objective {glamlasso} | R Documentation |
Computes the objective values of the penalized log-likelihood problem for the models implemented in the package glamlasso.
objective(Y, Weights, X, Beta, lambda, penalty.factor, family, penalty)
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 |
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 |
family |
A string specifying the model family (essentially the response distribution). |
penalty |
A string specifying the penalty. |
A vector of length length(lambda)
containing the objective values for each lambda
value.
## 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)