cat_control: Auxiliary Function for gvcm.cat

In gvcm.cat: Regularized Categorical Effects/Categorical Effect Modifiers/Continuous/Smooth Effects in GLMs

Description

Auxiliary function for gvcm.cat. Modifies the algorithm's internal parameters.

Usage

cat_control(center = FALSE, standardize = FALSE, accuracy = 2, digits = 4,
g = 0.5, epsilon = 10^(-5), maxi = 250, c = 10^(-5), gama = 20, steps = 25, 
nu = 1, tuning.criterion = "GCV", K = 5, cv.refit = FALSE, 
lambda.upper=50, lambda.lower=0, lambda.accuracy=.01, scaled.lik=FALSE,
adapted.weights=FALSE, adapted.weights.adj = FALSE, adapted.weights.ridge =
FALSE, assured.intercept=TRUE, 
level.control = FALSE, case.control = FALSE, pairwise = TRUE, 
grouped.cat.diffs = FALSE, bootstrap = 0, start.ml = FALSE, L0.log = TRUE, 
subjspec.gr = FALSE, high = NULL, ...)

Arguments

center	logical; if TRUE, all metric covariates are centered by their empirical mean
standardize	logical; if TRUE, the design matrix is standardized by its (weighted) empirical variances
accuracy	integer; number of digits being compared when setting coefficents equal/to zero
digits	integer; number of digits for estimates
g	step length parameter for the PIRLS-algorithm; out of )0,1(
epsilon	small, positive, real constant; the PIRLS-algorithm is terminated when the (scaled, absolute) difference of the coefficients of the current iteration and the coefficients of the previous iteration is smaller than epsilon
maxi	integer; maximal number of iterations in the fitting algorithm
c	small, positive, real constant; needed for the approximation of the absolute value function in the PIRLS-algorithm
gama	positive number; tuning parameter for the approximation of the L0 norm
steps	integer; tuning parameter for path-plotting; minimal number of estimates employed for path-plotting
nu	optional weighting parameter
tuning.criterion	loss criterion for cross-validation; one out of "GCV" (generalized cross validation criterion), "deviance" (K-fold cross-validation with the predictive deviance as criterion)
K	integer; number of folds for cross-validation
cv.refit	logical; if TRUE, cross-validation is based on a refit of the selected coefficients
lambda.upper	integer; upper bound for cross-validation of lambda
lambda.lower	integer; lower bound for cross-validation of lambda
lambda.accuracy	numeric; how accurate shall lambda be cross-validated?; minimal absolute difference between two candidates for lambda
scaled.lik	if TRUE, the likelihood in the objective function is scaled by 1/n
adapted.weights	logical; if TRUE, penalty terms are weighted adaptively, that is by inverse ML-estimates; set to FALSE, if ML-estimates do not exist/are to close to zero; only for specials v, p, grouped, SCAD, elastic
adapted.weights.adj	logical; if TRUE, adapted weights of several categorical covariates are scaled such that they are comparable
adapted.weights.ridge	logical; if TRUE, adapted weights are based on aa estimate that is slightly penalized by a Ridge penalty
assured.intercept	logical; shall a constant intercept remain in the model in any case?
level.control	logical; if TRUE, the penalty terms are adjusted for different number of penalty terms per covariate
case.control	logical; if TRUE, the penalty terms are adjusted for the number of observations on each level of a categorical covariate
pairwise	experimental option; disabled if TRUE
grouped.cat.diffs	experimental option; disabled if FALSE
bootstrap	experimental option; disabled if 0
start.ml	logical; if TRUE, the initial value is the ML-estimate
L0.log	experimental option; disabled if TRUE
subjspec.gr	experimental option; disabled if FALSE
high	experimental option; disabled if NULL
...	further arguments passed to or from other methods

Value

Returns a list containing the (checked) input arguments.

See Also

Function gvcm.cat

gvcm.cat documentation built on May 1, 2019, 10:13 p.m.