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

On Mon, Mar 23, 2009 at 1:18 PM, Kingsford Jones wrote: > On Mon, Mar 23, 2009 at 11:26 AM, Ben Domingue 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 of doing t

[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 mix

[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 https://sta

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 n

Re: [R] propensity score adjustment using R

Bunny, lautloscrew.com 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. >

[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 dstrat<-svydesign(id=~

Re: [R] Mimicking SPSS weighted least squares

gy > McMaster University > Hamilton, Ontario, Canada L8S 4M4 > 905-525-9140x23604 > http://socserv.mcmaster.ca/jfox > > > > > > -Original Message- > > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > > project.org] On Behalf Of JRG >

[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 accordingly,

[R] Matrix inversion

ing ginv(). The process 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 P