similar to: problems with which

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 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"),

Hi, I have a multilevel situation where subjects are nested within clinics, and each subject has multiple measurements. For simplicity, suppose there 4 clinics, 3 subjects per clinic, and each subject has 3 repeated measures. Outcome is continuous. I am trying to implement this model with lmer function in lme4 library.

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

I have a file, each column of which is a separate year, and each row of each column is mean precipitation for that month. Looks like this (except it goes back to 1964). month X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007 X2008 X2009 1 1.600 1.010 4.320 2.110 0.925 3.275 3.460 0.675 1.315 2.920 2 2.960 3.905 3.230 2.380 2.720 1.880 2.430 1.380

Dear Colleagues, John Mandel ( Chain block designs with two-way elimination of heterogeneity. Biometrics 10, 251-272 ,1954). extended the class of chain block designs (Youden & Conner (1953) to elimination of both row and column (blocks) effects. These experimental designs can be useful in engineering and other fields. I am having difficulty obtaining his adjusted treatment means in his