similar to: ANOVA with means and SDs as input

Dear R-experts! Running a meta-analysis (using the magnificent metafor-package), I use cohen's d as a main outcome measure in a random-effects model. For most of the samples cohen's d is derived form a comparison of two groups (A & B). However some studies report results from an ANOVA (one-factor with three levels: C,D,E) whereas two groups (C,D) correspond to one group in the other

Hi - I am conducting a meta-analysis and I have a matrix of f-statistics, Ns and dfs from a series of studies that tested for an interaction in a 2x2 anova. I'd like to test whether the 2x2 interaction is significant in the aggregate. Similarly, I have a matrix of chi-square statistics that I'd like to meta-analyze. How can I input these test statistics into the "metafor"

Hello,   I would like to do a meta-analysis with the package « metafor ». Ideally I would like to use a mixed model because I’m interested to see the effect of some moderators. But the data set I managed to collect from literature presents two limits.   -         Firstly, for each observation, I have means for a treatment and for a control, but I don’t always have corresponding standard

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

Dear R-community, I'm currently trying to assemble a forest plot using the "forest" function from package "metaphor". Works well. Even the regular "main"-argument works for adding a title to the graph. However, I would like to add one top row which explains the nature of the columns. Very much like the usual header in spreadsheet programs. For example:

Hi, I want to sample from a distribution (say a normal distribution, for example) using vectors of the different parameters (i.e. the mean and standard deviation). That is, I have a list/vector of say 100 means and another of the corresponding 100 SD's, and I want a matrix of 100 rows (one for each mean and SD pair) each having 1000 random samples. Something like: sample_matrix =

I am a relatively new user of R, and have a question on ANOVA I have a sumamrised data set of the form (data dummy only) Cell Treatment Total Resp 1 1 25000 682 1 0 35000 600 2 1 5000 40 2 0 4000 30 3 1 36000 2400 3 0 400 250 and wish to perform ANOVA. Because its sumamrised I need to tell R to treat the data as if I had all the individual rows. Otherwise, I only end up with 1 or

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,

Dear R-experts! In a meta-analysis (metafor) I would like to assess the effect of two categorical covariates (A & B) whereas they both have 4 levels. Is my understanding correct that this would require to dummy-code (0,1) each level of each covariate (A & B)? However I am interested in the main-effects and the interaction of these two covariates and the dummy-coding would only allow to

Dear metafor users, I am working on a meta-analysis of reliability and the correlation associations. I need some help about conducting categorical moderators variables. Questions 1: How to conduct the weighted ANOVAs assuming a mixed-effects model on the tranformed alpha coefficients/the tranformes correlation coefficients for the categorical moderator variables? Questions 2: How to

Hello,   Anyone familiar with robust ANOVA in R? Please help.   I am conducting a between-between-within (2 X 2 X 2) ANOVA, with the focus on the significance of the three-way interaction. Because of very uneven sample sizes, and the violation of covariance homogeneity, I need to use some sort of robust ANOVA method, and I prefer bootstrap. I am aware of Rand Wilcox's robust package that can

I am conducting an experiment with four independent variables each of which has three or more factor levels. The sample size is quite large i.e. several thousand. The dependent variable data does not pass a normality test but "visually" looks close to normal so is there a way to compute the affect this would have on the p-value for ANOVA or is there a way to perform an nonparametric test

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 Metafor users, I'm conducting a metaanalysis of prevalence of a particular behaviour based on someone elses' code. I've been labouring under the impression that this: summary(rma.1<-rma(yi,vi,mods=cbind(approxmeanage,interviewmethodcode),data=mal,method="DL",knha=F,weighted=F,intercept=T)) is doing the multivariate analysis that i want, but have read that

I sent a message a couple days ago about doing calculations for power of the ANOVA. Several people got back to me very quickly which I really appreciated. I'm working now on a similar problem, but instead of a balanced ANOVA, I have an unbalanced one. The first part of the question was: You assume that the within-population standard deviations all equal 9. You set the Type 1 error rate at á

Dear experts, I just noticed that I get different results conducting type III anova using drop1 or the Anova command from the car package. I suppose I made a mistake and hope you can offer me some help. I have no idea where I got wrong and would be very grateful for explaination as R is new terrain for me. If I run the commands in line, they produce the same results. But if I run them in

I am trying to implement an ANOVA on a pair of quantile regression models in R. The anova.rq() function performs a basic check to see whether the models are nested, but I think this check is failing in my case. I think my models are nested despite the anova.rqlist() function saying otherwise. Here is an example where the GLM ANOVA regards the models as nested, but the quantile regression ANOVA

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

A new package is now available via CRAN, called metafor. The metafor package consists of a collection of functions for conducting meta-analyses in R. Fixed- and random-effects models (with and without moderators) can be fitted via the general linear (mixed-effects) model. For 2x2 table data, the Mantel-Haenszel and Peto's method are also implemented. The package also provides various plot

Hello, I experimented the Metafor and Meta packages in the scope of replacing Excel for meta-analysis. I performed the first working example provided in Michael Borenstein's book "Introduction to Meta-Analysis" with Excel, Metafor and Meta. The numbers given by my spreadsheet, which I validated from Borenstein's book, conrespond quite closely to those given by Meta, but