Mike Harwood <harwood262 <at> gmail.com> writes: > Is the labeling/naming of levels in the documentation for the > predict.glmmPQL function "backwards"? The documentation states "Level > values increase from outermost to innermost grouping, with level zero > corresponding to the population predictions". Taking the sample in > the documentation: > > fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, > family = binomial, data = bacteria) > > > head(predict(fit, bacteria, level = 0, type="response")) > [1] 0.9680779 0.9680779 0.8587270 0.8587270 0.9344832 0.9344832 > > head(predict(fit, bacteria, level = 1, type="response")) > X01 X01 X01 X01 X02 X02 > 0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782 > > head(predict(fit, bacteria, type="response")) ## population prediction > X01 X01 X01 X01 X02 X02 > 0.9828449 0.9828449 0.9198935 0.9198935 0.9050782 0.9050782 > > The returned values for level=1 and level=<unspecified> match, which > is not what I expected based upon the documentation.
Well, the documentation says: "Defaults to the highest or innermost level of grouping", which is level 1 in this case -- right? > Exponentiating > the intercept coefficients from the fitted regression, the level=0 > values match when the random effect intercept is included Do you mean "is NOT included" here? 0.9680779 (no random effect, below) matches the level=0 prediction above 0.9828449 (include random effect, below) matches the level=1 prediction, which is also the default prediction, above. > > > 1/(1+exp(-3.412014)) ## only the fixed effect > [1] 0.9680779 > > 1/(1+exp(-1*(3.412014+0.63614382))) ## fixed and random effect intercepts > [1] 0.9828449 This all matches my expectations. If your expectations still go in the other direction, could you explain in more detail? By the way, I recommend r-sig-mixed-mod...@r-project.org for mixed model questions in general ... Ben Bolker ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
