similar to: lme convergence error

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 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

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

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

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 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 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

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

I have a 45 x 16 data frame consisting of dissimilarities among 10 colors, giving in each column the 45 = 10*9/2 pairwise judgments for one of 16 subjects. The rownames identify each pair of colors, e.g, "AC" = ("A","C"), and the pairs are ordered by columns in the lower triangle of each distance matrix. > helm.raw <-

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 <-

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 guys, I dont know if my burner here got a problem. Im downloaded the iso's for cd 3 and 4 thrice and burned it on the cd thrice also. but when i test the cd's they failed. But calculating the checksum was ok. any suggestions ? --------------------------------- Do you Yahoo!? With a free 1 GB, there's more in store with Yahoo! Mail. -------------- next part --------------

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)

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)

I contacted the package developer and that lead to me removing events at time 0 (or subjects with only 1 longitudinal measurement). I then still had the error message "Can't fit a Cox model with 0 failures" which I have managed to avoid by adding 1.8*10^(-15) to all my survival times, any number greater than this also works but nothing smaller! Any explanation of this would help!

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

xdelta results: 666198016 CentOS-4.0-Beta-Binary-CD1.iso 635330560 CentOS-4.0-Beta-Binary-CD2.iso 661059584 CentOS-4.0-Beta-Binary-CD3.iso 662018048 CentOS-4.0-Beta-Binary-CD4.iso 444139520 CentOS-4.0-Beta-Binary-CD5.iso - 658491392 CentOS-4.0.rc1-i386-bin1of5.iso 611952640 CentOS-4.0.rc1-i386-bin2of5.iso 666099712 CentOS-4.0.rc1-i386-bin3of5.iso 663128064 CentOS-4.0.rc1-i386-bin4of5.iso

Hi all: I perform the linear mixed model for 300 persons, y is CD4 count,x is time. I randomized slope and intercept,so I can get 300 slopes and 300 intercepts.Now I wanna test wheter the variance of 300 slopes and 300 intercepts differs from zero. If the variance of 300 slopes(or intercepts) differs from zero at 0.05 significant level,I should randomize the slope(or intercept), and if not,I