get_data.RdThis 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 gee get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for rqss get_data(x, component = c("all", "conditional", "smooth_terms"), ...) # 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 glmmadmb get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for rlmerMod get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for clmm get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for mixed get_data(x, effects = c("all", "fixed", "random"), ...) # S3 method for lme 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. |
| effects | Should model data for fixed effects, random effects or both be returned? Only applies to mixed 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. |
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