similar to: within-subject factors in lme

En innebygd og tegnsett-uspesifisert tekst ble skilt ut... Navn: ikke tilgjengelig Nettadresse: https://stat.ethz.ch/pipermail/r-help/attachments/20080129/5f8a6ac4/attachment.pl

I am trying to perform a model 3 ANOVA for a 2 factor (say factor A and factor B) anova in which factor A is fixed and factor B is random. Therefore, the error term for the test of factor A should be the A:B interaction term and the error terms for B and A:B should be the model residual (within) term. I have tried to work out how to specify such error strata using aov, however, I have had

Does someone out there have an example of R-code for a split-split plot ANOVA using aov or another function? The design is not balanced. I never set up one in R before and it would be nice to see an example before I tackle a very complex design I have to model. Thanks, Mike Mike Saunders Research Assistant Forest Ecosystem Research Program Department of Forest Ecosystem Sciences University of

Dear All, I wonder if anyone can please offer any advice on a model including 2 fixed effects and 1 random effect, as well as a covariate? The experimental design is as follows: I have a two by two factor design, where the two factors, Age (A) and Group size (G), both have 2 levels (old or young, and 1 or 3 respectively), and I am interested in the effect of these factors upon a continuous

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

list, i am using lmer to fit multilevel models and trying to use anova to compare the models. however, whenever i run the anova, the AIC, BIC and loglik are different from the original model output- as below. can someone help me out with why this is happening? (i'm hoping the output assocaited with the anova is right!). thank you, darren > unconditional<-lmer(log50 ~ 1 + (1 |

Dear R-Helpers, I would like to know what syntax I need to use to do a nested anova for 1. a continuous variable and 2. count data (x out of y) 1. The first I used to do in SPSS and I would like to be able to do it in R as well. This is the hierarchical model I would like to use: a continuous variable explained by factor A(fixed) + factor B(random) nested in A + factor C (random) nested in

Hi I try to implement a simple 2-factorial repeated-measure anova in the lme framework and would be grateful for a short feedback -my dependent var is a reaction-time (rt), -as dependent var I have -the age-group (0/1) the subject belongs to (so this is a between-subject factor), and -two WITHIN experimental conditions, one (angle) having 5, the other 3 (hands) factor-levels;

Hi there Please excuse this elementary question, but I have been fumbling with this for hours and can't seem to get it right. I have a nested anova, with random factor "lakefac" nested within factor "fishfac" (fixed), with an additional fixed factor "Habfac". If I consider everything as fixed effects, it's addmittedly not the correct model, but I can at

Hello. I am fitting a two-level mixed model which assumes equality of variance in the lowest-level residuals across groups. The call is: fit3<-lme(CLnNAR~CLnRGR,data=meta.analysis, + na.action="na.omit",random=~1+CLnRGR|study.code) I want to test the assumption of equality of variances across groups at the lowest level. Can someone tell me how to do this? I know that one

Dear all, I do not seem to grasp how contrasts are set for ordered factors. Perhaps someone can elighten me? When I work with ordered factors, I would often like to be able to reduce the used polynomial to a simpler one (where possible). Thus, I would like to explicetly code the polynomial but ideally, the intial model (thus, the full polynomial) would be identical to one with an ordered factor.

Dear R users, I have not found anything on this in the archives. Does anyone know whehther the parameter use= is not functioning in cor or enlighten me what it is supposed to do? My R version is "R version 1.8.1, 2003-11-21" on Windows 2000. I am hoping to be able to update to 1.9.1 as soon as it has appeared (we are not allowed here to install software on our own and thus I am trying

I do not think anyone has answered this. > I'm trying to run a simple one-way ANCOVA with the lmer > function in R package lme4, but have encountered some > conceptual problem. The data file MyData.txt is like this: > > Group Subj Cov Resp > A 1 3.90 4.05 > A 2 4.05 4.25 > A 3 4.25 3.60 > A 4 3.60 4.20 > A 5 4.20 4.05 > A 6 4.05 3.85

Hello all! Consider a dataset with a grouping structure, Group (factor) Several treatments, Treat (factor) Some sort of yield, Yield (numeric) Something, possibly important, measured for each group; GroupCov (numeric) To look for fixed effects from Treat on Yield, a first attempt could be: m1 <- lm(Yield ~ Treat) which gives, in a symmetric situation, the same estimated fixed effects as:

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

Hi, I am a first user of R. I was hoping I could get some help on some data I need to analyze. The experimental design is a complete randomized design with 2 factors (Source material and Depth). The experimental design was suppose to consist of 4 treatments replicated 3 time, Source 1 and applied at 10 cm and source 2 applied at 20 cm. During the construction of the treatmetns the depths vary

This graph -> http://www.math.hope.edu/~tanis/dallas/images/disth36.gif is an example I found at http://www.math.hope.edu/~tanis/dallas/disth1.html created by Maple. Does anybody know how to create something similar in R? I have a feeling it could be possible using scatterplot3d (perhaps with type=h, the fourth example in help('scatterplot3d')?), but I cannot figure it out. Thanks in

Hi, All; When I generate a "special" paper postscript image larger than "a4" or "letter" using R, I can only see one-page portion of all image, of course. What will be the simple solution for this? Is there any way I can set the bounding box information on the image? Or any other suggestions? Thanks in advance; Tae-Hoon Chung

Dear all, Suppose I have a linear mixed-effects model (from the package nlme) with nested random effects (see below); how would I present the results from the random effects part in a publication? Specifically, I?d like to know: (1) What is the total variance of the random effects at each level? (2) How can I test the significance of the variance components? (3) Is there something like an

Hi again. Perhaps a simple question this time.... I am analysing data with a dependent variable of insect counts, a fixed effect of site and two random effects, day, which is the same set of 10 days for each site, and then transect, which is nested within site (5 each). I am trying to fit the cross classified model using GLMM in lme4. I have, for potential use, created a second coding