cubinf.control: Control parameters for the function cubinf

Description

Allows the user to set parameters affecting the estimation of the discrete GLMs implemented in cubinf. Most control parameters are parameters of the ROBETH subroutine GYMAIN (Marazzi, 1993).

Usage

cubinf.control(tlo = 0.001, tua = 1e-06, mxx = 30, mxt = 10, mxf = 10, ntm = 0, gma = 1, 
               iug = 1, ipo = 1, ilg = 2, icn = 1, icv = 1, ufact = 0, cpar = 1.5,
               null.dev=TRUE, ...)

Arguments

tlo

Relative precision for the convergence criterion of the main algorithm (GYMAIN) called by cubinf. The relative precision for the convergence criterion in the lower level steps (theta-step, A-step and c-step) is '10*tlo'.

tua

Tolerance used for the determination of the pseudo-rank.

mxx

Maximum number of cycles for the main algorithm.

mxt

Maximum number of iterations for the theta-step.

mxf

Maximum number of iterations for the A-step.

ntm

Parameter to control iteration monitoring. When the number of iterations in the theta-step reaches a multiple of 'ntm', the current parameter values as well as the corresponding value of the objective function are printed.

gma

Relaxation factor for the theta-step.

iug

Parameter for the choice of the u-function in the A-step. See Marazzi, 1993, for details.

ipo

Parameter for the choice of the steplength algorithm in the theta-step. If 'ipo=1', a quadratic comparison function is minimized. If 'ipo=2', the Goldstein-Armijo step length algorithm is used.

ilg

Parameter for the choice of the algorithm in the c-step. If 'ilg=1', the H-algorithm is used. If 'ilg=2', the W-algorithm is used.

icn

Parameter for the choice of the convergence criterion for the theta-step and the main algorithm. If 'icn=1', convergence is assumed when the change in each coefficient is less than the tolerance ('10*tlo') times an estimate of the coefficient variance. See Marazzi (1993, p. 281), for the other options ('icn=2' and 'icn=3').

icv

Parameter for the choice of the convergence criterion for the A-step. If 'icv=1', convergence is assumed when the norm of the difference between two consecutive values of A is less than the tolerance (10*tol). See Marazzi (1993, p.288 and p. 301), for another option ('icv=2').

ufact

The tuning constant b is set equal to ufact*sqrt(p), where p is the dimension of the observation vectors. The default value of b is 1.1*sqrt(p); this value is used when 'ufact=0' on input.

cpar

Parameter used in determining an initial value of theta (standard Mallows estimate, see Marazzi, 1993, p281).

null.dev

If 'null.dev=TRUE', the null deviance is computed. The null deviance is the deviance of the model with no predictors.

…

Further named control arguments as singular.ok or qr.out used in the case where the x matrix is singular

Value

List of control parameters.

References

Marazzi, A. (1993). Algorithms, Routines, and S-functions for robust Statistics. Chapman and Hall, New York.

Examples

Run this code

# NOT RUN {
#To compute the classical estimates using cubinf, set:
control <- cubinf.control(ufact=300)
# }