`get_data.Rd`

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 hurdle get_data(x, component = c("all", "conditional", "zi", "zero_inflated", "dispersion"), ...) # S3 method for glmmTMB get_data(x, effects = c("all", "fixed", "random"), component = c("all", "conditional", "zi", "zero_inflated", "dispersion"), ...) # S3 method for merMod get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for rlmerMod get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for mixed get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for clmm get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for lme get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for gee get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for rqss get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for MixMod get_data(x, effects = c("all", "fixed", "random"), component = c("all", "conditional", "zi", "zero_inflated", "dispersion"), ...) # S3 method for brmsfit get_data(x, effects = c("all", "fixed", "random"), component = c("all", "conditional", "zi", "zero_inflated"), ...) # S3 method for stanreg get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for MCMCglmm get_data(x, effects = c("all", "fixed", "random"), ...)

x | A fitted model. |
---|---|

... | Currently not used. |

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. |

effects | Should model data for fixed effects, random effects or both be returned? Only applies to mixed models. |

The data that was used to fit the model.

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.

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