similar to: Plotting 95% Confidence Intervals around RMA slope

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

Hi, Does anybody know whether it's possible (and sensible) to calculate R^2 (or something approximating R^2) on the results of a major axis Model II regression done by lmodel2? The function lmodel2 provides an R^2 for the OLS regression only (example below), but not for major axis regression. Also, is it possible to extract major axis regression residuals? (Resid did not work on the lmodel2

Hi, Does anybody know whether it's possible (and sensible) to calculate R^2 (or something approximating R^2) on the results of a major axis Model II regression done by lmodel2? The function lmodel2 provides an R^2 for the OLS regression only (example below) Also, is it possible to extract major axis regression residuals? Many thanks, Karen Example: > Result <- lmodel2(

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, 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

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

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

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]]

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,

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

Forwarded to: ismtp[samba@samba.org] cc: Comments by: Charles J. Huenke@Admin@SAC Comments: Please offer any input you may have on the issue described below. Thank you. Chuck Huenke Delaware Statistical Analysis Center (302) 739-3680 -------------------------- [Original Message] ------------------------- We received a replacement EFG20 on the referenced

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,

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 accent and make repeat the same steps over and over again that you will just

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.

Hi, I am a student using R for my final year project, with the metafor package being particularly helpful. I have been following the steps laid out in the manual 'Conducting Meta-analysis in R with the Metafor package' (Viechtbauer, 2010) and applying it to my own data of infected Anopheles mosquitoes across Africa. In particular, I am attempting to apply a mixed-effect model to my data

Hi, I am a Ph.D. student from Québec, Canada. I’m a beginner with R and Bioconductor. Until now the only experience I have is in analyzing microarray data using affy and limma packages. Now I am trying to analyze Rat Gene 10 st arrays and I would like to run RMA analysis and Smyth moderated t test on those arrays. Since no cdf official package is available for those arrays, after reading many

I have a raidz2 pool with one disk that seems to be going bad, several errors are noted in iostat. I have an RMA for the drive, however - no I am wondering how I proceed. I need to send the drive in and then they will send me one back. If I had the drive on hand, I could do a zpool replace. Do I do a zpool offline? zpool detach? Once I get the drive back and put it in the same drive bay..

The Blue Book allows the 'type' argument to be used in matpoints and matlines. matlines(x, y, type="l", lty=1:5, pch=, col=1:4) R-1.0.0 does not. Thus, type="h", "b", must be invoked thru matplot( x, y, type = "h", add=TRUE) For the sake of consistency with S, it would be nice to have matlines defined as: "matlines" <-

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