Parameters from multinomial or cumulative link models
Parameters from multinomial or cumulative link models
## S3 method for class '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 class 'bracl'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
## S3 method for class 'mlm'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
verbose = TRUE,
...
)
## S3 method for class '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,
say, logistic regressions, or more generally speaking: for models with log
or logit link. Note: standard errors are also transformed (by
multiplying the standard errors with the exponentiated coefficients), to
mimic behaviour of other software packages, such as Stata. For
|
verbose |
Toggle warnings and messages. |
... |
Arguments passed to or from other methods. For instance, when
|
p_adjust |
Character vector, if not |
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.
A data frame of indices related to the model's parameters.
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)
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