Plot transreg-object
plot.transreg.Rd
Plot transreg-object
Usage
# S3 method for 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
andlambda.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 (In press). doi:10.1093/bioinformatics/btad680 armin.rauschenberger@uni.lu
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")