similar to: Another wishlist for R

First, a disclaimer. I am not affiliatied with the SAMM package. I am only a user of the package, but I have been contacted (off list) by people requesting information about SAMM and so I am posting this information here. SAMM is software for fitting mixed models. Versions are available for both S-Plus and R. More information and downloads of the software (and manual) are available here:

Dear all; This an FAQ. I tried to access lme source script so I can step into it to debug the problems resulting from a lme() call. I used getAnywhere("lme") or nlme:::lme, both produced only the function definition and "UseMethod("lme"). Any idea how to list the source code? TIA, Richard Yang [[alternative HTML version deleted]]

Dear R-users, I am a newbie to this site and a relative new-comer to S/R, so please tread lightly, for you tread... There have been several posting relating to problems with augPred() from the nlme library. Here is a "fix" for one of these problems which may lie at the root of others. In my case the problem with augPred() lay in gsummary(), which augPred() uses, causing it to fail.

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Dear List, This may be related to this email thread initiated by Ben Bolker last month: https://stat.ethz.ch/pipermail/r-devel/2011-July/061630.html In answering this Question on StackOverflow http://stackoverflow.com/q/7195628/429846 I noticed that `methods()` was not listing some S3 methods for `plot()` provided by the mgcv package. At the time I wanted to check the development version of R as

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively

On page 165 of Mixed-Effects Models in S and S-Plus by Pinheiro and Bates there is an example of using lme() in the nlme package to fit a model with crossed random factors. The example assumes though that the data is grouped. Is it possible to use lme() to fit crossed random factors when the data is not grouped? E.g., y <- rnorm(12); a=gl(4,1,12); b=gl(3,4,12). Can I fit an additive model

Hi, I'm trying to work out how the nlme function estimates the variances of the fixed effects parameters, so I tried to look at the code for these functions: summary.nlme, summary.lme, MEestimate. > MEestimate Error: Object "MEestimate" not found > summary.nlme Error: Object "summary.nlme" not found > summary.lme Error: Object "summary.lme" not found

dear list: I used the nlme library according to the great Pinheiro/Bates book, on R2.3, WinXp Lac.lme is an lme object with unbalanced data, group is a factor variable with three levels, when I tried to plot the residuals by group I got this error msg: >plot(Lac.lme,resid(.,type='p')~fitted(.)|group) Error in limits.and.aspect(prepanel.default.xyplot, prepanel = prepanel, :

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

I am analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am

Dear list, I am trying to fit some mixed models using packages lme4 and nlme. I did the model selection using lmer but I suspect that I may have some autocorrelation going on in my data so I would like to have a look using the handy correlation structures available in nlme. The problem is that I cannot translate my lmer model to lme: mod1<- lmer(y~x + (1|a:b) + (1|b:c), data=mydata)

Hello all! R2.0.1, W2k. All packages updated. I?m heavily dependant on using mixed models. Up til?now I have used lme() from nlme as I have been told to. Together with estimable() from gmodels it works smooth. I also often run Rcmdr, mostly for quick graphics. After using Rcmdr, on reopening the R workspace all help libraries for Rcmdr (22 !) loads, among them nlme, but not Rcmdr itself. Why?

I have some questions about the use of lme(). Below, I constructed a minimal dataset to explain what difficulties I experience: # two participants subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2)) # within-subjects factor Word Type wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w")) # within-subjects factor

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.

I am newbie in R, trying to understand and compare syntax in nlme and lme4. lme() model from the nlme package I am interested in is: lme.m1.1 = lme(Y~A+B+C,random=~1|D/E,data=data,method="ML") (for simplicity reason, I am giving generic names of factors) If I understand well, there are three fixed factors: A, B and C, and two random factors: D and E. In addition to that, E is nested in

I need to specify a model similar to this lme.formula(fixed = sqrt(lbPerAc) ~ y + season + y:season, data = cy, random = ~y | observer/set, correlation = corARMA(q = 6)) except that observer and set are actually crossed instead of nested. observer and set are factors y and lbPerAc are numeric If you know how to do it or have suggestions for reading I will be grateful. eal ps I have

Hello all, I have problem with setting up random effects. I have a model: y=x1+x2+x1*x2+z1+z1*x2 where x1, x2, x1*x2 are fixed effects and z1, z1*x2 are random effects (crossed effects) I use library(nlme) 'lme' function. My question is: how I should set up random effects? I did lme(y~x1+x2+x1:x2, data=DATA, random=~z1+z1:x2, na.action='na.omit') but it did not work.

Hi, Can someone please offer a procedure for going from CIs produced by intervals.lme() to fixed-effects response CIs. Here's a simple example: library(mlmRev) library(nlme) hsb.lme <- lme(mAch ~ minrty * sector, random = ~ 1 | cses, Hsb82) (intervals(hsb.lme)) (hsb.new <- data.frame minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public',