These are all good questions. I'm taking the liberty of forwarding it to r-sig-mixed-models, as that's a public venue where multiple people can ask questions, answers are publicly viewable, and answers are archived. Short answers: drop1() (my preference) or the anova method from the 'car' package are good for testing whole factors (you may also want to see the ?pvalues help page in recent versions of the lme4 package). For translation to probabilities, you may want predict(...,type="response"). You may also find the effects package useful. http://glmm.wikidot.com/faq can also be helpful; if you have particular suggestions for material to go there, you can add it yourself or suggest it on the r-sig-mixed-models list and someone (maybe me) might add it ... good luck Ben Bolker
On 14-03-14 11:18 AM, Brad White wrote:
Dr. Bolker, Hi, I imagine you receive quite a few questions regarding modeling applications, but I have searched online to no avail. I found your paper in Trends and Ecology and Evolution from 2010 very useful, but it didn?t answer all my questions. I?m a researcher in cattle disease and we have worked in the area of evaluating disease risk among groups (count data) as well as monitoring behavioral changes (again, count data). In the past, we have done events/trials modeling with GLMM to account for random effects of repeated measures on individual animal as well as housing (calves housed within pens and sometimes pens within operations). For a little background (probably more than you want or need), I attached one of our articles describing behavior and the figures represent the type of information I?d like to glean from these models. I have previously used SAS GLIMMIX to model our outcomes, but would really like to model these with R as I think this would be helpful for our graduate students (and me) in the future. Our projects are set up to test specific hypotheses, thus generating estimates and inferences are important to me. I?ve tried several methods in R, but can?t seem to figure out the best way to evaluate model output (ideally I?d like to have a test for the entire factor, not just the tests on individual parameters of the factor). Also, I typically transform the estimates from these models to probabilities, but am having trouble from this part also. I would much appreciate some direction on places to find further resources on this, it appears from my online searching that this is not the common way R output has been used. As a specific example, below are models created in glmer and glmmpql where success is the event happening (count of seconds) and failure is the total number of seconds ? success. Var1 is our treatment of interest (3 levels) and study day is our time factor of interest (again three levels). When running these models, I get the estimate for Var1Hi, Var1Lo, but not for Var1CO as it is in the intercept. I would like to have a test for the overall effect of Var1 to test our hypothesis that Var1 impacts outcome: if so, then I would test individual treatments. Same is true for study day. Then I?d also like to create a table with the estimates (and SE) transformed to probabilities for the multiple variables. Any help you can provide is much appreciated as I?ve struggled with this a while. Thanks, Brad Glmer model: model <- glmer(cbind(success, failure) ~ var1 + studyday + (1 | pen) + (1 | calfid), family = binomial(link = "logit"), data = mydata) summary(model) Generalized linear mixed model fit by maximum likelihood ['glmerMod'] Family: binomial ( logit ) Formula: cbind(success, failure) ~ var1 + studyday + (1 | pen) + (1 | calfid) Data: mydata AIC BIC logLik deviance 394776.0 394794.6 -197381.0 394762.0 Random effects: Groups Name Variance Std.Dev. calfid (Intercept) 4.352e+00 2.086e+00 pen (Intercept) 3.237e-10 1.799e-05 Number of obs: 105, groups: calfid, 35; pen, 3 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.315422 0.604552 -2.18 0.0296 * var1Hi 1.395121 0.853311 1.63 0.1021 var1Lo 1.256089 0.872415 1.44 0.1499 studyday1 -0.618371 0.001991 -310.62 <2e-16 *** studyday2 -0.484639 0.001981 -244.64 <2e-16 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 Correlation of Fixed Effects: (Intr) var1Hi var1Lo stdyd1 var1Hi -0.708 var1Lo -0.693 0.491 studyday1 -0.002 0.000 0.000 studyday2 -0.002 0.000 0.000 0.589 glmmPQL model model <- glmmPQL(cbind(success, failure) ~ var1 + studyday, random=list(~1|pen, ~1|calfid), family = binomial(link = "logit"), data = mydata) summary of glmmPQL model Linear mixed-effects model fit by maximum likelihood Data: mydata AIC BIC logLik NA NA NA Random effects: Formula: ~1 | pen (Intercept) StdDev: 0.2823529 Formula: ~1 | calfid %in% pen (Intercept) Residual StdDev: 0.0008658502 103.4659 Variance function: Structure: fixed weights Formula: ~invwt Fixed effects: cbind(success, failure) ~ var1 + studyday Value Std.Error DF t-value p-value (Intercept) -0.1944745 0.2510724 68 -0.7745754 0.4413 var1Hi 0.2526331 0.1879374 30 1.3442412 0.1889 var1Lo 0.1101132 0.1942646 30 0.5668208 0.5751 studyday1 -0.5603492 0.1997436 68 -2.8053418 0.0065 studyday2 -0.4560453 0.1985154 68 -2.2972798 0.0247 Correlation: (Intr) var1Hi var1Lo stdyd1 var1Hi -0.381 var1Lo -0.373 0.497 studyday1 -0.460 -0.006 0.001 studyday2 -0.463 -0.007 0.003 0.584 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.553151595 -0.664950798 0.007380211 0.706653488 2.626906196 Number of Observations: 105 Number of Groups: pen calfid %in% pen 3 35 Brad White, DVM, MS Beef Production Medicine Q211 Mosier Hall Manhattan, KS 66506 Phone: 785-532-4243