Skip to contents

Get the data that was used to fit the model

Source: R/get_data.R
get_data.Rd

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

Usage

get_data(x, ...)

# Default S3 method
get_data(x, source = "environment", verbose = TRUE, ...)

# S3 method for class 'glmmTMB'
get_data(
  x,
  effects = "all",
  component = "all",
  source = "environment",
  verbose = TRUE,
  ...
)

# S3 method for class 'afex_aov'
get_data(x, shape = c("long", "wide"), ...)

# S3 method for class 'rma'
get_data(
  x,
  source = "environment",
  verbose = TRUE,
  include_interval = FALSE,
  transf = NULL,
  transf_args = NULL,
  ci = 0.95,
  ...
)

Arguments

x

A fitted model.

...

Currently not used.

source

String, indicating from where data should be recovered. If source = "environment" (default), data is recovered from the environment (e.g. if the data is in the workspace). This option is usually the fastest way of getting data and ensures that the original variables used for model fitting are returned. Note that always the current data is recovered from the environment. Hence, if the data was modified after model fitting (e.g., variables were recoded or rows filtered), the returned data may no longer equal the model data. If source = "frame" (or "mf"), the data is taken from the model frame. Any transformed variables are back-transformed, if possible. This option returns the data even if it is not available in the environment, however, in certain edge cases back-transforming to the original data may fail. If source = "environment" fails to recover the data, it tries to extract the data from the model frame; if source = "frame" and data cannot be extracted from the model frame, data will be recovered from the environment. Both ways only returns observations that have no missing data in the variables used for model fitting.

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

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, the instrumental variables or marginal effects be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variables (so called fixed-effects regressions), or models with marginal effects (from mfx). See details in section Model Components .May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

  • If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

shape

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

include_interval

For meta-analysis models, should normal-approximation confidence intervals be added for each response effect size?

transf

For meta-analysis models, if intervals are included, a function applied to each response effect size and its interval.

transf_args

For meta-analysis models, an optional list of arguments passed to the transf function.

ci

For meta-analysis models, the Confidence Interval (CI) level if include_interval = TRUE. Default to 0.95 (95%).

Value

The data that was used to fit the model.

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.

  • "nonlinear": for non-linear models (like models of class nlmerMod or nls), returns staring estimates for the nonlinear parameters.

  • "correlation": for models with correlation-component, like gls, the variables used to describe the correlation structure are returned.

  • "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.

Special models

Some model classes also allow rather uncommon options. These are:

  • mhurdle: "infrequent_purchase", "ip", and "auxiliary"

  • BGGM: "correlation" and "intercept"

  • BFBayesFactor, glmx: "extra"

  • averaging:"conditional" and "full"

  • mjoint: "survival"

  • mfx: "precision", "marginal"

  • betareg, DirichletRegModel: "precision"

  • mvord: "thresholds" and "correlation"

  • clm2: "scale"

  • selection: "selection", "outcome", and "auxiliary"

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here. It can be any pre-defined or arbitrary distributional parameter, like mu, ndt, kappa, etc.

Examples

data(cbpp, package = "lme4")
cbpp$trials <- cbpp$size - cbpp$incidence
m <- glm(cbind(incidence, trials) ~ period, data = cbpp, family = binomial)
head(get_data(m))
#>   incidence 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