R/SMME_print.R

In SMME: Soft Maximin Estimation for Large Scale Heterogeneous Data

#' @title Print Function for objects of Class SMME
#'
#' @description This function will print some information about the SMME object.
#'
#' @param x a SMME object
#' @param ... ignored
#'
#'
#' @examples
#'
#' ##size of example
#' n1 <- 65; n2 <- 26; n3 <- 13; p1 <- 13; p2 <- 5; p3 <- 4
#'
#' ##marginal design matrices (Kronecker components)
#' X1 <- matrix(rnorm(n1 * p1, 0, 0.5), n1, p1)
#' X2 <- matrix(rnorm(n2 * p2, 0, 0.5), n2, p2)
#' X3 <- matrix(rnorm(n3 * p3, 0, 0.5), n3, p3)
#' X <- list(X1, X2, X3)
#'
#' component <- rbinom(p1 * p2 * p3, 1, 0.1)
#' Beta1 <- array(rnorm(p1 * p2 * p3, 0, .1) + component, c(p1 , p2, p3))
#' Beta2 <- array(rnorm(p1 * p2 * p3, 0, .1) + component, c(p1 , p2, p3))
#' mu1 <- RH(X3, RH(X2, RH(X1, Beta1)))
#' mu2 <- RH(X3, RH(X2, RH(X1, Beta2)))
#' Y1 <- array(rnorm(n1 * n2 * n3, mu1), dim = c(n1, n2, n3))
#' Y2 <- array(rnorm(n1 * n2 * n3, mu2), dim = c(n1, n2, n3))
#'
#' Y <- array(NA, c(dim(Y1), 2))
#' Y[,,, 1] <- Y1; Y[,,, 2] <- Y2;
#'
#' fit <- softmaximin(X, Y, zeta = 10, penalty = "lasso", alg = "npg")
#' fit
# @return The matrix above is silently returned
#' @author Adam Lund
#' @method print SMME
#' @export

print.SMME <- function(x, ...) {

if(length(x$zeta) > 1){
out <- vector(mode = "list", length = length(x$zeta))
names(out) <- x$zeta
for(i in 1:length(x$zeta)){
out[[i]] <- data.frame(Df = x$df[[i]], lambda = x$lambda[[i]])
}
}else{
out <- data.frame(Df = x$df, lambda = x$lambda)
}

print(out)
}