predict.glamlasso {glamlasso} | R Documentation |
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).
## S3 method for class 'glamlasso' predict(object, x = NULL, X = NULL, ...)
object |
An object of Class glamlasso, produced with |
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 |
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\}.
Adam Lund
## Not run: 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, family = "gaussian", penalty = "lasso", iwls = "exact") ##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]] ## End(Not run)