prior.control {mi}R Documentation

Auxiliary for Adding Priors to Missing Data Imputation

Description

Auxiliary function as user interface for adding priors for mi procedure. Typically only used when calling mi.

Usage

prior.control(augment.data=FALSE, pct.aug=10, K=1)

Arguments

augment.data add the extra rows of data with pct.aug of the random draws from the observed data, default= FALSE
pct.aug percent of N being add into the exisitng data, where N is the number of observation of the completed data set, default is 10.
K the cooling parameter, default is 1. If K > 0, the imputation will randomly switch between randomly imputing data from marginal (imputing data from the observed values) and drawing from the modeled based values. And with each iteration (s), the probability of cooling (q) decreases by number of iterations, such that q = K/s.

Details

If both augment.data=TRUE and K>0 are specified, it will switch augment.data=FALSE and use only K>0.

The default mi process uses K=1. In genereal, add priors to the mi process will speed the convergence of the imputation.

Author(s)

Yu-Sung Su yajima@stat.columbia.edu

See Also

mi

Examples

### NOT RUN
#=================================================
# add priors by augmenting 10
#=================================================
if(0){
 data(CHAIN)
 IMP <- mi(CHAIN, add.priors=prior.control(augment.data=TRUE, pct.aug=10))
#=================================================
# add priors by randomly drawing from the marginal
#=================================================
## This is the default setting
 IMP <- mi(CHAIN, add.priors=prior.control(K=1))
#=================================================
# use no prior
#=================================================
 IMP <- mi(CHAIN, add.priors=prior.control(K=0))
}

[Package mi version 0.04-6 Index]