[R-sig-ME] Characterizing correlation between binomial effects

[Viechtbauer Wolfgang (STAT)]

I meant to respond to this earlier, but now finally got around to it. The nice thing is, there is (for once) an easy answer. For testing covariance parameters, the value under the null is obviously not on the boundary of the parameter space and the concerns with testing parameters on the boundary do not apply. You can actually test this out with some data and the simulate.lme() function (see also chapter 2 in Pinheiro & Bates, 2000). For example:

library(nlme)

res1 <- lme(distance ~ age, random = ~ age | Subject, data = Orthodont)
res2 <- lme(distance ~ age, random = list(Subject = pdDiag(~ age)), data = Orthodont)
res3 <- lme(distance ~ age, random = ~ 1 | Subject, data = Orthodont)

anova(res1, res2, res3)

res.sim13 <- simulate.lme(res3, res1, nsim=1000, seed=18271) ### parameter tested on the boundary
res.sim12 <- simulate.lme(res2, res1, nsim=1000, seed=18271) ### parameter tested not on boundary

plot(res.sim13, df = c(1,2), panel = function(...) {panel.xyplot(...); panel.abline(a=0,b=1, lty="dotted")})
plot(res.sim12, df = c(1,2), panel = function(...) {panel.xyplot(...); panel.abline(a=0,b=1, lty="dotted")})

Note that the 50:50 mixture of chi^2_1 and chi^2_2 seems adequate in the first case. However, in the second case, chi^2_1 is closer to giving you a nominal test (actually it's a bit conservative, but better than the 50:50 mixture).

Best,

Wolfgang

--   
Wolfgang Viechtbauer, Ph.D., Statistician   
Department of Psychiatry and Psychology   
School for Mental Health and Neuroscience   
Faculty of Health, Medicine, and Life Sciences   
Maastricht University, P.O. Box 616 (VIJV1)   
6200 MD Maastricht, The Netherlands   
+31 (43) 388-4170 | http://www.wvbauer.com   


> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-
> bounces at r-project.org] On Behalf Of Jake Westfall
> Sent: Thursday, September 06, 2012 17:57
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Characterizing correlation between binomial
> effects
> 
> This brings up a related question for me. Some of the issues that arise in
> testing the significance of random variances are due to the null value
> being on the boundary of the parameter space (i.e., testing for variance =
> 0). However, since COvariances can be positive or negative, this seems to
> imply that these issues would not apply to the case of testing null
> hypotheses about random covariances, such as covariance = 0, as Mike is
> doing in his and Reinhold's solution. Is this correct?
> Jake