standard_error()
attempts to return standard errors of model parameters, while standard_error_robust()
attempts to return robust standard errors.
standard_error(model, ...)# S3 method for factor
standard_error(model, force = FALSE, verbose = TRUE, ...)
# S3 method for default
standard_error(model, method = NULL, ...)
# S3 method for merMod
standard_error(model, effects = c("fixed", "random"), method = NULL, ...)
# S3 method for glmmTMB
standard_error(
model,
effects = c("fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated"),
...
)
# S3 method for MixMod
standard_error(
model,
effects = c("fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated"),
...
)
# S3 method for zeroinfl
standard_error(
model,
component = c("all", "conditional", "zi", "zero_inflated"),
method = NULL,
...
)
# S3 method for coxph
standard_error(model, method = NULL, ...)
# S3 method for mixor
standard_error(model, effects = c("all", "fixed", "random"), ...)
# S3 method for clm2
standard_error(model, component = c("all", "conditional", "scale"), ...)
# S3 method for betareg
standard_error(model, component = c("all", "conditional", "precision"), ...)
# S3 method for DirichletRegModel
standard_error(model, component = c("all", "conditional", "precision"), ...)
A model.
Arguments passed to or from other methods. For standard_error()
,
if method = "robust"
, arguments vcov_estimation
, vcov_type
and vcov_args
can be passed down to standard_error_robust()
.
Logical, if TRUE
, factors are converted to numerical
values to calculate the standard error, with the lowest level being the
value 1
(unless the factor has numeric levels, which are converted
to the corresponding numeric value). By default, NA
is returned
for factors or character vectors.
Toggle off warnings.
If "robust"
, robust standard errors are computed
by calling standard_error_robust()
.
standard_error_robust()
, in turn, calls one of the vcov*()
-functions
from the sandwich or clubSandwich package for robust covariance
matrix estimators. For certain mixed models, method
may also be one
of "wald"
, "ml1"
, "betwithin"
,
"satterthwaite"
or "kenward"
.
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.
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. component
may be one of "conditional"
,
"zi"
, "zero-inflated"
or "all"
(default). May be abbreviated.
A data frame.
# NOT RUN {
model <- lm(Petal.Length ~ Sepal.Length * Species, data = iris)
standard_error(model)
# }
Run the code above in your browser using DataLab