Get model parameters from mixed modelsSource:
Returns the coefficients from a model.
# S3 method for glmm get_parameters(x, effects = c("all", "fixed", "random"), ...) # S3 method for coxme get_parameters(x, effects = c("fixed", "random"), ...) # S3 method for merMod get_parameters(x, effects = c("fixed", "random"), ...) # S3 method for glmmTMB get_parameters( x, effects = c("fixed", "random"), component = c("all", "conditional", "zi", "zero_inflated", "dispersion"), ... ) # S3 method for glimML get_parameters(x, effects = c("fixed", "random", "all"), ...)
A fitted model.
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
Currently not used.
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model or the dispersion term? Applies to models with zero-inflated and/or dispersion formula. Note that the conditional component is also called count or mean component, depending on the model. There are three convenient shortcuts:
component = "all"returns all possible parameters. If
component = "location", location parameters such as
zero_inflatedare returned (everything that are fixed or random effects - depending on the
effectsargument - but no auxiliary parameters). For
component = "distributional"(or
"auxiliary"), components like
dispersion(and other auxiliary parameters) are returned.
effects = "fixed", a data frame with two columns: the
parameter names and the related point estimates. If
effects = "random", a list of data frames with the random effects (as returned by
ranef()), unless the random effects have the same simplified
structure as fixed effects (e.g. for models from MCMCglmm).
In most cases when models either return different "effects" (fixed,
random) or "components" (conditional, zero-inflated, ...), the arguments
component can be used.