similar to: Some questions to GLMM

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Dear all,   I have data from the following experimental design and trying to fit a mixed model with lme function according to following steps but struggling. Any help is deeply appreciated.   1) Experimental design: I have 40 plants each of which has 4 clones. Each clone planted to one of 4 blocks. Phenotypes were collected from each clone for 3 consecutive years. I have genotypes of plants. I

Hi again, thank you for your previous answers. Just another question, though ... I get the following variance components after fitting a mixed model. Groups Name Variance Std.Dev. Corr PlantID TreatmCtrl 0.51784 0.71961 TreatmNoAccess 4.77469 2.18511 -0.063 TreatmNoKeel 4.22726 2.05603 0.513 0.751 TreatmNoSpur 0.45918

Hi all, I am using cross-validation to validate a generalized linear mixed effects model fitted using glmmPQL. i found that the predict function has a problem and i wonder if anyone has encountered the same problem? glmm1 = glmmPQL(y~aX+b,random=~1|sample,data=traindata) predict(glmm1,newdata=testdata,level=1,type="response") gives me all "NA"s. it works for level=0 (the

Hi, I am new to this list as a poster, but a reader for some time. I've using R for several weeks now, and I have a lot of questions about certain procedures. Here I go: I want to test if there are differences in the time spent by pollinators visiting flowers of a given plant species, according to a number of experimental manipulations made on those flowers. All experimental

Hi guys, I'm very new to R and have been teaching myself over the past few months - it's a great tool and I'm hoping to use it to analyse my PhD data.As I'm a bit of a newb, I'd really appreciate any feedback and/or guidance with regards to the following questions that relate to generalized linearmixed modelling (or, at least, I think they do!)(if there is a 'better',

Full_Name: jan hattendorf Version: 2.9.0 OS: XP Submission from: (NULL) (213.3.108.185) I get an incorrect result for (41/10-1/10)%%1 [1] 1 The error did not occur with other numbers than 41 (1, 11, 21, 31, 51, ...) test <- rep(NA, 1000) for(i in 1:1000){ test[i] <- i/10-1/10 } test[test%%1==0]

Hello Experts, how do I reformat a data frame in the way described below: df1: ID desc resist thick temp 1 4711 100 5 20 2 4712 101 4 21 3 4711 99 3 19 4 4712 98 7 22 TO df2: id desc Param Value 1 4711 resist 100 1 4711 Thick 5 1 4711 temp 20 2 4712 resist 101 2 4712 Thick 4 2 4712 temp 21 3

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

Hi! (a complete newby, but will not give up easily!) I was wondering if there is any way to decouple the axis and tick mark widths? As I understand they are both controlled by the lwd setting, and cannot be controlled independently? For example I might want to create major and minor ticks, which I now know how to do by superimposing two axes with different at settings, but what if I also wanted

Apologies for continuing to ask about this but . . in my quest to fit a gamma GLMM model to my data (see partial copy of thread below), I'm exploring using hglm today. The question of the day has to do with the errors I'm currently getting from the hglm package. Can hglm handle a model with nested random effects? I don't see an example of one of those in the package documentation. If

Hi all, I'm trying to fit models for data with three levels of nested random effects: site/transect/plot. For example, modelincrBS<-glmer(l.ru.ba.incr~shigo.av+pre.f.crwn.length+bark.thick.bh+Date+slope.pos.num+dens.T+dbh+leaf.area+can.pos.num+(1|site/transect/plot), data=rws30.UL, family=gaussian, na.action=na.omit) but I get the following error: Error: length(f1) == length(f2) is not

Hi to all, Here is a question which I cannot solve. Appreciate so much for any suggestions! I have a squared region which is irregularly divided into many rectangular patches. Each patch is associated with a value, and two patches possibly share a common value. I hope to fill in each patch a pattern according to its value. For instance, if a patch has value 1, then I fill in that patch

After a long and winding road (sorry but I'm a novice) I get to a final result which is quite close to what I need; nevertheless I would like to tweak a little further the xyplot so that I can get ***for each single panel defined by variable z*** a finer control over: -the x and y the limits: I would like to be the same for both axes; -the number of thick marks: again I would like to be same

Dear People, In a qqplot I am doing, I get lines/points that are very thick. I've tried setting the lwd variable to 0.1, but it doesn't seem to have any effect. Also, I have set the value of lty to dashed, but I still get dots. The command looks like qqplot(cdf.inv(seq(0,1,length=size),theta,pos,len),empmargdistvec(len,theta,pos,size), xlim=c(-theta,theta), ylim=c(-theta,theta),

Hello! I am using glmmML for a logitic regression with random effect. I use the posterior.mode as an estimate for the random effects. These can be very different from the estimates obtained using SAS , NLMIXED in the random with out= option. (all the fixed and standard error of random effect estimators are almost identical) Can someone explain to me why is that. The codes I use: R:

Hi All, I am new to both Ruby and Rails, so please be patient with me. I am currently working on a view for a controller and I have used the following excerpt: <td><%= link_to_image "b_edit", :action => ''edit'', :id => part_prefix %></td> The image is being displayed and the link is working. However, the formatting of image rendered

I'm unclear about where and how this is to be used. I gather it is to be a new standard, but I didn't seem to find anything about it in the Dovecot Wiki Mail Location page. There are some words about it in the 1.1.12/3 release notes, but perhaps I'm thick, but they're meta-ambiguous to me. Is it meant to be used like this: mail_location =

Dear all, In the x-axis of a xyplot I define dates ,for example 01-10-2007 instead of numbers, in a range of 77 days. I would like in my output to have the whole date as a point. When I plot it, then the output is a big mess! I can not distinguish anything; the result is a thick, black line of overlapping dates. Is there any solution so I can really distinguish and see clearly the dates in the

I'm experiencing odd graphics device behavior running R 2.15.3 on Ubuntu. Regardless of what I try like: require(stats) plot(cars) lines(lowess(cars)) plot(sin, -pi, 2*pi) for example, the graphics device fills the entire screen with the graphic and a very large font. When I shrink the graphics device window, the lettering remains large and the line thicknesses stay quite thick. It may have