similar to: LIMMA contrast.matrix

Hi I am using the flowClust package from BioConductor, which is largely implemented in c. For some of my data, the package occasionally (and quite stochastically) encounters a particular condition which halts its operation. At this point, it calls the error() function defined by Rcpp, and halts. What I would like to be able to do is to catch the error thrown, and retry the operation a few

I Dears, if that wouldn't take u too much effort I'd like to ask for a swift opinion: I have a alinear (mixed effect) model that I wish to run as a piecewise one. When I predict the values Iget quite some odds and disturbing results: The predicted stright line after the break point is not straight at all, instead behaves like if it was a hig order polynomial or something similar!!!! I

Hi, I'm trying to do a multiple events model using coxph. I need to choose which covariates stay in the final model and i'm using anova but it seems that it doesn't work with robust variances as a error message appears when I try to do it: /PWPfit<-coxph(Surv(ini,fim,status)~Sexo+Transmissao+Regime+HAART+Drogas+Psi+ + CD4+CV+Neo+IO+cluster(idPARTICIPANTE)+strata(Estrato),data=PWP)

Hi all,I have some questions about the covariants of regression. My target: To explore the trend of CD4 level through a period of time. Response variable: CD4 count Explanatory variable:time Also, the demology information is available,such as gender,occupation,income level... Q1,Are these variables of demology information called covariant? Q2,How can I correct the impact of

Hi dears while modeling an interaction random effect in lmer i receive the instantaneous error message > ldlM4<-lmer(ldl~rt*cd4+age+rf+pharmac+factor(hcv)+ + hivdur+(rt:cd4|id),na.action=na.omit,REML=F) *Warning message: In mer_finalize(ans) : false convergence (8) * I think the matter lies in syntax, 'cause i sistematically receive the same message even when changing response... PS:

Hi folks... check this out.. > GLU<-lme(gluc~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ + haartd+hivdur+factor(arv), + random= ~rt|id, na.action=na.omit) > intervals(GLU)$fixed lower est. upper (Intercept) 67.3467070345 7.362307e+01 7.989944e+01 rt *0.0148050160* 6.249304e-02 1.101811e-01 cd4

Thomas, Thank you for your reply about the for () loop. The as.character advice worked. Sorry for the delay in getting back to?I had to set the project aside for a few weeks. This didn?t work exactly as is for (patient in as.character(1:n)){ pt <- MRN == patient (rest of the function) } But this did for (patient in as.character(levels(MRN))){ pt <- MRN == patient (rest of the function)

Hi all: There's a question about glht function. My data:data_ori,which inclue CD4, GROUP,time. f_GROUP<-factor(data_ori$GROUP) f_GROUP is a factor of 3 levels(0,1,2,3) result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori) glht(result, linfct = mcp(f_GROUP="Tukey") ) Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected I can't

Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't update to 2.13). I am iterating through many linear mixed models for longitudinal data and I occasionally receive the following convergence error: > BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12, random =~ 1 + time | IID, data = d) Error in lme.formula(cd4 ~ time + genBI + genBI:time +

Hello! I need some help with creating plots for each study subject. I have tried the 'for' command as described in 'An Introduction to R', but I wasn't successful. Here's what I want to do: -Create and save the following overlayed scatterplots for each subject (1 to n). -The data frame is in the form of one line per visit per subject (so more than one line per

Hi all: My data has 3 variables: age(3levels : <30y=1 30-50y=2, >50y=3) gender(Male=0, Female=1) CD4 cell count(raw lab measurement) y(1:death 0:alive) I perform logistic regression to find out the factors that influence y. result<-glm(y ~ factor(age) + factor(gender) + CD4,family = binomial) >From the result,I can get OR(Odds Ratio) of gender via exp(Estimate of Female,

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

Hi all: I finished cox analysis like this: fit_cox<-coxph(Surv(dat$Time, dat$death) ~ dat$CD4 + strata(dat$gender),data=dat); > fit_cox Call: coxph(formula = Surv(data_ori$Time, data_ori$death) ~ data_ori$drug + strata(data_ori$gender), data = data_ori) coef exp(coef) se(coef) z p data_ori$drugddI 0.216 1.24 0.146 1.47 0.14 Likelihood ratio test=2.17 on

Hi All, A newbie question :-( How do I go about installing the add-ons found in CD4? Many thanks, Joao -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.centos.org/pipermail/centos/attachments/20050205/8c4f13bc/attachment-0005.html>

Hi all: I have a question about linear mixed model. my linear mixed model with randomized slope and intercept with interaction of time and group(g1,g2,g3): model<- glmmPQL(log10(CD4) ~ time + factor(group)+ time:factor(group), random = ~time|id) What I get is only the main and interaction of time and group.My question is: 1. How can I get the g1,g2,g3's slope respectively?In other

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 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

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 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