Missing data in R

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Missing data in R

CRAN Task View: Statistics for the Social Sciences

For the cat option for categorical data by Joe Schafer, see The multiple imputation FAQ page, which follows a probabilistic model with error terms. Here, from the manual, are some examples
Example 
data(HairEyeColor) # load data
m=c(1,2,0,1,3,0,2,3) # no three-way interaction
thetahat <- ipf(HairEyeColor,margins=m,
showits=TRUE) # fit model
thetahat <- ipf(HairEyeColor+.5,m) # find an interior starting value
rngseed(1234567) # set random generator seed
theta <- bipf(HairEyeColor,m,
start=thetahat,prior=0.5,
steps=50) # take 50 steps

Missing Values in S+ and R

ATS Stat Consulting Group to DRW

show details 4:39 PM (13 hours ago)


Greetings,


Thank you for the link. It looks interesting.

I am not sure it is a good idea doing factor analysis on pairwise correlation, since there is no guarantee that a pairwise correlation is actually a correlation matrix, i.e., being positively definite. I believe that is why people usually don't use pairwise correlation.

I hope this helps a little.

Best regards,

_______________________________ Xiao Chen

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