Predicts outcome from features with stacked model.
Usage
# S3 method for class 'joinet'
predict(object, newx, type = "response", ...)
Arguments
- object
joinet object
- newx
covariates:
numeric matrix with \(n\) rows (samples)
and \(p\) columns (variables)
- type
character "link" or "response"
- ...
further arguments (not applicable)
Value
This function returns predictions from base and meta learners.
The slots base and meta each contain a matrix
with \(n\) rows (samples) and \(q\) columns (variables).
Examples
if (FALSE) { # \dontrun{
n <- 50; p <- 100; q <- 3
X <- matrix(rnorm(n*p),nrow=n,ncol=p)
Y <- replicate(n=q,expr=rnorm(n=n,mean=rowSums(X[,1:5])))
Y[,1] <- 1*(Y[,1]>median(Y[,1]))
object <- joinet(Y=Y,X=X,family=c("binomial","gaussian","gaussian"))
predict(object,newx=X)} # }