similar to: metafor - multivariate analysis

Dear Wolfgang Viechtbauer and R users, I have few questions regarding the development of the package 'metafor. As you suggested , I post to the R-help mailing list. I read you're planning an extension of this method to the multivariate case. I think it would be a useful tool. I'm currently performing some analyses with R on multiple outcomes, using the Stata command mvmeta to get

Dear R Community, I am pleased to announce the release of a new package called 'mvmeta', now available on CRAN (version 0.2.0). The package mvmeta provides some functions to perform fixed and random-effects multivariate meta-analysis and meta-regression. This modelling framework is exploited to pool multiple correlated outcomes across studies, and already applied in different fields:

Dear R Community, I am pleased to announce the release of a new package called 'mvmeta', now available on CRAN (version 0.2.0). The package mvmeta provides some functions to perform fixed and random-effects multivariate meta-analysis and meta-regression. This modelling framework is exploited to pool multiple correlated outcomes across studies, and already applied in different fields:

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

Dear R community, I'm using the ,metafor'-package by Wolfgang Viechtbauer (Version: 0.5-5) to calculate random-effects meta-analyses using Correlations and Sample Sizes as the raw data. (By the way: Really a nice piece of work, Wolfgang! Thanks heaps.) I specified the "rma.uni' function so that it looks like this: MAergebnis<-rma.uni(ri=PosOutc, ni=N,

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Hi all, I received a request from the R maintainers (kurt Hornik) to fix some problems arising in r-devel with my package mvmeta. Basically, the check returns a series of warnings about a mismatch in the documentation of some data sets between the code and the docs. See the results at: http://cran.r-project.org/web/checks/check_results_mvmeta.html The problem might be that I grouped the

Dear all, I get an unexpected error when using the mantel.test function in library(ape). Is there something wrong with my matrices? > dmatr<- read.table("obt.Loiselle.kinship.intens02.DIST.matrix.txt",head=F) > kmatr<- read.table("obt.Loiselle.kinship.intens02.KINSHIP.matrix.txt",head=F) > > mantel.test(as.matrix(kmatr), as.matrix(dmatr), nperm = 500, graph

Hi, I have some code I run interactively through the R interpreter and it works fine. I then run it as a script with Rscript and I get an error. The error is coming when Rscript builds a model matrix. Here is the Rscript code : #!/usr/lib/R/bin/Rscript --verbose require(MASS) options(contrasts = c("contr.treatment", "contr.poly")) .... form <- as.formula(paste(ef.var,

Hi Experts, I am new to R, and was wondering how to do 3D linear regression in R. In other words, I need to Fit a 3-Dimensional Line to Data Points (input). I googled before posting this, and found that it is possible in Matlab and other commercial packages. For example, see the Matlab link:

Dear all, I have run the confint() function according to below and I get the following error message: > confint(stepAIC.glm.spe.var.konn2.abund, level=0.95) Waiting for profiling to be done... Error: missing value where logical needed In addition: Warning message: NaNs produced in: sqrt((fm$deviance - OriginalDeviance)/DispersionParameter) or > confint(stepAIC.glm.spe.var.konn2.abund,

Hi, I have a data set: > Dataset X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 1 user1 m 22 19 28 24 12 18 9 7 4 5 4 7 5 7 9 2 user2 f 25 19 23 18 18 15 6 8 6 6 7 10 7 7 7 3 user3 f 28 21 24 18 15 12 10 6 7 9 5 10 5 9 5 4 user4 f 26 19 26 21 12 18 6 6 5 1 3 8 6 5 6 5 user5 m 21 22 26 18 9 6 4 6 1

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the

Dispongo de un experimento en el que cinco tratamientos ha sido aplicados a cinco grupos de voluntarios. En cada grupo había tres personas y a cada persona se le tomaron 3 medidas, En total dispongo de 45 medidas, pero evidentemente no son independientes entre sí. Si no tomo en cuenta que las medidas de la misma persona son más parecidas entre sí (bloques anidados) estaría incurriendo en

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

LLVMers, The 2.1 pre-release (version 1) is available for testing: http://llvm.org/prereleases/2.1/version1/ I'm looking for members of the LLVM community to test the 2.1 release. There are 2 ways you can help: 1) Download llvm-2.1, llvm-test-2.1, and the appropriate llvm-gcc4.0 binary. Run "make check" and the full llvm-test suite (make TEST=nightly report). 2) Download

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >