Sets parameters for iterations in Firth's penalized-likelihood logistic regression.
logistf.control(
maxit = 25,
maxhs = 0,
maxstep = 5,
lconv = 1e-05,
gconv = 1e-05,
xconv = 1e-05,
collapse = TRUE,
fit = "NR"
)The maximum number of iterations
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.
Specifies the maximum step size in the beta vector within one iteration. Set to -1 for infinite stepsize.
Specifies the convergence criterion for the log likelihood.
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector).
Specifies the convergence criterion for the parameter estimates.
If TRUE, evaluates all unique combinations of x and y and collapses data set.
Fitting method used. One of Newton-Raphson: "NR" or Iteratively reweighted least squares: "IRLS"
The maximum number of iterations
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.
Specifies the maximum step size in the beta vector within one iteration.
Specifies the convergence criterion for the log likelihood.
Specifies the convergence criterion for the first derivative of the log likelihood (the score vector).
Specifies the convergence criterion for the parameter estimates.
If TRUE, evaluates all unique combinations of x and y and collapses data set.
Fitting method used. One of Newton-Raphson: "NR" or Iteratively reweighted least squares: "IRLS"
The function call.
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)).
data(sexagg)
fit2<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sexagg, weights=COUNT,
control=logistf.control(maxstep=1))
summary(fit2)
#> logistf(formula = case ~ age + oc + vic + vicl + vis + dia, data = sexagg,
#> control = logistf.control(maxstep = 1), weights = COUNT)
#>
#> Model fitted by Penalized ML
#> Coefficients:
#> coef se(coef) lower 0.95 upper 0.95 Chisq
#> (Intercept) 0.12025404 0.4763429 -0.8185591 1.07315122 0.06286298
#> age -1.10598130 0.4149021 -1.9737884 -0.30742514 7.50773092
#> oc -0.06881673 0.4344026 -0.9414363 0.78920202 0.02467044
#> vic 2.26887464 0.5384872 1.2730216 3.43543273 22.93139022
#> vicl -2.11140817 0.5320395 -3.2608611 -1.11773495 19.10407252
#> vis -0.78831694 0.4089620 -1.6080879 0.01518468 3.69740975
#> dia 3.09601158 1.5052189 0.7745682 8.03029352 7.89693139
#> p method
#> (Intercept) 8.020268e-01 2
#> age 6.143472e-03 2
#> oc 8.751911e-01 2
#> vic 1.678877e-06 2
#> vicl 1.237805e-05 2
#> vis 5.449701e-02 2
#> dia 4.951873e-03 2
#>
#> Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
#>
#> Likelihood ratio test=49.09064 on 6 df, p=7.15089e-09, n=239
#> Wald test = 31.96836 on 6 df, p = 1.65471e-05