
Parameters from multiply imputed repeated analyses
Source:R/methods_mice.R
model_parameters.mira.Rd
Format models of class mira
, obtained from mice::width.mids()
.
Usage
# 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,
...
)
Arguments
- model
An object of class
mira
.- ci
Confidence Interval (CI) level. Default to
0.95
(95%
).- 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):
"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 inmodel_parameters()
for further details. Whenci_method=NULL
, in most cases"wald"
is used then.- bootstrap
Should estimates be based on bootstrapped model? If
TRUE
, then arguments of Bayesian regressions apply (see alsobootstrap_parameters()
).- 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
NULL
(default; no standardization),"refit"
(for re-fitting the model on standardized data) or one of"basic"
,"posthoc"
,"smart"
,"pseudo"
. See 'Details' instandardize_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 usestandardize="basic"
or standardize the data withdatawizard::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 thanNULL
) of standardized parameters only works whenstandardize="refit"
.
- exponentiate
Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. 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.- p_adjust
Character vector, if not
NULL
, indicates the method to adjust p-values. Seestats::p.adjust()
for details. Further possible adjustment methods are"tukey"
,"scheffe"
,"sidak"
and"none"
to explicitly disable adjustment foremmGrid
objects (from emmeans).- summary
Logical, if
TRUE
, prints summary information about the model (model formula, number of observations, residual standard deviation and more).- keep
Character containing a regular expression pattern that describes the parameters that should be included (for
keep
) or excluded (fordrop
) 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. Ifkeep
is a character vector, every parameter name in the "Parameter" column that matches the regular expression inkeep
will be selected from the returned data frame (and vice versa, all parameter names matchingdrop
will be excluded). Furthermore, ifkeep
has more than one element, these will be merged with anOR
operator into a regular expression pattern like this:"(one|two|three)"
. Ifkeep
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 wheremodel_parameters()
returns multiple columns with parameter components, like inmodel_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.- drop
See
keep
.- parameters
Deprecated, alias for
keep
.- 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.
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"
.
- vcov_args
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.
Details
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.
Examples
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)
}
#>
#> Attaching package: ‘mice’
#> The following object is masked from ‘package:stats’:
#>
#> filter
#> The following objects are masked from ‘package:base’:
#>
#> cbind, rbind
#>
#> iter imp variable
#> 1 1 bmi hyp chl
#> 1 2 bmi hyp chl
#> 1 3 bmi hyp chl
#> 1 4 bmi hyp chl
#> 1 5 bmi hyp chl
#> 2 1 bmi hyp chl
#> 2 2 bmi hyp chl
#> 2 3 bmi hyp chl
#> 2 4 bmi hyp chl
#> 2 5 bmi hyp chl
#> 3 1 bmi hyp chl
#> 3 2 bmi hyp chl
#> 3 3 bmi hyp chl
#> 3 4 bmi hyp chl
#> 3 5 bmi hyp chl
#> 4 1 bmi hyp chl
#> 4 2 bmi hyp chl
#> 4 3 bmi hyp chl
#> 4 4 bmi hyp chl
#> 4 5 bmi hyp chl
#> 5 1 bmi hyp chl
#> 5 2 bmi hyp chl
#> 5 3 bmi hyp chl
#> 5 4 bmi hyp chl
#> 5 5 bmi hyp chl
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | Statistic | df | p
#> ------------------------------------------------------------------------------
#> (Intercept) | 19.07 | 4.55 | [ 8.40, 29.73] | 4.19 | 7.35 | 0.004
#> age40-59 | -4.77 | 2.07 | [ -9.32, -0.22] | -2.31 | 10.95 | 0.041
#> age60-99 | -5.89 | 2.49 | [-11.62, -0.17] | -2.37 | 8.10 | 0.045
#> hypyes | 2.34 | 2.42 | [ -3.23, 7.91] | 0.97 | 8.13 | 0.362
#> chl | 0.05 | 0.03 | [ -0.02, 0.12] | 1.84 | 5.76 | 0.118
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald distribution approximation.
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)
}
#>
#> iter imp variable
#> 1 1 bmi hyp chl
#> 1 2 bmi hyp chl
#> 1 3 bmi hyp chl
#> 1 4 bmi hyp chl
#> 1 5 bmi hyp chl
#> 2 1 bmi hyp chl
#> 2 2 bmi hyp chl
#> 2 3 bmi hyp chl
#> 2 4 bmi hyp chl
#> 2 5 bmi hyp chl
#> 3 1 bmi hyp chl
#> 3 2 bmi hyp chl
#> 3 3 bmi hyp chl
#> 3 4 bmi hyp chl
#> 3 5 bmi hyp chl
#> 4 1 bmi hyp chl
#> 4 2 bmi hyp chl
#> 4 3 bmi hyp chl
#> 4 4 bmi hyp chl
#> 4 5 bmi hyp chl
#> 5 1 bmi hyp chl
#> 5 2 bmi hyp chl
#> 5 3 bmi hyp chl
#> 5 4 bmi hyp chl
#> 5 5 bmi hyp chl
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | Statistic | df | p
#> ------------------------------------------------------------------------------
#> (Intercept) | 19.07 | 4.55 | [ 8.40, 29.73] | 4.19 | 7.35 | 0.004
#> age40-59 | -4.77 | 2.07 | [ -9.32, -0.22] | -2.31 | 10.95 | 0.041
#> age60-99 | -5.89 | 2.49 | [-11.62, -0.17] | -2.37 | 8.10 | 0.045
#> hypyes | 2.34 | 2.42 | [ -3.23, 7.91] | 0.97 | 8.13 | 0.362
#> chl | 0.05 | 0.03 | [ -0.02, 0.12] | 1.84 | 5.76 | 0.118
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald distribution approximation.