# objective: Compute objective values In glamlasso: Penalization in Large Scale Generalized Linear Array Models

## Description

Computes the objective values of the penalized log-likelihood problem for the models implemented in the package glamlasso.

## Usage

 1 2 3 4 5 6 7 8 objective(Y, Weights, X, Beta, lambda, penalty.factor, family, penalty) 

## Arguments

 Y The response values, an array of size n_1 \times \cdots \times n_d. Weights Observation weights, an array of size n_1 \times \cdots \times n_d. X A list containing the tensor components of the tensor design matrix, each of size n_i \times p_i. Beta A coefficient matrix of size p_1\cdots p_d \times nlambda. lambda The sequence of penalty parameters for the regularization path. penalty.factor An array of size p_1 \times \cdots \times p_d. Is multiplied with each element in lambda to allow differential shrinkage on the coefficients. family A string specifying the model family (essentially the response distribution). penalty A string specifying the penalty.

## Value

A vector of length length(lambda) containing the objective values for each lambda value.

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 ## 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") objfit <- objective(Y, NULL, list(X1, X2, X3), fit$coef, fit$lambda, NULL, fit\$family) plot(objfit, type = "l") ## End(Not run) 

glamlasso documentation built on May 2, 2019, 2:18 a.m.