similar to: GLMM, ML, PQL, lmer

Hi, I would appreciate if someone could comment on this problem I am experiencing. I am writing a C++ program to be called from R. In this program, there is a verbose switch that decides whether to print some debugging info using Rprintf. On windows, things work ok. On linux, things are fine in non-verbose mode, but in verbose mode, I get error saying "C stack usage is too close to the

But not quite. If I could just get past the sound, I think it might start and run. I'm running Wine in desktop display mode, emulating win98. comdlg32, shell32, shlwapi, and '*' are set to "native, builtin" while the rest of the DLLs that the default installation names are builtin, native. I've tried running it with 'winedbg --gdb civilization3', and it always

I am trying to run separate regressions for different groups of observations using the lapply function. It works fine when I write the formula inside the lm() function. But I would like to pass formulae into lm(), so I can do multiple models more easily. I got an error message when I tried to do that. Here is my sample code: #generating data x1 <- rnorm(100,1) x2 <- rnorm(100,1) y <-

OS X R 4.3.3 Colleagues I have created objects using the Surv function in the survival package: > FIT.1 Call: survfit(formula = FORMULA1) n events median 0.95LCL 0.95UCL SUBDATA$ARM=1, SUBDATA[, EXP.STRAT]=0 18 13 345 156 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=1 13 5 NA 186 NA SUBDATA$ARM=2, SUBDATA[, EXP.STRAT]=2 5

Hi all, The problem is that when you try to use the function summary of a polr object in a function, it does not work. The problem is not related to the formula or the structure of data involved. It is probably related to the use of the function "vcov" in the code of summary for polr, and the iterative procedure to estimate the Hessian. Anyway, here there is an example extracted from

Hi Dennis, look at the help page for summary.survfit, the Value n.event. G?ran On 2024-05-15 22:41, Dennis Fisher wrote: > OS X > R 4.3.3 > > Colleagues > > I have created objects using the Surv function in the survival package: >> FIT.1 > Call: survfit(formula = FORMULA1) > > n events median 0.95LCL 0.95UCL >

Hi all, I have a problem to estimate Std. Error and t-value by ?polr? in library Mass. They result from the summary of a polr object. I can obtain them working in the R environment with the following statements: temp <- polr(formula = formula1, data = data1) coeff <- summary(temp), but when the above statements are enclosed in a function, summary reports the following error:

I am having trouble predicting new data with a model created from package mboost: > mb1<-glmboost(as.formula(formula1),data=data_train,control=boost_control(mstop=400,nu=.1)) > f.predict<-predict(mb1,newdata=data_train) Error in scale.default(X, center = cm, scale = FALSE) : length of 'center' must equal the number of columns of 'x' Ultimately I want to predict

Hi everyone, We have been trying to construct a Lasso-cox model with discrete time. We conducted follow-up examinations on the epileptic attack after tumor surgical resection among glioma patients. The patients are followed-up in the 6/12/24 months after surgical resection, which makes the epilepsy-free time discrete (6/12/24 months). We calcluated many features from the T2-images

Hi, Having a matrix F.zoo (6575,189) with NA's in some columns I'm trying to extract from each column the percent of days within an specific range, so I've wrote this procedure: length(subset(F.zoo[,86],(F.zoo[,86]>=5) & (F.zoo[,86]<= 9)))/(length(F.zoo[,86])-length(subset(F.zoo[,86],is.na(F.zoo[,86]))))*100 But to do this for each column (189) is pretty hard, so I want

Reinventeaza-te! Ai fost invitat de CHELARIU CRISTIAN-IULIAN la jocul Regleaza Linkutil ca pagina de Start de pe www.linkutil.ro cu castiguri de 500 RON saptamanal! Acest email este trimis tie pentru ca prietenul tau/prietena ta CHELARIU CRISTIAN-IULIAN se afla in concurs si are nevoie de sprijinul tau, deoarece sansele se maresc daca se fac mai multe inregistrari pentru invitatii. Martea

https://forum.linode.com/viewtopic.php?f=19&t=14570&p=72785 I can not figure out what I need to do. Apparently according to linode support, the VM is trying to grab an IPv6 address with some privacy stuff enabled by default causing it to not grab the IPv6 address that is assigned to me. Nothing I have tried seems to work, and it seems that Linode support are far more familiar with

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Hello, Does anyone know of a function that will determine whether or not a formula object has a left hand side? I.e., can differentiate between y ~ x + z and ~ x + z Perhaps I'm overlooking the obvious... Thanks!

I am writing a program in which I would like to take in a formula, change the response (Y) variable into something else, and then pass the formula, with the new Y variable to another function. That is, I am starting with formula <- Y~X1+X2+X3 and I'd like to do something like Y <- formula$Y newY <- f(Y) lm(newY~X1+X2+X3) So far, it seems that my

Hi all, I have searched for a long time to find out R program about V ariable S election for GLMM (Generalized Linear Mixed Model). I saw several great R packages for V ariable S election. I  also found several R packages for GLMM. But, I did not find yet R package about V ariable S election for GLMM even though sevel  papers about it have been published.   In fact,  I need V ariable 

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Hi R-users,   I intend to apply a mixed model on a set of longitudinal data, with a negative binomial distributed dependent variable, and after following the discussions on R help list I saw that more experienced people recommended using lmer (from lme4 pack), glmmPQL (from MASS) or glmm.admb (from glmmADMB pack)     My first problem: yesterday this syntax was ok, now I get this weird message (I