model_parameters.mipo {parameters} | R Documentation |
Format models of class mira
, obtained from mice::width.mids()
.
## S3 method for class '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, verbose = TRUE, vcov = NULL, vcov_args = NULL, ... ) ## S3 method for class 'mira' model_parameters( model, ci = 0.95, exponentiate = FALSE, p_adjust = NULL, keep = NULL, drop = NULL, verbose = TRUE, ... )
model |
An object of class |
ci |
Confidence Interval (CI) level. Default to |
ci_method |
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): |
bootstrap |
Should estimates be based on bootstrapped model? If
|
iterations |
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models. |
standardize |
The method used for standardizing the parameters. Can be
|
exponentiate |
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 |
p_adjust |
Character vector, if not |
summary |
Logical, if |
keep |
Character containing a regular expression pattern that
describes the parameters that should be included (for |
drop |
See |
verbose |
Toggle warnings and messages. |
vcov |
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.
|
vcov_args |
List of arguments to be passed to the function identified by
the |
... |
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) } ## Not run: # 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) } ## End(Not run) # 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) }