lqa.control: Auxiliary for controlling lqa fitting

In lqa: Penalized Likelihood Inference for GLMs

Description

Auxiliary function as user interface for lqa fitting. Typically only used when calling lqa or lqa.update2.

Usage

   lqa.control (x = NULL, var.eps = .Machine$double.eps, max.steps = 5000, 
            conv.eps = 0.001, conv.stop = TRUE, c1 = 1e-08, 
            digits = 5, ...)

Arguments

x	object of class 'lqa'. This optional argument is just included to be in line with the S3 class concept.
var.eps	tolerance in checking for zero variance of some regressors.
max.steps	maximum number of steps in the lqa algorithm.
conv.eps	tolerance for convergence break in parameter updating.
conv.stop	whether or not to stop the iterations when estimated coefficients are converged.
c1	controls the amount of approximation of linear combinations in the penalty term.
digits	number of digits of tuning parameter candidates to take into consideration when returning the loss array and mean array in cv.lqa.
...	further arguments.

Value

A list with the arguments as components.

Author(s)

Jan Ulbricht

See Also

lqa, cv.lqa

Examples

set.seed (1111)

n <- 200
p <- 5
X <- matrix (rnorm (n * p), ncol = p)
X[,2] <- X[,1] + rnorm (n, sd = 0.1)
X[,3] <- X[,1] + rnorm (n, sd = 0.1)
true.beta <- c (1, 2, 0, 0, -1)
y <- drop (X %*% true.beta) + rnorm (n)

control.obj <- lqa.control (max.steps = 200, conv.eps = 1e-3, 
   conv.stop = FALSE)
obj <- lqa (y ~ X, family = gaussian (), penalty = lasso (1.5), 
            control = control.obj)
obj$coef

lqa documentation built on May 30, 2017, 3:41 a.m.