search for: rmaing

Can anybody explain why RMA has to have a default normalization method: quantile-quantile? Why don't leave the choices to users? If I just want to use RMA to do a background correction without normalization, how should I specify the ? in the normalize.method="?" ? Hairong [[alternative HTML version deleted]]

Hi (R 2.13.1, OSX 10.6.8) I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example

Hi, I have been looking for an R package which allowed to do meta-analysis (both pairwise and network/mixed-treatment) at arm-level rather than at trial-level, the latter being the common way in which meta-analysis is done. By arm-level meta-analysis I mean one that accounts for data provided at the level of the individual arms of each trial and that does not simply derive the difference between

Anyone have any luck RMAing a Sipura phone since the Cisco take over? Sipura only has support via email or fax to end users and I haven't gotten a response to either for over 2 months. Linksys Support will jump you through all their scripted hoops to resolve your problem (they hope if they speak with a thick enough accen...

I think that you misunderstood me. As far as I know, RMA does three things: background correction, quantile normalization, and summary from probes to probesets. I want the probe values after background correction and quantile normalization but before the summary. On Sat, Dec 26, 2009 at 12:07 PM, Benilton Carvalho <bcarvalh at jhsph.edu> wrote: > pm(data) > > b > > On Dec

Hi *, is there a way to obtain some kind of p-value for a model fitted with RMA using the lmodel2 package? I know that p-values are discussed and criticized a lot and as you can image from my question I'm not very much of a statistican (only writing my bachelor thesis). As fare as I understood the confidence interval statistic correctly, a coefficient is regarded as statistically significant

Dear R-experts! I am working on some meta-analysis using the metafor package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML

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 all, I'm currently applying a mixed model approach to meta analysis using the package metafor. I use the "model.matrix()" function to create dummy variables. The option btt gives me the combined test for the dummies. Problem is, I don't know which indices I have to use, and can't really figure it out from the help file and the examples. I use following code : X <-

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 R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

I run R 1.9.0 on windows 2000, and have the following libraries installed: affydata_1.3.1 affy_1.4.23 Biobase_1.4.10 DynDoc_1.3.14 gcrma_1.0.6 hgu133acdf_1.4.3 hgu95av2cdf_1.4.3 hgu95av2probe_1.0 matchprobes_1.0.7 moe430acdf_1.4.3 multcomp_0.4-6 mvtnorm_0.6-6 rae230acdf_1.4.3 reposTools_1.3.29 rgu34acdf_1.4.3 tkWidgets_1.5.1 widgetTools_1.2.7 1. The rma function (in affy library) always crashes.

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

Hello metafor users, I'm using metafor to perform a single-effect summary estimate of the raw proportion of patients experiencing a post-operative complication, and I'm interested in seeing if this proportion differs between the three most commonly used surgical techniques. The software is working as expected, but I would like to double check on the interpretation of my mixed-effect model

All, I am working on a multiple-regression meta-analysis and have too many alternative models to fit by hand. I am using the "metafor" package in R, which generates AIC scores among other metrics. I'm using a simple formula to define these models. For example, rma(Effect_size,variance, mods=~Myco_type + N.type +total, method="ML")->mod where Effect_size is the

Hi, for testing if the slope of experimental data differs from a given slope I'm using the function "test_regression_against_slope" (see below). I am now confronted with the problem that I have data which requires a modelII regression (also called reduced major axes regression (RMA) or geometric mean regression). For this I use the function "modelII" (see below). What

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Hi, I'm regressing various body dimensions upon body mass using the 'lmodel2' function, as I'm keen to obtain both OLS and RMA slope values. I also wish to create a plot of the regressions, with the 95% confidence interval of both the slope and intercept. I know how to plot 95% ci bands of the OLS slope using lm with the 'predict' function and 'matlines'. Does

Hello, I am using function lmodel2 to calculate RMA regression between life-history traits in ladybirds beetles. It works well but I am not able to plot an RMA regression line on the scatterplot of my data. I am of course unable to plot the confidence intervals. For ordinary least square regression abline(lm(x~y))works well but for RMA regression, abline(lmodel2(x~y))does not do it. If somebody

Hi, i was looking into the documentation for the rma() function in affy() package, and was trying to figure out how exactly the background normalization is done. I read all three papers cited in the rma() documentation, but the most detailed explanation i could find was in Irizary et al., 2003, where they state that they compute B(PM_{ijn}) = E[s_{ijn} | PM_{ijn}] where s_{ijn} is assumed to