predict.transreg: Make Predictions

In transreg: Penalised Regression with Multiple Sets of Prior Effects ('Transfer Learning')

Make Predictions

Description

Predicts outcome

Usage

## S3 method for class 'transreg'
predict(object, newx, stack = NULL, ...)

Arguments

object	object of class 'transreg'
newx	features: matrix with n rows (samples) and p columns (variables)
stack	character "sta" (standard stacking) or "sim" (simultaneous stacking)
...	(not applicable)

Value

Returns predicted values or predicted probabilities. The output is a column vector with one entry for each sample.

References

Armin Rauschenberger, Zied Landoulsi, Mark A. van de Wiel, and Enrico Glaab (2023). "Penalised regression with multiple sets of prior effects". Bioinformatics 39(12):btad680. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btad680")}. (Click here to access PDF.)

See Also

Methods for objects of class [transreg] include coef and predict.

Examples

#--- simulation ---
set.seed(1)
n0 <- 100; n1 <- 10000; n <- n0 + n1; p <- 500
X <- matrix(rnorm(n=n*p),nrow=n,ncol=p)
beta <- rnorm(p)
prior <- beta + rnorm(p)
y <- X %*% beta

#--- train-test split ---
foldid <- rep(c(0,1),times=c(n0,n1))
y0 <- y[foldid==0]
X0 <- X[foldid==0,]
y1 <- y[foldid==1]
X1 <- X[foldid==1,]

#--- glmnet (without prior effects) ---
object <- glmnet::cv.glmnet(y=y0,x=X0)
y_hat <- predict(object,newx=X1,s="lambda.min")
mean((y1-y_hat)^2)

#--- transreg (with prior effects) ---
object <- transreg(y=y0,X=X0,prior=prior)
y_hat <- predict(object,newx=X1)
mean((y1-y_hat)^2) # decrease in MSE?

transreg documentation built on June 10, 2025, 5:14 p.m.