easy.glmnet (version 1.1)

glmnet_fit: Obtain and use a glmnet prediction model

Description

Function to easily fit and apply glmnet models (including best lambda estimation, etc).

Usage

glmnet_fit(x, y, family = c("binomial", "cox", "gaussian"),
           nfolds = 10, min.beta = 1e-12, ...)
glmnet_predict(m, x)

Value

An object of class "glmnet_fit", which is briefly a list with the intercept ("a0") and regressors ("beta") of the model; it also includes the indices of the regressors ("i") and the "family" of the response.

Arguments

x

input matrix of dimension nobs x nvars; each row is an observation vector. It can be easily obtained with data.frame2glmnet.matrix.

y

response to be predicted. A binary vector for "binomial", a "Surv" object for "cox", or a numeric vector for "gaussian".

family

distribution of y: "binomial", "cox", or "gaussian".

m

lasso model to conduct the prediction, obtained with glmnet_fit.

nfolds

number of folds.

min.beta

minimum value of betas.

...

further arguments passed to the function glmnet, such as standardize (a logical flag for x variable standardization, though the coefficients are always returned on the original scale).

Author

Joaquim Radua and Aleix Solanes

Details

The function glmnet_fit mainly calls the function glmnet to fit a generalized linear model with lasso regularization, though with some extra code to make the call easier: it allow x to have a single column, it conducts an internal cross-validation using the function cv.glmnet to select the regularization parameter lambda automatically, and it removes the negligible coefficients.

References

Solanes, A., Mezquida, G., Janssen, J., Amoretti, S., Lobo, A., Gonzalez-Pinto, A., Arango, C., Vieta, E., Castro-Fornieles, J., Berge, D., Albacete, A., Gine, E., Parellada, M., Bernardo, M.; PEPs group (collaborators); Pomarol-Clotet, E., Radua, J. (2022) Combining MRI and clinical data to detect high relapse risk after the first episode of psychosis. Schizophrenia, 8, 100, doi:10.1038/s41537-022-00309-w.

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.

See Also

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, nfold = 2)

# Show accuracy
se = mean(res$predictions$y.pred[res$predictions$y == 1] > 0.5)
sp = mean(res$predictions$y.pred[res$predictions$y == 0] < 0.5)
bac = (se + sp) / 2
cat("Sensitivity:", round(se, 2), "\n")
cat("Specificity:", round(sp, 2), "\n")
cat("Balanced accuracy:", round(bac, 2), "\n")