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 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 zcpglm get_data(x, component = c("all", "conditional", "zi", "zero_inflated"), ...) # 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