This functions tries to get the data that was used to fit the model and returns it as data frame.
get_data(x, ...)# S3 method for default
get_data(x, verbose = TRUE, ...)
# S3 method for glmmTMB
get_data(x, effects = "all", component = "all", verbose = TRUE, ...)
# S3 method for afex_aov
get_data(x, shape = c("long", "wide"), ...)
The data that was used to fit the model.
A fitted model.
Currently not used.
Toggle messages and warnings.
Should model data for fixed effects ("fixed"
), random
effects ("random"
) or both ("all"
) be returned? Only applies to mixed
or gee models.
Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model.
Return long or wide data? Only applicable in repeated measures designs.
Possible values for the component
argument depend on the model class.
Following are valid options:
"all"
: returns all model components, applies to all models, but will only
have an effect for models with more than just the conditional model component.
"conditional"
: only returns the conditional component, i.e. "fixed effects"
terms from the model. Will only have an effect for models with more than
just the conditional model component.
"smooth_terms"
: returns smooth terms, only applies to GAMs (or similar
models that may contain smooth terms).
"zero_inflated"
(or "zi"
): returns the zero-inflation component.
"dispersion"
: returns the dispersion model component. This is common
for models with zero-inflation or that can model the dispersion parameter.
"instruments"
: for instrumental-variable or some fixed effects regression,
returns the instruments.
"location"
: returns location parameters such as conditional
,
zero_inflated
, smooth_terms
, or instruments
(everything that are
fixed or random effects - depending on the effects
argument - but no
auxiliary parameters).
"distributional"
(or "auxiliary"
): components like sigma
, dispersion
,
beta
or precision
(and other auxiliary parameters) are returned.
if (require("lme4")) {
data(cbpp, package = "lme4")
cbpp$trials <- cbpp$size - cbpp$incidence
m <- glm(cbind(incidence, trials) ~ period, data = cbpp, family = binomial)
head(get_data(m))
}
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