similar to: hierarchical partitioning

Hi, I have a trouble trying to use \href in a rd doc. See example below: \references{ \href{http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702787/}{Vaniscotte A., Pleydell D., Raoul F., Quere J.P., Coeurdassier M., Delattre P., Li T., Qian W., Takahashi K., Weidmann J.C., Qiu J., Giraudoux P. 2009 Modelling and spatial discrimination of small mammal assemblages: an example from western

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl

Hello List, My question is an elementary one. I have run a fuzzy kmeans cluster using FANNY to group freshwater fish assemblages. I then went in the field to validate that classification and have retrieved new assemblage data for a new suite of streams. Therefore I would like to use Predict to determine how well the original clustering fits the new data. However I have not figured out a

Dear R users, I am using the *mgcv package* to model the ratio of hectares of damaged culture by wild boar in french departments according to some environmental covariates. I used a _Beta distribution_ for the response. For each department, we estimated the damaged in 3 different culture types (??Culture??). Our statistical individual are therefore the department crossed by the culture

Dear all, I am trying to compare several methods for classify data into groups. In that purpose I 'd like to developp model comparison and selection using AIC. In the lda function of the MASS library, the maximum likelihood of the function is not given in the output and the script is not available. Do anyone know how to extract or compute the maximum likelihood used in the lda function?

Hi, I hope someone knows the answer to this or has a real good reference about it (I am using Legendre & Legendre, Numerical Ecology, 1998).... My data is a data.frame with locations as rows and vegetation assemblages / species as columns. I've done a PCA, a correspondance analysis (CA) using ca in ca package and a detrended correspondance analysis (DCA) using decorana from vegan package.

Hi,everyone. I use the following program calculates Fisher's alpha from counts of individuals and species. The program is wrote by Prof. Kyle Harm. However, when I run the program, it can work very quickly sometimes, but it can not work very well sometimes. It depends on the counts of individuals and species. For example, > calc.alpha(1000,70) [1] 17.14375 > calc.alpha(10000,70) [1]

Statistics questions are largely off topic on this list, although they do sometimes intersect R programming issues, which are on topic. However, I believe a statistics list like stats.stackexchange.com might be more suitable for your query. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka

Hello, I would like to know what it means, when the hier.part's output has a data frame of a negative independent contribution for variables (?IJ) and how this can be caused. Thank you very much. Yours sincerely M. Heuner

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

I'm not sure how you are incorporating time period into your data structure. Typically we are looking at plots or assemblages as the rows and taxa as the columns. Time period adds a third dimension that could be added as blocks of rows. For example, depending on the resolution of your data, one approach would be to have up to 8 rows for each locality: Loc1.1000, Loc1.2000, . . . Loc1.8000. If

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

Hi. For a spatial analysis (its visualization) I need to produce a map of conditions and traits. When I used SpatialPixelsDataFrame (sp package) my grid cells were regular and between each row was a small gap, which is not only messy but incorrect. So I generated the coords of each grid from the centroids in order to elongate the cells manually (or does anybody know another way to solve this?).

Hello Amelie, I don't have an answer to your question, but I just wanted to point out this page I noticed recently ( http://hlplab.wordpress.com/2009/05/07/multinomial-random-effects-models-in-r/), which might be helpful. I'm also interested in figuring out how to do a multinomial glmm, so if you find out anything I'd be happy to hear more about it! Based on what I've found so

Hi, I''m using heatmap.2 to cluster my data, using the centroid method for clustering and the maximum method for calculating the distance matrix: library("gplots") library("RColorBrewer") test <- matrix(c(0.96, 0.07, 0.97, 0.98, 0.50, 0.28, 0.29, 0.77, 0.08, 0.96, 0.51, 0.51, 0.14, 0.19, 0.41, 0.51), ncol=4, byrow=TRUE)

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

I'm parallelizing some computation on hierarchical data, and would find it natural to do something like this (where a call to parLapply is embedded in outer call to parLapply): cl <- makeCluster(rep.int('localhost', 5), type='SOCK') clusterExport(cl, 'cl') parLapply(cl, 1:5, function(i) parLapply(cl, 1:5, function(j) i * j)) Snow

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

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

> From: Martin Devera <devik@cdi.cz> > Subject: [LARTC] (E)SFQ suggestion > Hi, > just simple note. Maybe it is already in progress :) > > There are attempts to replace hashing routine in SFQ to > consider IPs or ports. > What about to use HRR - roundrobin around bunch of IP > adresses and then smaller WRR for ports per IP ? > It would solve both