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Dear All, I am doing a growth modeling using NLME. I have three levels in my data: observation, individual, household. About half of my total sample have missing values in my household-level covariates. Under this situation, the best way to go is probably to multiply impute the data (for, say, 5 times), estimate the same model separately on each model using LME function, and merge the results. My

Hi R community, I have a question I'm sure is very simple for most of you. I have a list, with each element being a matrix and the names of the elements are numbers (like 1,3,...). I can extract the matrices and the names individually. Now, I want to multiply each of the names to the individual list matrices (after converting to numbers of course). I could use a for loop, but the very

...worked. tradevectors<-mapply(seq,from=tempa,to=tempb) or tradevectors<-sapply(1:length(tempa),function(x) seq(tempa[x],tempb[x]) >both return a list with 3 components. the problem is that i want to take these 3 sequences and use them as the indices of two other vectors, X and Y and mutiply them ( element by element ) >so, i tried > >temp<-sapply(1:length(tradevectors),function(i)X[tradevectors[[i]]]*Y[tradevectors[[i]]] basically, the sequence output from the previous command are indices to two vectors that i want to multiply ( element by element ). but, the messag...

Hi R community, I posted a question on using the R maximum likelihood functions a short while ago and got an email saying that some of the content was "unprocessed". Hence, I am reposting the question just to be sure (sorry for the multiple emails if both reached you). My question is reagarding the way we use constraints in programs like maxBFGS, etc. We need to put two matrices into