[R] Extracting SD of random effects from lme object

Hello, How do I get the standard deviations for the random effects out of the lme object? I feel like there's probably a simple way of doing this, but I can't see it. Using the first example from the documentation: fm1 - lme(distance ~ age, data = Orthodont) # random is ~ age fm1 Linear

Re: [R] Extracting SD of random effects from lme object

On Mon, Mar 23, 2009 at 1:18 PM, Kingsford Jones kingsfordjo...@gmail.com wrote: On Mon, Mar 23, 2009 at 11:26 AM, Ben Domingue ben.domin...@gmail.com wrote: Hello, How do I get the standard deviations for the random effects out of the lme object?  I feel like there's probably a simple way

[R] Ng-Perron Tests for Unit Roots

Hello, I've searched all the standard spots, and I can't find any implementation of the Ng-Perron test for unit roots. I am aware of the PP tests in urca. Anybody know of something I missed? Thanks, Ben __ R-help@r-project.org mailing list

Re: [R] propensity score adjustment using R

Bunny, lautloscrew.com bunny at lautloscrew.com writes: ix of some covariates. I wonder right now if te glm respectively summary(glm(...)) puts out something comparable to ML estimates that can be used as the estimated pscores, in such a way that there is one value for every observation.

Re: [R] propensity score adjustment using R

I'm not quite sure what you mean. If all you need is propensity scores to run an IPW analysis, the fitted values should work. Having many binary covariates shouldn't be a problem, the whole point of the propensity score is boiling down many dimensions to a single one. I use matchit() for my psm

[R] Warning when using survey:::svyglm

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning (non-integer #successes in a binomial glm!) when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample

Re: [R] Mimicking SPSS weighted least squares

://socserv.mcmaster.ca/jfox -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] project.org] On Behalf Of JRG Sent: March-10-08 10:27 PM To: Rolf Turner; r-help@r-project.org; Ben Domingue Cc: r-help@r-project.org Subject: Re: [R] Mimicking SPSS weighted least

[R] Mimicking SPSS weighted least squares

Howdy, In SPSS, there are 2 ways to weight a least squares regression: 1. You can do it from the regression menu. 2. You can set a global weight switch from the data menu. These two options have no, in my experience, been equivalent. Now, when I run lm in R with the weights= switch set

[R] Matrix inversion

works, but I end up with a different set of regression coefficients after I finish the process than what I had with lm(). To the best of my knowledge, this shouldn't happen. I've been digging around all day and can't figure this out. Thanks, Ben Domingue PhD Student, School of Education