
Parameters from multinomial or cumulative link models
Source:R/methods_DirichletReg.R
, R/methods_bife.R
, R/methods_brglm2.R
, and 2 more
model_parameters.mlm.Rd
Parameters from multinomial or cumulative link models
Usage
# S3 method for DirichletRegModel
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "precision"),
standardize = NULL,
exponentiate = FALSE,
verbose = TRUE,
...
)
# S3 method for bifeAPEs
model_parameters(model, ...)
# S3 method for bracl
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for mlm
model_parameters(
model,
ci = 0.95,
vcov = NULL,
vcov_args = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
# S3 method for clm2
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "scale"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
Arguments
- model
A model with multinomial or categorical response value.
- ci
Confidence Interval (CI) level. Default to
0.95
(95%
).- 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.
- component
Should all parameters, parameters for the conditional model, for the zero-inflated part of the model, or the dispersion model be returned? Applies to models with zero-inflated and/or dispersion component.
component
may be one of"conditional"
,"zi"
,"zero-inflated"
,"dispersion"
or"all"
(default). May be abbreviated.- 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.- verbose
Toggle warnings and messages.
- ...
Arguments passed to or from other methods. For instance, when
bootstrap = TRUE
, arguments liketype
orparallel
are passed down tobootstrap_model()
, and arguments likeci_method
are passed down tobayestestR::describe_posterior()
.- 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).- 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.
Details
Multinomial or cumulative link models, i.e. models where the
response value (dependent variable) is categorical and has more than two
levels, usually return coefficients for each response level. Hence, the
output from model_parameters()
will split the coefficient tables
by the different levels of the model's response.
See also
insight::standardize_names()
to rename
columns into a consistent, standardized naming scheme.
Examples
library(parameters)
if (require("brglm2", quietly = TRUE)) {
data("stemcell")
model <- bracl(
research ~ as.numeric(religion) + gender,
weights = frequency,
data = stemcell,
type = "ML"
)
model_parameters(model)
}
#> # Response level: definitely
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -------------------------------------------------------------------
#> (Intercept) | -1.25 | 0.26 | [-1.76, -0.73] | -4.76 | < .001
#> religion | 0.44 | 0.10 | [ 0.23, 0.64] | 4.20 | < .001
#> gender [female] | -0.14 | 0.17 | [-0.47, 0.19] | -0.82 | 0.414
#>
#> # Response level: probably
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> ----------------------------------------------------------------
#> (Intercept) | 0.47 | 0.29 | [-0.10, 1.04] | 1.62 | 0.105
#> religion | 0.26 | 0.13 | [ 0.01, 0.51] | 2.01 | 0.044
#> gender [female] | 0.19 | 0.21 | [-0.22, 0.60] | 0.90 | 0.370
#>
#> # Response level: probably not
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> -----------------------------------------------------------------
#> (Intercept) | 0.43 | 0.39 | [-0.33, 1.18] | 1.11 | 0.268
#> religion | 0.01 | 0.17 | [-0.33, 0.35] | 0.07 | 0.945
#> gender [female] | -0.16 | 0.28 | [-0.71, 0.39] | -0.57 | 0.566
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald z-distribution approximation.