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Dear R Users, A would like to fit a loglinear analysis to a three dimensional contingency table. But I Don't want to run a full saturated modell. Is there any package in R that could handle somekind of stepwise search to choose out the best soultion? And how can I fit a non fully saturated modell, which only use the important interactions? Best Regards Zoltan Kmetty [[alternative HTML

Hi, is it possible to do this operation faster? I am going over 35k data entries and this takes quite some time. for(cnt in 2:length(sdata$date)) { if(sdata$value[cnt] < sdata$value[cnt - 1]) { sdata$ddtd[cnt] <- sdata$ddtd[cnt - 1] + sdata$value[cnt - 1] - sdata$value[cnt] } else sdata$ddtd[cnt] <- 0 } return(sdata) Thank you, Benjamin

Hi! I had some memory problem with R - hope somebody could tell me a solution. I work with very large datasets, but R cannot allocate enough memoty to handle these datasets. I want work a matrix with row= 100 000 000 and column=10 A know this is 1 milliard cases, but i thought R could handle it (other commercial software like spss could do), but R wrote out everytime: not enough memory.. any