similar to: Question about Linear Mixed-Models (in R)

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Hi all, I figured out some things about an earlier question I posted today; this is a new question: I have a for-loop in which I have the following lines for (i in list.files(pattern = "*.dmp")) { currentfile <- read.table(i) vecrawrts <- as.vector(currentfile$V3) ... } Now, vecrawrts is numeric, but if I run the script on all the files with extension .dmp, I get the

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Dear R users, Many of you are aware of the statistical package lme4 used in R for carrying out mixed-effects modeling. The lme4 R-Forge website [ https://r-forge.r-project.org/projects/lme4/] is going to maintain a list of all dissertations and articles (accepted for publication, in press, or published) that use lme4 or its precursor, nlme. It would be helpful if members of this list could

Dear all, I'm trying to install lme4 (after having installed R 2.4.0 from source, and having installed the latest Matrix package). lme4 fails with the following message: pedigree.o definition of _lme4_xSym in section (__DATA,__common) pedigree.o definition of _lme4_ySym in section (__DATA,__common) make: *** [lme4.so] Error 1 ERROR: compilation failed for package 'lme4' ** Removing

Hi all, All the files related to the question below are in http://ling.ohio-state.edu/~vasishth/TempRdir/ I've been using a script (called newresiduals.R, the code is in the above directory; I wanted to avoid cluttering up this message) that is run in the following manner at the command prompt: R --vanilla "wordlen-2-01-g1" < newresiduals.R What it does is: it goes through a

Hi folks, I want to have a y-axis using matplot where the y axis is truncated as shown schematically below: (msecs) 3000 | x ~ ~ 800 | x | | x | ----|----|----|----------------- 1 2 3 Position The ~ is supposed to stand for a curved line showing a break. The issue is that I want to plot the points shown by the

Hi, I am trying to install the nlme package, but during installation I get the message <snip> pdSymm text html latex example plot.ACF text html latex example missing link(s): xyplot plot.Variogram text html latex example missing link(s): xyplot plot.augPred

Thanks for the advice, folks! Shravan Vasishth <vasishth at ling.ohio-state.edu> Try pico. There's no substitute for/comparison with emacs, though, IMHO. but just to be sure: you don't have to have ESS to use (x)emacs for editing R scripts. Sven Garbade <garbade at psy.uni-muenchen.de> ESS can be installed via rpm on Mandrake. baron at cattell.psych.upenn.edu (Jonathan

Hi all, I'm trying to figure out the example about mixed models in the Pinheiro and Bates book (Mixed-Effects Models in S and S-Plus, 2000, pp. 135-137). One thing I don't understand is: When I run the command fm1Orth.lm <- lm( distance ~ age, Orthodont ) followed by fm2Orth.lm <- update( fm1Orth.lm, formula = distance ~ Sex*age ) and then do summary(fm2Orth.lm)

Hello everybody, I'm trying to subset a data frame "foo", which looks like this: sent cond raw V1 10 2 A 614 7 11 2 A 724 6 12 2 A 641 5 13 2 A 665 9 33 5 B 510 7 34 5 B 659 7 35 5 B 607 6 37 5 B 467 8 38 5 B 586 9 39 5 B 523 10 57 8 C 858 8 58 8 C 513 9 59 8 C

Hi, My apologies for sending this message earlier to the wrong list (next time I'll wait till I'm fully awake). I'm a new R user. I want to do the following: I want to pass a filename to R repeatedly (about 400 times) in order to do a repetitive task (calculating regressions). The way I'm setting it up is to write a bash script that will create an R source file on the fly for

Hi all, I'm building a plot of the values in tmeant (below) against positions 1 to 5, using matplot. tmeant looks like this: case1 case2 pos1 861.8466 818.5909 pos2 961.2841 976.3466 pos3 878.6080 1262.8523 pos4 950.8011 1129.6080 pos5 968.1080 1063.3920 I also have lower (object tl) and upper (object tu) bounds on the confidence intervals as follows: tl: pos1

Hi all, I have the following dataset, call it test.data (30 columns, and one row named "0"): ADVP ADVP AP AP CONJ CONJ CP CP DU DU INF INF MWU MWU NP NP PP PP PPRT PPRT REL REL SMN SMN SSB SSB SV1 SV1 TI TI 0 96.85 2.05 89.07 2.54 70.91 2.37 94.92 3.46 82.31 11.33 40.96 2.25 98.06 3.43 90.77 17.63 86.60 10.78 60.27 1.32 93.27 0.97 77.60

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

Hi everyone, I'm using lmer and estimable (from packages lme4 and gmodels respectively) and have the disconcerting happening that when I run exactly the same code, I get different results! In checking this out by running the code 50x, it seems to be that answers may be randomly deviating around those which I get from another stats package (GenStat, using the linear mixed models functionality

Hi folks, I am having trouble to plot a mixed model analysis of covariance (ANCOVA). To do so I use the function predict in nlme but the line that is being drawn is totally out of control!!! here is my script (where MASS_S is dry mass and MASS_F is fresh mass): MEN<-read.table("Mentha_lme2.txt", h=T) attach(MEN) lme1<-lme(log(MASS_S)~log(MASS_F)*TREAT, random=~1|INDIV)