similar to: nested random effects with lmer

Dear all, I'm quite new in R and especially with linear mixed effects models and I'm not completely sure to read the lmer table in the right way. for example: head(march.f) fam subjID Cond Code reg total first second log.total log.second cat 3 f 30 an fDan1 3 1.2304688 0.6679688 0.56250000 0.20739519 0.44628710 f

Relatively new to R, I'm trying to do a relatively simple task. I have data set that has several variables arranged by SubjID and visit, with multiple observations for that combination. I do linear regression on those multiple observations, then generated a set of interpolated values from the regression at fixed intervals along "x". I now want to average each of those across all the

Dear all, I'm quite new in R and I have a problem with the function which. When I use it to select a subset of a dataframe it works well but somewhere R takes trace of the past dataframe and this creates problems with following operations. For example: sentences <- read.xls("frasi.tot.march.3.xls", header=TRUE) head(sentences) fam subjID Cond Code reg total first

This is very possibly not a question on R. I was under the impression that the argument that gives rise to Fisher's LSD method in ANOVA works in other situations with three-way comparisons too, given that formal logic works the same ("if the omnibus test rejects, only two of the three groups may be equal, and therefore only one hypothesis can be rejected falsely"). However, when I

Dear All, I met problems when doing contrast and now really need some help in the model below: Fit=gam(y~treat+SEQUENCE+PERIOD+SEX+s(x),data=dat, random=list(SUBJID=~1),correlation=corAR1(form=~1|SUBJID)) And error message keeps coming out when I want to compare the differences between treatments: Diff=contrast(Fit, list(treat=treatment[-placebo.pos]),list(treat="Placebo"),