R/methods_DirichletReg.R
, R/methods_brglm2.R
, R/methods_mlm.R
, and 1 more
model_parameters.mlm.Rd
Parameters from multinomial or cumulative link models
# 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 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,
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,
...
)
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

iterations  The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models. 
component  Model component for which parameters should be shown. May be
one of 
standardize  The method used for standardizing the parameters. Can be

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: Deltamethod 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 
verbose  Toggle warnings and messages. 
...  Arguments passed to or from other methods. For instance, when

p_adjust  Character vector, if not 
A data frame of indices related to the model's parameters.
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.
standardize_names()
to rename
columns into a consistent, standardized naming scheme.
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  LogOdds  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  LogOdds  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  LogOdds  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