Compute Profile Penalized Likelihood
Evaluates the profile penalized likelihood of a variable based on a logistf model fit
## S3 method for class 'logistf' profile( fitted, which, variable, steps = 100, pitch = 0.05, limits, alpha = 0.05, firth = TRUE, legends = TRUE, control, plcontrol, plot = FALSE, ... )
fitted |
An object fitted by |
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 ( |
legends |
legends to be included in the optional plot |
control |
Controls Newton-Raphson iteration. Default is |
plcontrol |
Controls Newton-Raphson iteration for the estimation of the profile likelihood
confidence intervals. Default is |
plot |
If |
... |
Further arguments to be passed. |
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. |
Georg Heinze and Meinhard Ploner
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")
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