I would like to impute missing data in a set of correlated variables (columns of a matrix). It looks like transcan() from Hmisc is roughly what I want. It says, "transcan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables." And, "By default, transcan imputes NAs with "best guess" expected values of transformed variables, back transformed to the original scale."
But I can't get it to work. I say
m1 <- matrix(1:20+rnorm(20),5,) # four correlated variables colnames(m1) <- paste("R",1:4,sep="") m1[c(2,19)] <- NA # simulate some missing data library(Hmisc) transcan(m1,data=m1)
and I get
Error in rcspline.eval(y, nk = nk, inclx = TRUE) : fewer than 6 non-missing observations with knots omitted
Jonathan - you would need many more observations to be able to fit flexible additive models as transcan does. Also note that single imputation has problems and you may want to consider multiple imputation as done by the Hmisc aregImpute function, if you had more data.
Frank
I've tried a few other things, but I think it is time to ask for help.
The specific problem is a real one. Our graduate admissions committee (4 members) rates applications, and we average the ratings to get an overall rating for each applicant. Sometimes one of the committee members is absent, or late; hence the missing data. The members differ in the way they use the rating scale, in both slope and intercept (if you regress each on the mean). Many decisions end up depending on the second decimal place of the averages, so we want to do better than just averging the non-missing ratings.
Maybe I'm just not seeing something really simple. In fact, the problem is simpler than transcan assumes, since we are willing to assume linearity of the regression of each variable on the other variables. Other members proposed solutions that assumed this, but they did not take into account the fact that missing data at the high or low end of each variable (each member's ratings) would change its mean.
Jon
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Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
