easy.glmnet (version 1.1)

impute.glmnet.matrix_fit: Impute missing variables in a glmnet matrix multiple times

Description

Function to impute, multiple times, the missing variables in a glmnet.matrix. impute.glmnet.matrix_fit finds the "lasso" models to conduct the imputations, and impute.glmnet.matrix does the imputations (in the same or a different dataset).

Usage

impute.glmnet.matrix_fit(x, ncores = 1, verbose = TRUE)
impute.glmnet.matrix(m, x, nimp = 20, verbose = TRUE)

Value

A list of complete matrixes ready for glmnet_fit and glmnet_predict.

Arguments

m

model to conduct the imputations, obtained with impute.glmnet.matrix_fit.

x

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

ncores

number of number of worker nodes (for parallelization).

nimp

number of imputations

verbose

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

Author

Joaquim Radua and Aleix Solanes

Details

The user can then obtain a prediction from each dataset and combine the predictions using Rubin's rules (which usually means just averaging them). Note also that this function may take a lot of time.

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.

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

# Quick example

# Create random x with missing values
x = matrix(rnorm(300), ncol = 3)
x = x + rnorm(1) * x[,sample(1:3)] + rnorm(1) * x[,sample(1:3)]
x[sample(1:300, 30)] = NA

# Impute missing values
m_impute = impute.glmnet.matrix_fit(x, ncores = 2)
x_imputed = impute.glmnet.matrix(m_impute, x)


# Complete example (it might take some time even if the example is simple...)
# \donttest{
  # Create random x (predictors) and y (binary)
  x = matrix(rnorm(4000), ncol = 20)
  x = x + rnorm(1) * x[,sample(1:20)] + rnorm(1) * x[,sample(1:20)]
  y = 1 * (plogis(x[,1] - x[,2] + rnorm(200, 0, 0.1)) > 0.5)
  
  # Make some x missing values
  x[sample(1:4000, 400)] = NA
  
  # Predict y via cross-validation, including imputations
  fit_fun = function (x_training, y_training) {
    m = list(
      impute = impute.glmnet.matrix_fit(x_training, ncores = 1),
      lasso = list()
    )
    x_imputed = impute.glmnet.matrix(m$impute, x_training)
    for (imp in 1:length(x_imputed)) {
      m$lasso[[imp]] = glmnet_fit(x_imputed[[imp]], y_training, family = "binomial")
    }
    m
  }
  predict_fun = function (m, x_test) {
    x_imputed = impute.glmnet.matrix(m$impute, x_test)
    y_pred = NULL
    for (imp in 1:length(x_imputed)) {
      y_pred = cbind(y_pred, glmnet_predict(m$lasso[[imp]], x_imputed[[imp]]))
    }
    apply(y_pred, 1, mean)
  }
  # 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 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")
# }