p-values for Mixed Models
This function attempts to return, or compute, p-values of mixed models.
## S3 method for class 'cpglmm'
p_value(model, method = "wald", ...)
## S3 method for class 'glmmTMB'
p_value(
model,
component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
verbose = TRUE,
...
)
## S3 method for class 'lmerMod'
p_value(model, method = "wald", ...)
## S3 method for class 'merMod'
p_value(model, method = "wald", ...)
## S3 method for class 'MixMod'
p_value(
model,
component = c("all", "conditional", "zi", "zero_inflated"),
verbose = TRUE,
...
)
## S3 method for class 'mixor'
p_value(model, effects = "all", ...)model |
A statistical model. |
method |
For mixed models, can be |
... |
Arguments passed down to |
component |
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. |
verbose |
Toggle warnings and messages. |
effects |
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. |
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_satterthwaite.
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.
p_value_robust() resp. 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.
if (require("lme4")) {
data(iris)
model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
p_value(model)
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