similar to: MA / RMA / Type II regression?

First, I am really liking what I have seen so far in R. The demos are quite fascinating, and I look forward to doing some really neat stuff over the next little while with R. Being but an intiate to R, I am having quite a bit of trouble figuring out how to use the leaps library. Rather than ask several questions to the list at large, I would rather explain what I'm trying to do to someone in

Hi there, This problem has been dogging me for a bit, and I'm trying to figure out why. When running the the subsets function in the leaps library, R is giving me the following error message > lvodsub <- subsets(pred, resp$LVOD) Warning message: XHAUST returned error code -999 in: leaps.exhaustive(a, really.big = really.big) but this still happens if I add the really.big option:

Hi there, I realize that this is a bit of a strange question, but here goes. In SAS, one can use the REG procedure to carry out a least-squares regression analysis. By specifying the R-SQUARE option in the SELECTION command, the program carries out regressions for every combination of every independant variable against the dependant variable. This is useful in smaller datasets, though difficult

> Date: Wed, 09 Feb 2000 10:11:20 -0500 > From: "Pete St. Onge" <pete at seul.org> > Subject: Comparing Type 2 regressions Was: [R] Ancova in R? > > For starters, thanks to everyone who responsded to my previous request for > help about Type 2 regression. > > > > I see the R-sm has the ancova thing happening, but I kind of doubt that > > >

I have to admit that I'm at a bit of a loss here; any pointers would be greatly appreciated. I've been making correlation matricies from some of my datasets, and have been instructed to get the probability values for each of these correlations. I've checked the online help for info on both the cor and cov functions, but I was unable to find any relevant info on finding how to obtain

This is obviously not a long term acceptable solution. Could someone please enlighten me as to the permanent solution, or at least see why it doesn't work? This is not my problem, therefore I have not had the opportuntity to test - I don't have a version that old. Let me know if it is solved in a newer version as well. Thanks. It is a workable solution, although the generally accepted

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]]

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

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

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

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'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

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

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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, 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

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,

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,