similar to: R-help Digest, Vol 124, Issue 22

Hi Martin, In using the Cluster Package, I have results for PAM and DIANA clustering algorithms (below "part" and "hier" objects): part <- pam(trout, bestk) # PAM results hier <- diana(trout) # DIANA results GeneNames <- show(RG$genes) # Gene Names are in this object But

Dear R-Help, I am using the function "topTable" from the LIMMA package. To estimate adjusted P-values there are several options (adjust="fdr" , adjust="BH") as shown below: topTable(fit, number = 10, adjust = "BH", fit$Name) I guess any of these options (fdr, BH, etc.) is using a default of FDR=0.05 which is quite conservative (i.e., very

Dear R Users, In the expresso function, which combination of these methods for data pre-processing (when using affymetrix oligo arrays) is the best: bgcorrect.metod = rma rma2 mas normalize.method = qspline quantiles loess pmcorrect.method = pmonly subtractmm mas summary.method = liwong avgdiff medianpolish mas There are many options within each method. I would appreciate a hint on the best

Dear R users, I am using the beadarray package. I am trying to upload raw bead-level data using these commands: ######################################################## library(beadarray) datadir <- ("C:/Computer_programs/R/beadarray/cecilia") targets = read.table("targets.txt", sep = "\t", header = TRUE, as.is = TRUE) BLData = readIllumina(arrayNames =NULL,

In using the NLME package (R 2.6.1 for Windows), I am having a problem in running an R script that used to run with no problems using a Linux OS in 2004. So I am wondering if during these last ~3 yrs we had major changes in the syntax of the NLME package that I am not aware. This is the R script: library(nlme) treat=as.factor(c(1,2,1,2,1,2,1,2)) mouse=as.factor(c(1,1,2,2,3,3,4,4))

Dear R-Help, I am using the LIMMA User's Guide 5 January 2007 PDF version. For the example show in Section 7.4 DIRECT TWO-COLOR DESIGNS (pgs. 33-34), I could not grasp the rationale in developing the contrast.matrix with these R statements (">" indicates the R command prompt): > contrast.matrix <-

Dear All, How to cite the PDF user's guide for the LIMMA package? This is not about how to cite the LIMMA package. Roger Roger L. Vallejo, Ph.D. Computational Biologist & Geneticist U.S. Department of Agriculture, ARS National Center for Cool & Cold Water Aquaculture 11861 Leetown Road Kearneysville, WV 25430 Voice: (304) 724-8340 Ext. 2141 Email: roger.vallejo@ars.usda.gov

Does anyone know if the sib TDT has been implemented in R 1. Spielman, R.S., and Ewens, W.J. (1998) A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am J Hum Genet 62, 450-458 -- Farrel Buchinsky, MD Pediatric Otolaryngologist Allegheny General Hospital Pittsburgh, PA

Take a look at lmekin() in the coxme package. The motivating data set for my development of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families. The random effect is an intercept per subject with sigma^2 K as its variance where K is the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice, etc). lmekin is a linear models front

Dear Jarrod, I have a data set where residual have a heavy-tailed distribution with some extreme residual values and consequently the distribution deviates from the Gaussian one. Is it possible to include an skewed-normal density for the residual in MCMCglmm package? I have done the analysis of this data with both ASReml & MCMCglmm. The results are similar and outcome from MCMCglmm

Ron Crump wrote: > Hi, > > I have a dataframe that contains pedigree information; > that is individual, sire and dam identities as separate > columns. It also has date of birth. > > These identifiers are not numeric, or not sequential. > > Obviously, an identifier can appear in one or two columns, > depending on whether it was a parent or not. These should > be

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 MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for

I have a question for users of MCMCglmm that have experience implementing the zero-inflated poisson model. I find that the documentation, and previous questions, do not offer a lot of clear guidance on specifying and interpreting the zipoisson model. In particular, I see a lot of zero-inflated poisson examples that use the at.level(trait, x):variableName syntax. Specifically, the MCMCglmm

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi all, I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm. I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go into).

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use

Hi Chin Yi, If you are trying to correlate "Health" with "Disease", i.e. cydf<-read.table(text="OTU ID Health Disease Bacterial 1 0.29 0.34 Bacterial 2 0.25 0.07 Bacterial 3 0.06 0.06 Bacterial 4 0.07 0.09 Bacterial 5 0.02 0.05", header=TRUE) print(cor(cydf$Health,cydf$Disease)) [1] 0.7103517 If you are getting that error, it probably means that

On 2017-07-05 11:56, Jim Lemon wrote: > Hi Chin Yi, > If you are trying to correlate "Health" with "Disease", i.e. > > cydf<-read.table(text="OTU ID Health Disease > Bacterial 1 0.29 0.34 > Bacterial 2 0.25 0.07 > Bacterial 3 0.06 0.06 > Bacterial 4 0.07 0.09 > Bacterial 5 0.02 0.05", > header=TRUE) >

Hi David, Thanks for your comment. I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already know the answer. I will reconsider whether my University's ethics approval would allow me to post the data and update the question if I think