predict.glamlasso: Make Prediction From a glamlasso Object

In glamlasso: Penalization in Large Scale Generalized Linear Array Models

Description

Given new covariate data this function computes the linear predictors based on the estimated model coefficients in an object produced by the function glamlasso. Note that the data can be supplied in two different formats: i) as a n' \times p matrix (p is the number of model coefficients and n' is the number of new data points) or ii) as a list of two or three matrices each of size n_i' \times p_i, i = 1, 2, 3 (n_i' is the number of new marginal data points in the ith dimension).

Usage

## S3 method for class 'glamlasso'
predict(object, x = NULL, X = NULL, ...)

Arguments

object	An object of Class glamlasso, produced with glamlasso.
x	a matrix of size n' \times p with n' is the number of new data points.
X	A list containing the data matrices each of size n'_{i} \times p_i, where n'_{i} is the number of new data points in the ith dimension.
...	ignored

Value

A list of length nlambda containing the linear predictors for each model. If new covariate data is supplied in one n' \times p matrix x each item is a vector of length n'. If the data is supplied as a list of matrices each of size n'_{i} \times p_i, each item is an array of size n'_1 \times \cdots \times n'_d, with d\in \{2,3\}.

Author(s)

Adam Lund

Examples

 
n1 <- 65; n2 <- 26; n3 <- 13; p1 <- 13; p2 <- 5; p3 <- 4
X1 <- matrix(rnorm(n1 * p1), n1, p1) 
X2 <- matrix(rnorm(n2 * p2), n2, p2) 
X3 <- matrix(rnorm(n3 * p3), n3, p3) 
Beta <- array(rnorm(p1 * p2 * p3) * rbinom(p1 * p2 * p3, 1, 0.1), c(p1 , p2, p3))
mu <- RH(X3, RH(X2, RH(X1, Beta)))
Y <- array(rnorm(n1 * n2 * n3, mu), dim = c(n1, n2, n3))
fit <- glamlasso(list(X1, X2, X3), Y)

##new data in matrix form
x <- matrix(rnorm(p1 * p2 * p3), nrow = 1)
predict(fit, x = x)[[100]]

##new data in tensor component form
X1 <- matrix(rnorm(p1), nrow = 1)
X2 <- matrix(rnorm(p2), nrow = 1)
X3 <- matrix(rnorm(p3), nrow = 1)
predict(fit, X = list(X1, X2, X3))[[100]]

glamlasso documentation built on May 17, 2021, 1:08 a.m.