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Dear users of metafor, I am working on a meta-analysis using the metafor package. I have a excel csv database that I am working with. I am interested in pooling the effect measures for a particular subgroup (European women) in this csv database. I am conducting both sub-group and meta-regression. In subgroup-analyses, I have stratified the database to create a separate csv file just for European

Dear all, We have got data (response and predictor variables) for each country of the world; I started by fitting standard GLM and tested for spatial correlation using variogram models (geoR) fitted to the residuals of the GLM. Spatial autocorrelation is significant. Therefore, I think about using general estimation equations (geeglm or geese in geepack) allowing for residual spatial

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,

Hi all I am analyzing a data set containing information about the behaviour of marine molluscs on a vertical wall. Since I have replicate observations on the same individuals I was thinking to use the geepack library. The data are organised in a dataframe with the following variables Date = date of sampling, Size = dimensions (mm) Activity duration of activity (min) Water = duration of

Trying to do a meta-analysis of correlations in R using the meta package; have tried several things and keep getting a similar error. Can anyone help explain the error? > cor<-c(-0.3018, 0.667, -3.8002, -0.607, -0.4885, -3.8002, -0.0701, 0.1348, -0.9505, -0.5709, -0.6127, -1.2419, -0.1511, -0.1054)> n<-c(3,4,3,3,3,3,16,36,30,9,3,3,30,4)> library(meta)> metacor(cor, n, data=NULL,

Hi I have 2 data.frames each of the same number of rows (approximately 30000 or more entries). They also have the same number of columns, lets say 2. One column has the date, the other column has a double precision number. Let the column names be V1, V2. Now I want to calculate the correlation of the 2 sets of data, for the last 100 days for every day available in the data.frames. My code looks

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-