easy.glmnet (version 1.1)

glmnet_get.items.relevance: Get the relevance of the model items

Description

Function to calculate the relevance of the items of a model or of a list of models.

Usage

glmnet_get.items.relevance(x, childname = NULL)

Value

A numeric vector representing the relevance of the items of the model.

Arguments

x

an object of class "glmnet_fit", 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").

Author

Joaquim Radua, based on the previous work of others (see Details)

Details

The relevance is calculated as abs( standardized_coefficient ) / sum(abs( standardized_coefficients )), as in the function lasso_vars.

References

Palau, P., Solanes, A., Madre, M., Saez-Francas, N., Sarro, S., Moro, N., Verdolini, N., Sanchez, M., Alonso-Lana, S., Amann, B.L., Romaguera, A., Martin-Subero, M., Fortea, L., Fuentes-Claramonte, P., Garcia-Leon, M.A., Munuera, J., Canales-Rodriguez, E.J., Fernandez-Corcuera, P., Brambilla, P., Vieta, E., Pomarol-Clotet, E., Radua, J. (2023) Improved estimation of the risk of manic relapse by combining clinical and brain scan data. Spanish Journal of Psychiatry and Mental Health, 16, 235--243, doi:10.1016/j.rpsm.2023.01.001.

Salazar de Pablo, G., Radua, J., Frearson, G., Young, A.H., Arango, C., Kelleher, I., Sharma, A., Uhlhaas, P.J., Solmi, M., Fusar-Poli, P., Guinart, D., Correll, C.U. (2025) Development and validation of a prognostic model and risk calculator for the estimation of bipolar-spectrum disorder risk in hospitalised adolescents with non-psychotic/non-bipolar mental disorders. Molecular Psychiatry, in Press, doi:10.1038/s41380-025-03244-1.

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 relevance of the predictors
relevance = glmnet_get.items.relevance(res$models, "lasso")
relevance = relevance[which(relevance >= 0.01)] # Select items with >=1% relevance
round(relevance, 2)