plot.transreg: Plot transreg-object

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

Plot transreg-object

Description

Plot transreg-object

Usage

## S3 method for class 'transreg'
plot(x, stack = NULL, ...)

Arguments

x	object of type transreg
stack	character "sta" (standard stacking) or "sim" (simultaneous stacking)
...	(not applicable)

Value

Returns four plots.

* top-left: Calibrated prior effects (y-axis) against original prior effects (x-axis). Each line is for one source of prior effects, with the colour given by [grDevices::palette()] (black: 1, red: 2, green: 3, blue: 4, ...).

* top-right: Estimated coefficients with transfer learning (y-axis) against estimated coefficients without transfer learning (x-axis). Each point represents one feature.

* bottom-left: Estimated weights for sources of prior effects (labels 1 to k), and either estimated weights for 'lambda.min' and 'lambda.1se' models (standard stacking) or estimated weights for features (simultaneous stacking).

* bottom-right: Absolute deviance residuals (y-axis) against fitted values (x-axis). Each point represents one 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)
n <- 100; p <- 500
X <- matrix(rnorm(n=n*p),nrow=n,ncol=p)
beta <- rnorm(p) #*rbinom(n=n,size=1,prob=0.2)
prior1 <- beta + rnorm(p)
prior2 <- beta + rnorm(p)
prior3 <- rnorm(p)
prior4 <- rnorm(p)
y <- X %*% beta

prior <- cbind(prior1,prior2,prior3,prior4)
object <- transreg(y=y,X=X,prior=prior,alpha=0,stack=c("sta","sim"))

plot(object,stack="sta")

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