Quick and dirty reference grid
This function may make it possible to compute a reference grid for a model object that is otherwise not supported.
qdrg(formula, data, coef, mcmc, vcov, object, df, subset, weights, contrasts, link, qr, ...)
formula |
Formula for the fixed effects |
data |
Dataset containing the variables in the model |
coef |
Fixed-effect regression coefficients (must conform to formula) |
mcmc |
Posterior sample of fixed-effect coefficients |
vcov |
Variance-covariance matrix of the fixed effects |
object |
Optional model object. If provided, it is used to set certain other arguments, if not specified. See Details. |
df |
Error degrees of freedom |
subset |
Subset of |
weights |
Weights used in fitting the model |
contrasts |
List of contrasts specified in fitting the model |
link |
Link function (character or list) used, if a generalized linear model.
(Note: response transformations are auto-detected from |
qr |
QR decomposition of the model matrix; needed only if there are |
... |
Optional arguments passed to |
If object is specified, it is used to try to obtain certain
other arguments, as detailed below. The user should ensure that these defaults
will work. The default values for the arguments are as follows:
formula: Required unless obtainable via formula(object)
data: Required if variables are not in parent.frame() or
obtainable via object$data
coef: coef(object)
mcmc: object$sample
vcov: vcov(object)
df: Set to Inf if not available in object$df.residual
subset: NULL (so that all observations in data are used)
contrasts: NULL (so that getOption("contrasts") is used)
The functions qdrg and emmobj are close cousins, in that
they both produce emmGrid objects. When starting with summary
statistics for an existing grid, emmobj is more useful, while
qdrg is more useful when starting from a fitted model.
An emmGrid object constructed from the arguments
emmobj for an alternative way to construct an emmGrid.
if (require(biglm)) {
# Post hoc analysis of a "biglm" object -- not supported by emmeans
bigmod <- biglm(log(conc) ~ source + factor(percent), data = pigs)
rg2 <- qdrg(object = bigmod, data = pigs)
summary(emmeans(rg2, "source"), type = "response")
}
if(require(coda) && require(lme4)) {
# Use a stored example having a posterior sample
# Model is based on the data in lme4::cbpp
post <- readRDS(system.file("extdata", "cbpplist", package = "emmeans"))$post.beta
rg1 <- qdrg(~ size + period, data = lme4::cbpp, mcmc = post, link = "logit")
summary(rg1, type = "response")
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