plot.logistf.profile: 'plot' Method for 'logistf' Likelihood Profiles

In logistf: Firth's Bias-Reduced Logistic Regression

plot Method for logistf Likelihood Profiles

Description

Provides the plot method for objects created by profile.logistf or CLIP.profile

Usage

## S3 method for class 'logistf.profile'
plot(
  x,
  type = "profile",
  max1 = TRUE,
  colmain = "black",
  colimp = "gray",
  plotmain = T,
  ylim = NULL,
  ...
)

Arguments

x	A profile.logistf object
type	Type of plot: one of c("profile", "cdf", "density")
max1	If type="density", normalizes density to maximum 1
colmain	Color for main profile line
colimp	color for completed-data profile lines (for logistf.profile objects that also carry the CLIP.profile class attribute)
plotmain	if FALSE, suppresses the main profile line (for logistf.profile objects that also carry the CLIP.profile class attribute)
ylim	Limits for the y-axis
...	Further arguments to be passed to plot().

Details

The plot method provides three types of plots (profile, CDF, and density representation of a profile likelihood). For objects generated by CLIP.profile, it also allows to show the completed-data profiles along with the pooled profile.

Value

The function is called for its side effects

Author(s)

Georg Heinze und Meinhard Ploner

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")

#generate data set with NAs
freq=c(5,2,2,7,5,4)
y<-c(rep(1,freq[1]+freq[2]), rep(0,freq[3]+freq[4]), rep(1,freq[5]), rep(0,freq[6]))
x<-c(rep(1,freq[1]), rep(0,freq[2]), rep(1,freq[3]), rep(0,freq[4]), rep(NA,freq[5]),
   rep(NA,freq[6]))
toy<-data.frame(x=x,y=y)

# impute data set 5 times
set.seed(169)
toymi<-list(0)
for(i in 1:5){
   toymi[[i]]<-toy
   y1<-toymi[[i]]$y==1 & is.na(toymi[[i]]$x)
   y0<-toymi[[i]]$y==0 & is.na(toymi[[i]]$x)
   xnew1<-rbinom(sum(y1),1,freq[1]/(freq[1]+freq[2]))
   xnew0<-rbinom(sum(y0),1,freq[3]/(freq[3]+freq[4]))
   toymi[[i]]$x[y1==TRUE]<-xnew1
   toymi[[i]]$x[y0==TRUE]<-xnew0
 }
 
# logistf analyses of each imputed data set
fit.list<-lapply(1:5, function(X) logistf(data=toymi[[X]], y~x, pl=TRUE))

# CLIP profile 
xprof<-CLIP.profile(obj=fit.list, variable="x", data=toymi, keep=TRUE)
plot(xprof)

#plot as CDF
plot(xprof, "cdf")

#plot as density
plot(xprof, "density")

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