similar to: Hierarchical Terms in RE models?

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

Dear All I am attempting some power analyses, based on simulated data. My experimental set up is thus: Bleach: main effect, three levels (control, med, high), Fixed. Temp: main effect, two levels (cold, hot), Fixed. Main effect interactions, six levels (fixed) For each main-effect combination I have three replicates. Within each replicate I can take varying numbers of measurements (response

dear colleagues, I want to use hierarchical partitioning methods to evaluate variance contributions of differente predictors within a generalized linear mixed model framework. I don't know if this is possible, because I can not found anything about this issue. My error is poisson and I, at least, a random factor in the designed analysis. I acknowledge any idea about how deal with

Dear R users, I would like to fit cross classified or multiple membership logistic models or a 3 level hierarchical logistic model using the Umacs package. Can anyone advise me on how to proceed or better point me to examples of how its done. Regards, -- Luwis Diya, Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Kapucijnenvoer 35 blok d - bus 7001, 3000 Leuven,

Hi all -- So I'm working through my statistics homework again, and trying to reproduce the examples in the book (Kirk's _Experimental Design_, third edition) in R. This is a completely randomized hierarchical design (CRH-28(A)). The B factor is completely nested within the A factor. Pages 480-482, for those playing along at home. I can use: summary(aov(value ~ a + Error(b),

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

I've been using sna to work with some networks, and am trying to visualize them easily. My networks are hierarchical (food webs). All of the layout engines I've tried with gplot don't seem to plot hierarchical networks, as one would using dot from graphviz. While I could do all of this by outputting to dotfiles and running it through graphviz, the graphics I get from R are much

Hi, I am wondering if anyone knows how to perform an F-test on the change in R square between hierarchical models in R? SPSS provides this information and a researcher that I am working with is interested in getting this information. Alternatively, if someone knows how I can calculate the test statistic (SPSS calls it F-change?) and dfs that would be helpful as well. The output and the test I am

Dear Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

Hi R users! I have a bit of a problem with using an hierarchical clustering algorithm: a<-c(1:15) b<-rep(seq(1:3), 5) c<-rnorm(15, 0,1) d<-c(sample(1:100, 15, replace=T)) e<-c(sample(1:100, 15, replace=T)) f<-c(sample(1:100, 15, replace=T)) data<-data.frame(a,b,c,d,e,f) q<-data.frame(data$d, data$e, data$f) q<-scale(q) What i want to do is to use an

Hello All, I need to calculate cumulative lengths along a hierarchical network topology (it's a representation of a tree). I can roll my own, but thought that there might be a package out there that would handle hierarchical network topology functions such as this nicely (and that might bring along other useful functions as well). However, I haven't found one after searching. I

Dear R-users, I'm somehow new to the topic of mixed models. I'd liked to simulate data with a specific correlation-structure and analyze it with lme(). I have a fixed effect (group) and a random effect (block): y group block -5.2158969 -2 1 -2.4174197 -2 1 -2.6731572 -2 1 -3.6310082 -2 1 -0.3888706 0 2 1.3422255 0 2

I would appreciate any information about how to carry out a hierarchical cluster analysis with cluster of subjects. I need to find cluster of subjects that share many variables. I know that fcp package have a lot of options to carry out the regular hierarchical cluster analysis (cluster of variables). Thank´s in advance Juan Hernández Facultad de Psicología La Laguna University Canary Islands

hi, I want to do hierarchical clustering with Jaccord index. I tried to do with vegan package for finding index and hierarchical clustering with hclust function. While doing clustering it is showing an error message as "invalid distance method". I would be grateful if anyone tells how to rectify the error. Thanks in advance,   kind regards, Ms.Karunambigai M PhD Scholar Dept. of

Hi there, is there any R-function for k-nearest neighbour agglomerative hierarchical clustering? By this I mean standard agglomerative hierarchical clustering as in hclust or agnes, but with the k-nearest neighbour distance between clusters used on the higher levels where there are at least k>1 distances between two clusters (single linkage is 1-nearest neighbour clustering)? Best regards,

Dear R users, Is there a way to apply a “stopping rule” in hierarchical Clustering? I have a data and I want to find the optimal number of clusters while doing hierarchical clustering. I would be deeply obliged if someone, whoever applied stopping rule, could send me the scripts. thanks in advance Regards Eliza botto [[alternative HTML version deleted]]

Hi Friends, I'm trying to model the consumer decisions (Click-Through Rate and Conversion) in Search Engine Advertising using a hierarchical Bayesian binary logit. The input data is the weekly CTRs and Avg. Position for each search keyword. CTR is modeled as (for each keyword i and week j): Pij = exp(C + Bi x Positionij + A1 x Lengthi + A2 x Brandi + A3 x ProductSpecifici) / [1 + exp(C +

Addendum: I tried a gamma fit in glmmPQL and got the same errors. *Ben Caldwell* PhD Candidate University of California, Berkeley On Tue, May 17, 2011 at 3:51 PM, Benjamin Caldwell <btcaldwell@berkeley.edu>wrote: > Hello > After seeing this ( > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/005213.html) email > I thought I would check the issue with a gamma family

Hi, I have some hydrochemical data from observation stations of a river network. The data set provides information about the river hierarchy, i.e. the order of the rivers (main river is 1st order, contributing is 2nd order, and contributing to 2nd is 3rd order... etc). Now I would like to use the hierarchical information to: a) plot a dendrogram of the observation stations b) perform some simple

Dear experts, I would like to plot a hierarchical clustering of 300 items. I had a distance matrix with dimension of 300*300. The 300 items were from 7 groups which I would like to label with 7 different colours in the plot. > h<-hclust(as.dist(300_distance_matrix)) > plot(h,hang=-1,cex=0.5, col="blue") I used the above script to plot the result. The cluster was all blue,