alpha,
  mod_lambda = NULL,
  iter = 100,
  k = 10,
  seed = 123,
  loo = FALSE,
  eq_wt = FALSE,
  type_meas = "deviance",
  lr_cutoff = c(0.5)
)

Arguments

ddata	data frame containing the data to be modeled
response_var	string identifying the name of the outcome variable
mod_alpha	the alpha(s) value(s) to be checked. alpha is the ENR blending parameter that governs how much ridge regression (0) and lasso regression (1) will be used.
mod_lambda	the lambda(s) to be checked. lambda is the ENR penalty parameter for the ridge portion of the ENR
iter	the number of iterations to use
k	the number of folds to use
seed	the seed value for allowing results to be reproduced
loo	boolean indicating whether 'leave one out' cross validation should be used
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
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

Examples

en_kfold_accuracy()

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