A *conditional response* includes
contributions from both fixed- and random-effects predictors. A *marginal
response* includes contribution from only fixed effects.

Suppose the generalized linear mixed-effects model `glme`

has
an *n*-by-*p* fixed-effects design
matrix `X`

and an *n*-by-*q* random-effects
design matrix `Z`

. Also, suppose the estimated *p*-by-1
fixed-effects vector is $$\widehat{\beta}$$,
and the *q*-by-1 empirical Bayes predictor vector
of random effects is $$\widehat{b}$$.

The fitted conditional response corresponds to the `'Conditional',true`

name-value
pair argument, and is defined as

where $${\widehat{\eta}}_{ME}$$ is
the linear predictor including the fixed- and random-effects of the
generalized linear mixed-effects model

The fitted marginal response corresponds to the `'Conditional',false`

name-value
pair argument, and is defined as

where$${\widehat{\eta}}_{FE}$$ is
the linear predictor including only the fixed-effects portion of the
generalized linear mixed-effects model