similar to: modelling nested random effects with interactions in R

Hi, there I first sent this e-mail a couple months ago, to no avail.Since I am not a member on your mailing list, so could you please cc: a response to me? I'll be sure to check the list today for replies. I am currently attempting to perform an ANOVA with both nested and normal factors. My problem is that R is treating my nested factors the exact same way as it would interaction terms.

Hi, there I am not a member on your mailing list, so could you please cc: a response to me? I am currently attempting to perform an ANOVA with both nested and normal factors. My problem is that R is treating my nested factors the exact same way as it would interaction terms. My output for the nested model is as follows: > nested <- anova(lm(ltotinv ~ habitat + fish/lake +

Hi, I'm attempting to star Wine (as root). What does it mean when Wine-20011004 tells me that I need the "-desktop option to use the native USER" ? (start fails then) I don't understand because my install is Wine-only (no Windows so built-in should prevail over native), the .wine directory and the C: drive are all ./tools/wineinstall -generated. My config is Mandrake8.1 kernel

I am wondering how to customize a pairwise comparisons plot of a factorial ANOVA, without doing a lot of manual manipulation of a TukeyHSD object. The customizations I'd like are: 1. The aov used log-transformed response data, but I'd like to plot the intervals on their original, untransformed scales 2. Plot all the main and interaction effects together, rather than in a separate

I'm taking the residual-regression approach to semiparametric estimation (Robinson 1988, Econometrica), and basically using SemiPar simply as a convenient means of doing multivariate nonparamteric additive models. The final bit of code is here: finalfit <- spm(res~f(V3,basis="trunc.poly")+f(V5,basis="trunc.poly")+f(V6,basis="trunc.poly")) summary(finalfit)

In a rather simple regression, I?d like to ask the question, for high trees, whether it makes a difference (for volume) whether a three is thick. If my interpretation is correct, for low trees, i.e. for which trees$isHigh == FALSE, the answer is yes. The problem is how to "merge" the standard errors. Code follows. data(trees) trees$isHigh <- trees$Height > 76 trees$isThick

Hello, I was hoping that someone could answer a few questions for me (the background is given below): 1) Can the coxph accept an interaction between a covariate and a frailty term 2) If so, is it possible to a) test the model in which the covariate and the frailty appear as main terms using the penalized likelihood (for gaussian/t frailties) b)augment model 1) by stratifying on the variable that

Hi, I've been a bit confused by different wyas we specify random effects in lmer and MCMCglmm i just want to clear something. When I want to look for intersexual genetic correlations in the trait, is it equivalent to treat this trait for opposite sexes as separate traits and include the term idh(trait):animal - to treating this as a single trait and fitting idh(sex):animal? Do these two ways

Dear List, I'm interested in performing a permutation test on the interaction between a binary treatment indicator and a covariate (either continuous or categorical). I'm interested in the p-value of the interaction effect from a permutation test, and I'm using the coin package for that purpose. As I haven't seen any examples like this in the package documentation (or anywhere

Hi all, I'd like to compare all levels of my interaction with each other. I read the pdf 'Additional multcomp Examples' but even though there is an example with an interaction it doesn't work for me when I want to compare within and between groups. Here is an example: #### d.fr<-data.frame(id=rep(1:16,3),treat1=rep(as.factor(LETTERS[1:3]),each=

Dear All, I'm struggling a little with the behaviour of R with GLM interactions. In particular, I have a dataset with two factors - call them factor A and factor B, where I would like to fit a GLM that is factor A + (grouped factor A):factor B. To try to isolate this, I've ignored the original "factor A" part, as that I have this as a separate column in my data. So, it

I was trying to do a bunch of planned contrast but ran into a snag. fit.contrast won't work with an Error term in aov. Ok, then I use lme. Only, now it seems there is no way to enter the interaction terms (something like "x1:x2"). I couldn't find a way to use the interaction term in the help. Is there an undocumented method or some other command for planned contrasts?

Hi R community, I am trying to fit a PLS model with response Y and predictor X, where X consists of at least 30 columns (say x1, x2, ..., x30).  Aside from studying the relationship of Y and x1, ..., x30, I am also interested in studying the effect of quadratic terms (x1^2, ..., x30^2) and two-way interactions (x1*x2, x1*x3, ..., x29*x30) on Y. My R code is dat.pls<-plsr(Y ~ X^2 + I(X^2),

The proper use of SPRNG is a little unclear to me in each of these packages, even more so when I put them all together. My immediate question: in snow I did clusterSetupSRNG. If a later want to (re) set the system to a known seed, how do I do it? That is, I want each distributed process to recreate exactly the same random numbers that it did before; I assume SPRNG assures the streams are

On Feb 8, 2005, at 23:52, Chris Lattner wrote: > That would be great. As a first implementation, I would suggest > adding a lock to the ExecutionEngine class in > include/llvm/ExecutionEngine/ExecutionEngine.h, locking it when > particular state is accessed. Note that the JIT subclasses this in > lib/ExecutionEngine/JIT/JIT.h, so its accesses should be synchronized > as

Hi, > The problem that I've run into is that the LLVM codebase does > certain "idiomatic" things with STL containers that cause assertion > failures in the Microsoft runtime. Most notably: > - Taking the address of the "first element" of an emtpy vector > (either &v[0] or &*v.begin()) > - Using an iterator into a collection (even if just for

Hi, I'm using procmail to deliver to Maildir directories: DEFAULT=${HOME}/Maildir/ I notice dovecot keeps a 'dovecot-uidlist' plus cache and index files, in each maildir. How is it that dovecot keeps these up to date when the contents of cur/ keeps changing? Is dovecot scanning cur/ every so often checking for changes? Isn't this slow for large folders (100k+)? I note on the

I'm attempting to use stepAIC to select a model through a forward procedure. I want to consider up to all 3-way interactions. I've attempted to use the following code: m2.Fwd3way <- stepAIC(m1.Ionly, direction="forward", scope=list(upper=~(var1 + var2 + var3 + var4)^3, lower=~1)) When I submit this the trace indicates that only 2-way

In lme, what's the difference between "random = ~ 1 | x / y" and "random = ~ y | x" ? Thanks Julien [[alternative HTML version deleted]]

Dear list members, I am working with a Cox ph model for the duration of unemployment. The event of interest in my analysis is getting employed. I have various background variables explaining this event: age, sex, education etc. I have multiple unemployment spells per person. I use a model with person-specific frailty terms in order to take into account the correlation of spells by the same