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).