R/predict.design.matrix.bnp.R

In AROC: Covariate-Adjusted Receiver Operating Characteristic Curve Inference

predict.design.matrix.bnp <-
function(object, newdata, ...) {
	if(object$iformula$npartial == 0) { # Only the intercept
        Xp <- matrix(1, ncol = 1, nrow = nrow(newdata))
    } else { 
		Xp <- NULL
		# Organize the newdataframe as it was in the original data
		newdata <- newdata[,names(object$iformula$data.cov), drop = FALSE]
		for(i in 1:object$iformula$npartial) {
			if(any(object$iformula$II[,i] == -1)) {
				if(object$iformula$h[i] == 0 | object$iformula$h[i] == 1) { # Linear and factor
					mfp <- model.frame(object$terms[[i]], newdata, xlev = attr(object$terms[[i]], "xlev"))
					Xp <- cbind(Xp, model.matrix(object$terms[[i]], data = mfp, contrasts.arg = attr(object$terms[[i]], "contrast"))[,-1,drop = FALSE])
				} else {
					Bs <- suppressWarnings(predict.bbase.os(object$terms[[i]], newdata[,object$iformula$II[2,i]]))
					Xp <- cbind(Xp, Bs)
				}
			} else { # Factor by curve
				Bs <- predict.bbase.interaction.factor.by.curve.os(object$terms[[i]], newdata[,object$iformula$II[2,i]], newdata[,object$iformula$II[1,i]])
				Xp <- cbind(Xp, Bs)
			}

		}
		# Add the intercept
		Xp <- cbind(1, Xp)
	}
	res <- list()
	res$X <- Xp
	res
}