# plot method for logistf likelihood profiles

### Description

provides the plot method for objects created by `profile.logistf`

or `CLIP.profile`

### Usage

1 2 3 |

### Arguments

`x` |
A |

`type` |
Type of plot: one of |

`max1` |
if |

`colmain` |
color for main profile line |

`colimp` |
color for completed-data profile lines (for |

`plotmain` |
if FALSE, suppresses the main profile line (for |

`ylim` |
limits for the y-axis |

`...` |
further arguments to be passed to |

### 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 and 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.

### See Also

`profile.logistf`

, `CLIP.profile`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ```
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, dataout=TRUE))
# CLIP profile
xprof<-CLIP.profile(obj=fit.list, variable="x", keep=TRUE)
plot(xprof)
#plot as CDF
plot(xprof, "cdf")
#plot as density
plot(xprof, "density")
``` |