Description Usage Arguments Details Value Author(s) References Examples

This function uses CLIP (combination of likelihood profiles) to compute the pooled profile of the posterior after multiple imputation.

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`obj` |
Either a list of logistf fits (on multiple imputed data sets), or the result
of analysis of a |

`variable` |
The variable of interest, for which confidence intervals should be computed. If missing, confidence intervals for all variables will be computed. |

`data` |
A list of data set corresponding to the model fits. Can be left blank if obj was obtained with the dataout=TRUE option or if obj was obtained by mice. |

`which` |
Alternatively to variable, the argument which allows to specify the variable to compute the profile for as righthand formula, e.g. which=~X. |

`firth` |
If |

`weightvar` |
An optional weighting variable for each observation |

`control` |
control parameters for |

`offset` |
An optional offset variable |

`from` |
Lowest value for the sequence of values for the regression coefficients for which the profile will be computed. Can be left blank. |

`to` |
Highest value for the sequence of values for the regression coefficients for which the profile will be computed. Can be left blank |

`steps` |
Number of steps for the sequence of values for the regression coefficients for which the profile will be computed |

`legacy` |
If |

`keep` |
If |

While CLIP.confint iterates to find those values at which the CDF of the pooled posterior equals the confidence levels, CLIP.profile will evaluate the whole profile, which enables plotting and evaluating the skewness of the combined and the completed-data profiles. The combined and completeddata profiles are available as cumulative distribution function (CDF) or in the scaling of relative profile likelihood (minus twice the likelihood ratio statistic compared to the maximum). Using a plot method, the pooled posterior can also be displayed as a density.

An object of class `CLIP.profile`

with items:

`beta` |
The values of the regression coefficient |

`cdf` |
The cumulative distribution function of the posterior |

`profile` |
The profile of the posterior |

`cdf.matrix` |
An imputations x steps matrix with the values of the completed-data CDFs for each beta |

`profile.matrix` |
An imputations x steps matrix with the values of the completed-data profiles for each beta |

`call` |
The function call |

Georg Heinze und Meinhard Plonar

Heinze G, Ploner M, Beyea J (2013). Confidence intervals after multiple imputation: combining profile likelihood information from logistic regressions. Statistics in Medicine, to appear.

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#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",data =toymi, keep=TRUE)
plot(xprof)
#plot as CDF
plot(xprof, "cdf")
#plot as density
plot(xprof, "density")
``` |

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