standard_error()
attempts to return standard errors of model
parameters.
standard_error(model, ...)# S3 method for default
standard_error(
model,
component = "all",
vcov = NULL,
vcov_args = NULL,
verbose = TRUE,
...
)
# S3 method for factor
standard_error(model, force = FALSE, verbose = TRUE, ...)
# S3 method for glmmTMB
standard_error(
model,
effects = "fixed",
component = "all",
verbose = TRUE,
...
)
# S3 method for merMod
standard_error(
model,
effects = "fixed",
method = NULL,
vcov = NULL,
vcov_args = NULL,
...
)
A data frame with at least two columns: the parameter names and the standard errors. Depending on the model, may also include columns for model components etc.
A model.
Arguments passed to or from other methods.
Model component for which standard errors should be shown.
See the documentation for your object's class in model_parameters()
or
p_value()
for further details.
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.
A covariance matrix
A function which returns a covariance matrix (e.g., stats::vcov()
)
A string which indicates the kind of uncertainty estimates to return.
Heteroskedasticity-consistent: "vcovHC"
, "HC"
, "HC0"
, "HC1"
,
"HC2"
, "HC3"
, "HC4"
, "HC4m"
, "HC5"
. See ?sandwich::vcovHC
.
Cluster-robust: "vcovCR"
, "CR0"
, "CR1"
, "CR1p"
, "CR1S"
, "CR2"
,
"CR3"
. See ?clubSandwich::vcovCR
.
Bootstrap: "vcovBS"
, "xy"
, "residual"
, "wild"
, "mammen"
, "webb"
.
See ?sandwich::vcovBS
.
Other sandwich
package functions: "vcovHAC"
, "vcovPC"
, "vcovCL"
, "vcovPL"
.
List of arguments to be passed to the function identified by
the vcov
argument. This function is typically supplied by the sandwich
or clubSandwich packages. Please refer to their documentation (e.g.,
?sandwich::vcovHAC
) to see the list of available arguments.
Toggle warnings and messages.
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.
Should standard errors for fixed effects ("fixed"
), random
effects ("random"
), or both ("all"
) 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.
Method for computing degrees of freedom for
confidence intervals (CI) and the related p-values. Allowed are following
options (which vary depending on the model class): "residual"
,
"normal"
, "likelihood"
, "satterthwaite"
, "kenward"
, "wald"
,
"profile"
, "boot"
, "uniroot"
, "ml1"
, "betwithin"
, "hdi"
,
"quantile"
, "ci"
, "eti"
, "si"
, "bci"
, or "bcai"
. See section
Confidence intervals and approximation of degrees of freedom in
model_parameters()
for further details.
model <- lm(Petal.Length ~ Sepal.Length * Species, data = iris)
standard_error(model)
if (require("sandwich") && require("clubSandwich")) {
standard_error(model, vcov = "HC3")
standard_error(model,
vcov = "vcovCL",
vcov_args = list(cluster = iris$Species)
)
}
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