p-values
This function attempts to return, or compute, p-values of a model's parameters. See the documentation for your object's class:
Mixed models (lme4, nlme, glmmTMB, ...)
Bayesian models (rstanarm, brms, MCMCglmm, ...)
Zero-inflated models (hurdle
, zeroinfl
, zerocount
, ...)
Marginal effects models (mfx)
Models with special components (DirichletRegModel
, clm2
, cgam
, ...)
p_value(model, ...) ## Default S3 method: p_value(model, method = NULL, verbose = TRUE, ...) ## S3 method for class 'emmGrid' p_value(model, ci = 0.95, adjust = "none", ...)
model |
A statistical model. |
... |
Arguments passed down to |
method |
If |
verbose |
Toggle warnings and messages. |
ci |
Confidence Interval (CI) level. Default to 0.95 (95%). |
adjust |
Character value naming the method used to adjust p-values or confidence intervals. See |
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.
data(iris) model <- lm(Petal.Length ~ Sepal.Length + Species, data = iris) p_value(model)
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