R/methods_lme4.R, and 2 more
This function attempts to return, or compute, p-values of mixed models.
# S3 method for cpglmm p_value(model, method = "wald", ...) # S3 method for glmmTMB p_value( model, component = c("all", "conditional", "zi", "zero_inflated", "dispersion"), verbose = TRUE, ... ) # S3 method for lmerMod p_value(model, method = "wald", ...) # S3 method for merMod p_value(model, method = "wald", ...) # S3 method for MixMod p_value( model, component = c("all", "conditional", "zi", "zero_inflated"), verbose = TRUE, ... ) # S3 method for mixor p_value(model, effects = "all", ...)
A statistical model.
For mixed models, can be
Arguments passed down to
Should all parameters, parameters for the conditional model,
or for the zero-inflated part of the model be returned? Applies to models
with zero-inflated component.
Toggle warnings and messages.
Should standard errors for fixed effects or random effects be returned? Only applies to mixed models. May be abbreviated. When standard errors for random effects are requested, for each grouping factor a list of standard errors (per group level) for random intercepts and slopes is returned.
A data frame with at least two columns: the parameter names and the p-values. Depending on the model, may also include columns for model components etc.
By default, p-values are based on Wald-test approximations (see
p_value_wald()). For certain situations, the "m-l-1" rule might be a better approximation. That is, for
method = "ml1",
p_value_ml1() is called. For
lmerMod objects, if
method = "kenward", p-values are based on Kenward-Roger approximations, i.e.
p_value_kenward() is called, and
method = "satterthwaite" calls
p_value(method = "robust")
rely on the sandwich or clubSandwich package (the latter if
vcov_estimation = "CR" for cluster-robust standard errors) and will
thus only work for those models supported by those packages.