similar to: meta regression in R using lme function

Hi, I would like to do a meta-analysis, i.e., a mixed-effects regression, but I don't seem to get what I want using both the nlme or metafor packages. My question: is there indeed no way to do it? And if so, is there another package I could use? Here are the details: In my meta-analysis I'm comparing different studies that report a measure at time zero and after a certain followup

I am running R.And I want to make parallel computations. When using the snow package under R-2.13.1patched or the parallel package under R 2.14.0dev, R isn't able to create a cluster. When typing: cl <- makeCluster(4) R doesn't respond. It doesn't crash either. specs: OS: 32 bit, Windows XP Prof, SP3. Processor: Intel Core i3 cpu, quad core. My colleague works on exactly the

Dear all, Edmond Ng (http://multilevel.ioe.ac.uk/softrev/reviewsplus.pdf) provides an example to fit the mixed effects meta-analysis in Splus 6.2. The syntax is: lme(fixed=d~wks, data=meta, random=~1|study, weights=varFixed(~Vofd), control=lmeControl(sigma=1)) where d is the effect size, study is the study number, Vofd is the variance of the effect size and meta is the data frame.

Hi, I am performing meta-regression using linear mixed-effect model with the lme() function that has two fixed effect variables;one as a log transformed variable (x) and one as factor (y) variable, and two nested random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y),

Hello, Great to see the new metafor package for meta-analysis. I would like to perform a meta-analysis in which I initially calculate the intercept of the model with a nested random-effects structure. In lme, this would be model<- lme(v3~1, random=~1|species/study, weights = varFixed(~Wt), method = "REML") where multiple effects sizes are measured for some studies and more than

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Dear R developers, is there a preferred format or strategy for making a patch to contribute to a package that is maintained by R-core? Berwin Turlach and I have written a very minor extension to lmeControl to allow it to pass an argument to nlminb for the maximum number of evaluations of the objective function. I've edited the nlme/R/lme.R and nlme/man/lmeControl.Rd files. I can diff the

Dear Alina If I understand you correctly you cannot just have a single predicted curve but one for each level of your factor. On 09/08/2017 16:24, Alina Vodonos Zilberg wrote: > Hi, > > I am performing meta-regression using linear mixed-effect model with the > lme() function that has two fixed effect variables;one as a log > transformed variable (x) and one as factor (y)

Thank you Michael, Curves for each level of the factor sounds very interesting, Do you have a suggestion how to plot them? Thank you! Alina *Alina Vodonos Zilberg* On Thu, Aug 10, 2017 at 7:39 AM, Michael Dewey <lists at dewey.myzen.co.uk> wrote: > Dear Alina > > If I understand you correctly you cannot just have a single predicted > curve but one for each level of your

I am using R 2.7.0 on a linux platform. I am trying to reproduce a 2002 example using lme from the nlme library. I want to change the otimizer from the default (nlminb) to optim. Specifically, this is what I am trying to do: R> library(nlme) R> library(car) # for data only R> data(Blackmoor) # from car R> Blackmoor$log.exercise <- log(Blackmoor$exercise + 5/60, 2) R>

Dear R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

Dear R users, in the example below the name of the genus will be displayed in the main titles using the variable predictor[i] and paste. I would like to have the genus name in italic. However all my attempts using expression and substitute failed. Does anybody know a solution? Thanks a lot in advance. bernd Acrobeles <-c(65.1,0.0,0.0,0.0,0.0,0.0) Acrobeloides

Dear All, This is not strictly R related (though I would implement the solution in R; besides, being this list so helpful for these kinds of stats questions...). I got a "strange" request from a colleage. He has a bunch (approx. 25000) subjects that belong to one of 12 possible classes. In addition, there are 8 covariates (factors) that can take as values either "absence"

Dear list, I was trying to create a VarClass for nlme to work with Fay-Herriot (FH) models. The idea was to create a modification of VarComb that instead of multiplying the variance functions made their sum (I called it varSum). After some fails etc... I found that the I was not getting the expected results because I needed to make sigma fixed. Trying to find how to make sigma fixed I run into

Hello fellow R users, I have been getting a strange error message when using the cv.glmnet function in the glmnet package. I am attempting to fit a multinomial regression using the lasso. covars is a matrix with 80 rows and roughly 4000 columns, all the covariates are binary. resp is an eight level factor. I can fit the model with no errors but when I try to cross-validate after about 30 seconds

Dear R-help list readers, I am fitting a mixed model using the lme function (R V 2.3.1 for Windows). This is an example: dep<-c(25,40,33.33,60,70.83,72,71.43,50,40,53.33,64,54.17,60,53.57) yes<-c(0,1,2,3,4,5,6,0,1,2,3,4,5,6) treat<-c(1,1,1,1,1,1,1,0,0,0,0,0,0,0) #factor If I now fit a model with random slopes as well as intercepts: model1<-lme(dep~yes,random=yes|treat) R

I have a question about the lme function in R. My question is: After I got the object from function lme, why the numIter value of the object is always NULL? Following is my code: jjww<-lme(y~x*zz,data=simul,random=~x|group, control=lmeControl(returnObject=TRUE)) attributes(jjww) jjww$numIter the first 20 observation of data simul are: > simul y

Hi, I have a simple random-coefficients model for m subjects: y = b0 + b1 x + r0 + r1 x + e where b0 and b1 are fixed parameters, r0 and r1 are random, e ~ N(0,s2 I) and R' = [r0, r1] ~ N(0,T). I try to obtain the EM-estimates of s2 and the elements of T by lme(y~x,data=mydata,random= list(group=~x), control=lmeControl(maxIter = 0, niterEM=100,msVerbose = TRUE)) Does

Hello, all, I am running some simulations to estimate power for a complicated epidemiological study, and am using lme and lmer to get these estimates. I have to run a few thousand iterations, and once in a great while, an iteration will create fake data such that the model won't converge. I see from Google searches that this is not an uncommon situation. My question: is there a way to