Dear R-help,
I am trying to impute missing data for the first time using R. The norm
package seems to work for me, but the missing values that it returns seem
odd at times -- for example it returns negative values for a variable that
should only be positive. Does this matter in data analysis, and/or is
there a way to limit the imputed values to be within the minimum and
maximum of the actual data? Below is the code I am using.
Thanks,
Anders Corr
Ph.D. Candidate
#DOWNLOAD DATA (61Kb)
download.file("http://www.people.fas.harvard.edu/~corr/tc.csv","C:/R";)
#RUN NORM
tc <- read.csv("tc.csv", header = TRUE)
rngseed(1234567) #set random number generator seed
s <- prelim.norm(tc)
thetahat <- em.norm(s) #find the MLE for a starting value
theta <- da.norm(s,thetahat,steps=20,showits=TRUE,return.ymis=TRUE) #take 20
steps
ximp <- imp.norm(s,thetahat,tc) #impute missing data under the MLE
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