Format models of class mira, obtained from mice::width.mids().
# S3 method for mipo
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
vcov = NULL,
vcov_args = NULL,
...
)# S3 method for mira
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
An object of class mira.
Confidence Interval (CI) level. Default to 0.95 (95%).
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. When ci_method=NULL, in most
cases "wald" is used then.
Should estimates be based on bootstrapped model? If
TRUE, then arguments of Bayesian regressions apply (see also
bootstrap_parameters()).
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
The method used for standardizing the parameters. Can be
NULL (default; no standardization), "refit" (for re-fitting the model
on standardized data) or one of "basic", "posthoc", "smart",
"pseudo". See 'Details' in standardize_parameters().
Important:
The "refit" method does not standardized categorical predictors (i.e.
factors), which may be a different behaviour compared to other R packages
(such as lm.beta) or other software packages (like SPSS). to mimic
such behaviours, either use standardize="basic" or standardize the data
with datawizard::standardize(force=TRUE) before fitting the model.
For mixed models, when using methods other than "refit", only the fixed
effects will be returned.
Robust estimation (i.e., vcov set to a value other than NULL) of standardized parameters only
works when standardize="refit".
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use exponentiate = TRUE for models
with log-transformed response values. Note: Delta-method standard
errors are also computed (by multiplying the standard errors by the
transformed coefficients). This is to mimic behaviour of other software
packages, such as Stata, but these standard errors poorly estimate
uncertainty for the transformed coefficient. The transformed confidence
interval more clearly captures this uncertainty. For compare_parameters(),
exponentiate = "nongaussian" will only exponentiate coefficients from
non-Gaussian families.
Character vector, if not NULL, indicates the method to
adjust p-values. See stats::p.adjust() for details. Further
possible adjustment methods are "tukey", "scheffe",
"sidak" and "none" to explicitly disable adjustment for
emmGrid objects (from emmeans).
Logical, if TRUE, prints summary information about the
model (model formula, number of observations, residual standard deviation
and more).
Character containing a regular expression pattern that
describes the parameters that should be included (for keep) or excluded
(for drop) in the returned data frame. keep may also be a
named list of regular expressions. All non-matching parameters will be
removed from the output. If keep is a character vector, every parameter
name in the "Parameter" column that matches the regular expression in
keep will be selected from the returned data frame (and vice versa,
all parameter names matching drop will be excluded). Furthermore, if
keep has more than one element, these will be merged with an OR
operator into a regular expression pattern like this: "(one|two|three)".
If keep is a named list of regular expression patterns, the names of the
list-element should equal the column name where selection should be
applied. This is useful for model objects where model_parameters()
returns multiple columns with parameter components, like in
model_parameters.lavaan(). Note that the regular expression pattern
should match the parameter names as they are stored in the returned data
frame, which can be different from how they are printed. Inspect the
$Parameter column of the parameters table to get the exact parameter
names.
See keep.
Deprecated, alias for keep.
Toggle warnings and messages.
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.
A covariance matrix
A function which returns a covariance matrix (e.g., stats::vcov())
A string which indicates the kind of uncertainty estimates to return.
Heteroskedasticity-consistent: "vcovHC", "HC", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC.
Cluster-robust: "vcovCR", "CR0", "CR1", "CR1p", "CR1S", "CR2", "CR3". See ?clubSandwich::vcovCR.
Bootstrap: "vcovBS", "xy", "residual", "wild", "mammen", "webb". See ?sandwich::vcovBS.
Other sandwich package functions: "vcovHAC", "vcovPC", "vcovCL", "vcovPL".
List of arguments to be passed to the function identified by
the vcov argument. This function is typically supplied by the sandwich
or clubSandwich packages. Please refer to their documentation (e.g.,
?sandwich::vcovHAC) to see the list of available arguments.
Arguments passed to or from other methods.
model_parameters() for objects of class mira works
similar to summary(mice::pool()), i.e. it generates the pooled summary
of multiple imputed repeated regression analyses.
library(parameters)
if (require("mice", quietly = TRUE)) {
data(nhanes2)
imp <- mice(nhanes2)
fit <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
model_parameters(fit)
}
if (FALSE) {
# model_parameters() also works for models that have no "tidy"-method in mice
if (require("mice", quietly = TRUE) && require("gee", quietly = TRUE)) {
data(warpbreaks)
set.seed(1234)
warpbreaks$tension[sample(1:nrow(warpbreaks), size = 10)] <- NA
imp <- mice(warpbreaks)
fit <- with(data = imp, expr = gee(breaks ~ tension, id = wool))
# does not work:
# summary(pool(fit))
model_parameters(fit)
}
}
# and it works with pooled results
if (require("mice")) {
data("nhanes2")
imp <- mice(nhanes2)
fit <- with(data = imp, exp = lm(bmi ~ age + hyp + chl))
pooled <- pool(fit)
model_parameters(pooled)
}
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