GLMMadaptive | GLMMs with adaptive Gaussian quadrature | Development Tools library

The function emmeans::qdrg() can sometimes be used to create the needed object for a model not directly supported by emmeans. See its documentation. In very simple models (e.g., inheriting from lm, it may be enough to supply the object and data arguments.

That usually does not work for more sophisticated models, in which case you will need to specify data, the fixed-effects formula for the conditional or zero part of the model, and the associated regression coefficients (coef) and variance-covariance matrix (vcov) for the part of the model in question. Often with models like this with multiple components, you likely will have to pick a subset of the coefficients and covariance matrix. These all must conform: the length of coef must equal the number of rows and columns of vcov and the number of columns in the model matrix generated by formula [which may be checked via model.matrix(formula, data = data)].

qdrg() will not work for a multivariate model -- or at least it's tricky -- because the implied model involves other factor(s) that delineate the levels of the multivariate response. If there are special provisions for, say, spline smoothing, that is another instance where qdrg() probably can't be made to work.

Once qdrg() actually runs and produces results, it is a good idea to use it to estimate some contrasts that are estimated by the model parameterization. For example, suppose that the model was fitted with the default contr.treatment contrasts. Then the regression coefficients are interpretable as a comparison with the first level as a reference level. Accordingly, if we computedrg <- qdrg(...), and one of the factors is "treat", look at contrast(rg, "trt.vs.ctrl1", simple = "treat"), and check to see if the first set of estimated contrasts matches the main-effect estimates for treat.

I will illustrate all of this with a simple lm model, ignoring the fact that it is already supported by emmeans.