Message-ID: <loom.20130716T161417-54@post.gmane.org>
Date: 2013-07-16T14:18:44Z
From: Ben Bolker
Subject: 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).