Dear, I have the data in the following form:
>head(matrices_m) Location Variable Value Week 1 Africa Africa 21 4 weeks 2 Asia Africa 0 4 weeks 3 Canada Africa 17 4 weeks 4 China Africa 29 4 weeks 5 Europe Africa NA 4 weeks 6 Japan Africa 68 4 weeks It is a (melted) three-way count (Value is counts) table where for example first row has the following meaning - when the Variable had its maximal count, the Value for Location was 21, 4 weeks prior (covariate Week). The data has some missing Values, which I would like to impute. What I have so far is a logit model predicting NA's in the Value, to try to spot good predictors for missing entries. With those I hope to come up with a loglinear (poisson) GLM and try to impute the NA's. However coming up with a decent non-saturated model is difficult given the data. Could You point me towards sth that could capture the nature of the problem? Are lag models a good lead here? -- while(!succeed) { try(); } ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.