easy.glmnet (version 1.1)

glmnet_get.main.model: Get the main glmnet model across imputations and folds

Description

Function to choose the glmnet model most similar to the other models on the list according to the Dice coefficient.

Usage

glmnet_get.main.model(x, childname = NULL, verbose = TRUE)

Value

An object of class "glmnet_fit", representing the model most similar to the other models of the list according to the Dice coefficient.

Arguments

x

a list of objects of class "glmnet_fit" or a list of objects that have a child of class "glmnet_fit".

childname

name of the child of class "glmnet_fit" (if x) is a list of objects that have a child of class "glmnet_fit").

verbose

(optional) logical, whether to print some messages during execution.

Author

Joaquim Radua

Details

If there are several instances of the most similar model, it averages them.

References

Sobregrau, P., Bailles, E., Radua, J., Carreno, M., Donaire, A., Setoain, X., Bargallo, N., Rumia, J., Sanchez-Vives, M.V., Pintor, L. (2024) Design and validation of a diagnostic suspicion checklist to differentiate epileptic from psychogenic nonepileptic seizures (PNES-DSC). Journal of Psychosomatic Research, 180, 111656, doi:10.1016/j.jpsychores.2024.111656.

See Also

glmnet_predict for obtaining predictions. cv for conducting a cross-validation.

Examples

# Create random x (predictors) and y (binary)
x = matrix(rnorm(25000), ncol = 50)
y = 1 * (plogis(apply(x[,1:5], 1, sum) + rnorm(500, 0, 0.1)) > 0.5)

# Predict y via cross-validation
fit_fun = function (x_training, y_training) {
  list(
    lasso = glmnet_fit(x_training, y_training, family = "binomial")
  )
}
predict_fun = function (m, x_test) {
  glmnet_predict(m$lasso, x_test)
}
# Only 2 folds to ensure the example runs quickly
res = cv(x, y, family = "binomial", fit_fun = fit_fun, predict_fun = predict_fun, nfolds = 2)

# Show the main model
lasso = glmnet_get.main.model(res$models, "lasso")
cat(
  "Model: ~plogis(", round(lasso$a0, 2), "+",
  paste0(round(lasso$beta, 2), "*", names(lasso$beta), collapse = " + "),
  ")\n"
)