predict.sparselink: Out-of-sample Predictions

Description

Predicts outcomes with a multi-task or transfer learning regression model.

Usage

# S3 method for sparselink
predict(object, newx, weight = NULL, ...)

Value

Returns predicted values or predicted probabilities. The output is a list of \(q\) column vectors of length \(n_k\)

for \(k\) in \(1,\ldots,q\). Each vector corresponds to one target (multi-task learning) or one dataset (transfer learning).

Arguments

object: object of class "sparselink" (generated by function sparselink)
newx: features: matrix with \(n\) rows (samples) and \(p\) columns (variables) for multi-task learning; list of \(q\) matrices with \(n_k\) rows (samples) and \(p\) columns (variables) for transfer learning, for each \(k\) in \(1,\ldots,q\)
weight: hyperparameters for scaling external and internal weights: numeric vector of length 2, with the first entry for the external weights (prior coefficients from source data), and the second entry for the internal weights (prior coefficients from target data), selected values must be among the candidate values, default: NULL (using cross-validated weights)
...: (not applicable)

References

Armin Rauschenberger, Petr N. Nazarov, and Enrico Glaab (2025). "Estimating sparse regression models in multi-task learning and transfer learning through adaptive penalisation". Under revision. https://hdl.handle.net/10993/63425

Examples

Run this code

family <- "gaussian"
type <- "multiple" # try "multiple" or "transfer"
if(type=="multiple"){
 data <- sim_data_multi(family=family)
} else if(type=="transfer"){
 data <- sim_data_trans(family=family)
}
data <- sim_data_multi(family=family,n0=10,p=3)
object <- sparselink(x=data$X_train,y=data$y_train,family=family)
y_hat <- predict(object=object,newx=data$X_test)