objective {glamlasso} | R Documentation |
Compute objective values
Description
Computes the objective values of the penalized log-likelihood problem for the models implemented in the package glamlasso.
Usage
objective(Y,
Weights,
X,
Beta,
lambda,
penalty.factor,
family,
penalty)
Arguments
Y |
The response values, an array of size |
Weights |
Observation weights, an array of size |
X |
A list containing the tensor components of the tensor design matrix, each of size
|
Beta |
A coefficient matrix of size |
lambda |
The sequence of penalty parameters for the regularization path. |
penalty.factor |
An array of size |
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
## 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)
[Package glamlasso version 3.0.1 Index]