profile.logistf: Compute Profile Penalized Likelihood

In logistf: Firth's Bias-Reduced Logistic Regression

Compute Profile Penalized Likelihood

Description

Evaluates the profile penalized likelihood of a variable based on a logistf model fit

Usage

## S3 method for class 'logistf'
profile(
  fitted,
  which,
  variable,
  steps = 100,
  pitch = 0.05,
  limits,
  alpha = 0.05,
  firth = TRUE,
  legends = TRUE,
  control,
  plcontrol,
  ...
)

Arguments

fitted	An object fitted by logistf
which	A righthand formula to specify the variable for which the profile should be evaluated, e.g., which=~X).
variable	Alternatively to which, a variable name can be given, e.g., variable="X"
steps	Number of steps in evaluating the profile likelihood
pitch	Alternatively to steps, one may specify the step width in multiples of standard errors
limits	Lower and upper limits of parameter values at which profile likelihood is to be evaluated
alpha	The significance level (1-\alpha the confidence level, 0.05 as default).
firth	Use of Firth's penalized maximum likelihood (firth=TRUE, default) or the standard maximum likelihood method (firth=FALSE) for the logistic regression.
legends	legends to be included in the optional plot
control	Controls Newton-Raphson iteration. Default is control= logistf.control(maxstep, maxit, maxhs, lconv, gconv, xconv)
plcontrol	Controls Newton-Raphson iteration for the estimation of the profile likelihood confidence intervals. Default is plcontrol= logistpl.control(maxstep, maxit, maxhs, lconv, xconv, ortho, pr)
...	Further arguments to be passed.

Value

An object of class logistf.profile with the following items:

beta	Parameter values at which likelihood was evaluated
stdbeta	Parameter values divided by standard error
profile	profile likelihood, standardized to 0 at maximum of likelihood. The values in profile are given as minus \chi^2
loglik	Unstandardized profile likelihood
signed.root	signed root (z) of \chi^2 values (negative for values below the maximum likelihood estimate, positive for values above the maximum likelihood estimate)
cdf	profile likelihood expressed as cumulative distribution function, obtained as \Phi(z), where \Phi denotes the standard normal distribution function.

References

Heinze G, Ploner M, Beyea J (2013). Confidence intervals after multiple imputation: combining profile likelihood information from logistic regressions. Statistics in Medicine, to appear.

Examples

data(sex2)
fit<-logistf(case ~ age+oc+vic+vicl+vis+dia, data=sex2)
plot(profile(fit,variable="dia"))
plot(profile(fit,variable="dia"), "cdf")
plot(profile(fit,variable="dia"), "density")

logistf documentation built on Aug. 18, 2023, 5:06 p.m.