The maximum number of step-halvings in one iteration. The increment of the
beta vector within one iteration is divided by 2 if the new beta leads to a decrease
in log likelihood.
maxstep
Specifies the maximum step size in the beta vector within one iteration.
lconv
Specifies the convergence criterion for the log likelihood.
gconv
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector).
xconv
Specifies the convergence criterion for the parameter estimates.
collapse
If TRUE, evaluates all unique combinations of x and y and collapses data set.
Value
maxit
The maximum number of iterations
maxhs
The maximum number of step-halvings in one iteration. The increment of the
beta vector within one iteration is divided by 2 if the new beta leads to a decrease
in log likelihood.
maxstep
Specifies the maximum step size in the beta vector within one iteration.
lconv
Specifies the convergence criterion for the log likelihood.
gconv
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector).
xconv
Specifies the convergence criterion for the parameter estimates.
collapse
If TRUE, evaluates all unique combinations of x and y and collapses data set.
Details
logistf.control() is used by logistf and logistftest to set control parameters to default values.
Different values can be specified, e. g., by logistf(..., control= logistf.control(maxstep=1)).