search for: metagene

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 Sir or Madam, I am trying to plot forest plot. I extracted odds ratio and their corresponding 95% confidence interval from papers, then I calculated the log(OR) and standard error using the following command OR<-metagen(logOR,selogOR,sm="OR") forest(OR,comb.fixed=TRUE,comb.random=TRUE,digits=2) However, it does not produce a forest plot. Can someone kindly help? Thank

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Version 2.0-0 of meta (an R package for meta-analysis) is now available on CRAN. Changes are described below. Yours, Guido Major revision R package meta linked to R package metafor by Wolfgang Viechtbauer to provide additional statistical methods, e.g. meta-regression and other estimates for tau-squared (REML, ...) New functions: - metareg (meta-regression) - metabias

Hello! I have some data stored into 2 separate csv file. 1 file (called A.csv) (12 results named Group1, Group2, Group3, etc...) odds ratios, 2 file (called B.csv) 12 corresponded errors. How to import that data into R and make forest plot like I saw inside help file Rmeta and meta with included different font colors and names trough X and Y axis. I know for meta libb ... out <-

Stefan Roepcke <stefan.roepcke at metagen.de> writes: > Hey, > > hopefully there is an easy way to solve my problem. > All that i think off is lengthy and clumsy. > > Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. > Let FAC1 be something like experiment number, > so that there are exactly the same number of rows for each level of FAC1 > in the

Does anyone know which package include the computation of Cochran’s Q statistic in R? jlfmssm [[alternative HTML version deleted]]

Full_Name: Klaus Hermann Version: 1.5.0 OS: SUNRAY - Unix Submission from: (NULL) (213.61.59.254) if we produce a numeric vector of length 0 and want compute the sum over its elements the sum function returns 0 - this is obviously misleading. Better would be to return NA and give a corresponding warning. There should be a unified strategy to handle vectors of length 0. For example the functions

Hey, hopefully there is an easy way to solve my problem. All that i think off is lengthy and clumsy. Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. Let FAC1 be something like experiment number, so that there are exactly the same number of rows for each level of FAC1 in the data.frame. Now i would like to scale all values according to the center of its experiment. So i can apply s

...Martin Lauss http://crantastic.org/packages/rocc Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a metagene. The samples are ranked by their metagene value and the metagene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its metagene value relative to the threshold. In the first place, the package is aimed at two class probl...

Thank you for reading. I am trying to get sphericity values, and I understood I need to use mlm, but how do I implement a nested within subject design in mlm? I already read the R newsletter, fox chapter appendix, EZanova, and whatever I could find online. My original ANOVA anova(aov(resp ~ sucrose*citral, random =~1 | subject, data = p12bl, subset = exps==1)) Or anova(aov(resp ~