# profile.logistf: Compute Profile Penalized Likelihood In logistf: Firth's Bias-Reduced Logistic Regression

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```## 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-α 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 χ^2 `loglik` Unstandardized profile likelihood `signed.root` signed root (z) of χ^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 Φ(z), where Φ 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

 ```1 2 3 4 5``` ```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 Jan. 18, 2022, 5:07 p.m.