Usage
cat_control(standardize = FALSE, accuracy = 2, digits = 4, initials = NULL,
g = 0.5, epsilon = 10^(-5), maxi = 250, steps = 25, c = 10^(-5),
tuning.criterion = "deviance", K = 5, cv.refit = FALSE, lambda.upper = 10000,
lambda.accuracy = 0.01, adapted.weights = TRUE, assured.intercept = TRUE,
...)
Arguments
standardize
logical; if TRUE all metric covariates are standardized by their empirical variances
accuracy
integer; number of digits being compared when setting coefficents equal/to zero; must be 4 or less
digits
integer; number of digits for estimates
initials
numeric vector; starting parameter values for penalized estimation
g
step length parameter for the PIRLS-algorithm, see Ulbricht (2010); out of )0,1( epsilon
small, positive, real constant; termination criterion for the PIRLS-algorithm, see Ulbricht (2010) maxi
integer; maximal number of iterations in the fitting algorithm
steps
integer; tuning parameter for path-plotting; minimal number of estimates employed for path-plotting
c
small, positive, real constant; needed for a approximation of the absolute value function in the PIRLS-algorithm, see Ulbricht (2010) tuning.criterion
loss criterion for cross-validation; one out of "deviance", "SSE" (for sum of squared errors)
K
integer; folds for cross-validation
cv.refit
logical; if TRUE cross-validation is based on a refit of selected coefficients
lambda.upper
integer; upper bound for cross-validation of lambda
lambda.accuracy
numeric; how accurate shall lambda be cross-validated?; minimal absolute difference between two candidates for lambda
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
assured.intercept
logical; shall a constant intercept remain in the model in any case?
...
further arguments passed to or from other methods