This functions tries to get the data that was used to fit the model and returns it as data frame.

## Usage

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"), ...)

## Arguments

x

A fitted model.

...

Currently not used.

verbose

Toggle messages and warnings.

effects

Should model data for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed or gee models.

component

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.

shape

Return long or wide data? Only applicable in repeated measures designs.

## Value

The data that was used to fit the model.

## Note

Unlike model.frame(), which may contain transformed variables (e.g. if poly() or scale() was used inside the formula to specify the model), get_data() aims at returning the "original", untransformed data (if possible). Consequently, column names are changed accordingly, i.e. "log(x)" will become "x" etc. for all data columns with transformed values.

## Model components

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.

## Examples

if (require("lme4")) {
data(cbpp, package = "lme4")
cbpp$trials <- cbpp$size - cbpp\$incidence
m <- glm(cbind(incidence, trials) ~ period, data = cbpp, family = binomial)
}
#>   cbind(incidence, trials).incidence cbind(incidence, trials).trials period
#> 1                                  2                              12      1
#> 2                                  3                               9      2
#> 3                                  4                               5      3
#> 4                                  0                               5      4
#> 5                                  3                              19      1
#> 6                                  1                              17      2
#>   incidence trials
#> 1         2     12
#> 2         3      9
#> 3         4      5
#> 4         0      5
#> 5         3     19
#> 6         1     17