modeling effects in multiple data frames
Emmanuel Curis <emmanuel.curis at ...> writes:
Assuming your hundred data.frames are in a list called data, why not simply join them in a single data.frame: something like d <- do.call( rbind, data ) This should work if all your data.frames (text files) have the same number of variables and the same names for them.
More specifically, datList <- lapply(list.files(...),read.table) nvec <- sapply(datList,nrow) d <- cbind(do.call(rbind,datList),id=rep(1:length(datList),nvec)) lme4(out~poly(time,2,raw=TRUE)+(poly(time,2,raw=TRUE)|id),data=d) Note that time^2 won't work as expected in a formula context; you either need 1 + time + I(time^2) or poly(time,2) ## orthogonal polynomial or poly(time,2,raw=TRUE) ## regular polynomial orthogonal polynomials are probably better unless you need to be able to interpret the parameters in a specific way [snip]
? out: outcome variable (300 per participant) ? t: time variable (300 per participant) ? id: individual (100 for now) ? ? I wood like to model something like: ? ? lme4(out~1+time+time^2+(1+tim3+time^2|id, data=?????) ? ? So 100 data-frames (not exactly, txt files) with 300 data points per ? data-frame. id variable defined by data-frame (txt file used).