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...am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p), integer(n), PACKAGE = "impute") 2: knnimp.internal(x, k, imiss, irmiss, p, n, maxp = maxp) 3: knnimp(x, k, maxmiss = rowmax, maxp = maxp) 4: impute.knn(dummy0, k) Possible actions: 1: abort (wi...

...(cbind(wmis,ymis,x1,x2,x3,x4)) s <- prelim.mix(dat,3) # do preliminary manipulations thetahat <- em.mix(s) # ML estimate for unrestricted model rngseed(1234567) # set random number generator seed newtheta <- da.mix(s,thetahat,steps=100) # data augmentation ximp <- imp.mix(s, newtheta, dat) # impute under newtheta ################################################################ Your mix package is important part of our ongoing research on the missing data project and we would like to have it working. If you could point out to me what I am doing wrong...

...quot;) #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