R: 'plot' Method for 'logistf' Likelihood Profiles

plot.logistf.profile {logistf} | R Documentation

## `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 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

    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")

[Package logistf version 1.23 Index]