A clarification on my response below: I failed to note that I had defined the factor Info1 as
dat$Info1 <- factor(ifelse(dat$Info %in% "accessible","accessible","givenOrNew")) Best to all, Roger > The next question, of course, is how to deal with the dataset you > actually have. If you weren't using a mixed-effects model, you could > use a likelihood-ratio test by comparing your full model with a simpler > model; the likelihood-ratio test isn't susceptible to problems with > large parameter estimates the way the Wald test is. For example: > >> deacc.glm = glm (Deacc ~ Info, data = dat, family = "binomial") >> deacc.glm1 = glm (Deacc ~ Info1, data = dat, family = "binomial") >> anova(deacc.glm,deacc.glm1,test="Chisq") > Analysis of Deviance Table > > Model 1: Deacc ~ Info > Model 2: Deacc ~ Info1 > Resid. Df Resid. Dev Df Deviance P(>|Chi|) > 1 97 52.452 > 2 98 71.600 -1 -19.148 1.210e-05 > > In principle, you could do this with your mixed-effects model, being > sure to use ML instead of REML fitting: > >> deacc.lmer = lmer (Deacc ~ Info + (1 | Subject), data = dat, family = > "binomial",REML=F) >> deacc.lmer1 = lmer (Deacc ~ Info1 + (1 | Subject), data = dat, family > = "binomial",REML=F) >> anova(deacc.lmer,deacc.lmer1) > Data: dat > Models: > deacc.lmer1: Deacc ~ Info1 + (1 | Subject) > deacc.lmer: Deacc ~ Info + (1 | Subject) > Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) > deacc.lmer1 3 77.600 85.416 -35.800 > deacc.lmer 4 60.400 70.821 -26.200 19.2 1 1.177e-05 *** > > There is an argument that the likelihood-ratio test is anti-conservative > and hence inappropriate for comparing mixed-effects models differing > only in fixed-effects structure. (See Pinheiro & Bates, 2000, around > page 76 or so. The argument is made only for linear mixed-effects > models and I'm not sure of the status for logit mixed-effects models.) > That being said, it's not clear how strong the anti-conservativity is, > and in your case it seems like you have such an exceedingly powerful > effect that you might be safe in using the likelihood-ratio test here > and just mentioning the potential anti-conservativity as a caveat. > > So the summary is: believe it or not, how to assess the significance of > the parameter estimate for "new" for your model & dataset is a bit of an > open question, but it seems pretty clear that the estimate is > significantly non-zero. > > Best & hope this helps. > > Roger > > > -- Roger Levy Email: rl...@ling.ucsd.edu Assistant Professor Phone: 858-534-7219 Department of Linguistics Fax: 858-534-4789 UC San Diego Web: http://ling.ucsd.edu/~rlevy _______________________________________________ R-lang mailing list R-lang@ling.ucsd.edu http://pidgin.ucsd.edu/mailman/listinfo/r-lang
