Parameters from zero-inflated models (from packages like pscl,
cplm or countreg).
# S3 method for zcpglm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "zi", "zero_inflated"),
standardize = NULL,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)
Arguments
model |
A model with zero-inflation component. |
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 also
bootstrap_parameters() ). |
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 "conditional" , "precision" (betareg),
"scale" (ordinal), "extra" (glmx),
"marginal" (mfx), "conditional" or "full" (for
MuMIn::model.avg() ) or "all" . |
standardize |
The method used for standardizing the parameters. Can be
"refit" , "posthoc" , "smart" , "basic" ,
"pseudo" or NULL (default) for no standardization. See
'Details' in effectsize::standardize_parameters() .
Important: Categorical predictors (i.e. factors) are never
standardized by default, which may be a different behaviour compared to
other R packages or other software packages (like SPSS). If standardizing
categorical predictors is desired, either use standardize="basic"
to mimic behaviour of SPSS or packages such as lm.beta, or standardize
the data with effectsize::standardize(force=TRUE) before fitting
the model. Robust estimation (i.e. robust=TRUE ) of standardized
parameters only works when standardize="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. |
robust |
Logical, if TRUE , robust standard errors are calculated
(if possible), and confidence intervals and p-values are based on these
robust standard errors. Additional arguments like vcov_estimation or
vcov_type are passed down to other methods, see
standard_error_robust() for details
and this vignette
for working examples. |
p_adjust |
Character vector, if not NULL , indicates the method to
adjust p-values. See stats::p.adjust() for details. Further
possible adjustment methods are "tukey" , "scheffe" ,
"sidak" and "none" to explicitly disable adjustment for
emmGrid objects (from emmeans). |
keep |
Character containing a regular expression pattern that
describes the parameters that should be included in the returned data frame
(for keep ), resp. parameters to exclude (drop ). keep may also be a
named list of regular expressions. All non-matching parameters will be
removed from the output. If keep is a character vector, every parameter
name in the "Parameter" column that matches the regular expression in
parameters will be selected from the returned data frame (and vice versa,
all parameter names matching drop will be excluded). Furthermore, if
keep has more than one element, these will be merged with an OR
operator into a regular expression pattern like this: "(one|two|three)" .
If keep 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 where model_parameters()
returns multiple columns with parameter components, like in
model_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 |
Character containing a regular expression pattern that
describes the parameters that should be included in the returned data frame
(for keep ), resp. parameters to exclude (drop ). keep may also be a
named list of regular expressions. All non-matching parameters will be
removed from the output. If keep is a character vector, every parameter
name in the "Parameter" column that matches the regular expression in
parameters will be selected from the returned data frame (and vice versa,
all parameter names matching drop will be excluded). Furthermore, if
keep has more than one element, these will be merged with an OR
operator into a regular expression pattern like this: "(one|two|three)" .
If keep 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 where model_parameters()
returns multiple columns with parameter components, like in
model_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. |
parameters |
Deprecated, alias for keep . |
verbose |
Toggle warnings and messages. |
... |
Arguments passed to or from other methods. For instance, when
bootstrap = TRUE , arguments like type or parallel are
passed down to bootstrap_model() , and arguments like ci_method
are passed down to bayestestR::describe_posterior() . |
Value
A data frame of indices related to the model's parameters.
See also
Examples
library(parameters)
if (require("pscl")) {
data("bioChemists")
model <- zeroinfl(art ~ fem + mar + kid5 + ment | kid5 + phd, data = bioChemists)
model_parameters(model)
}
#> Loading required package: pscl
#> Classes and Methods for R developed in the
#> Political Science Computational Laboratory
#> Department of Political Science
#> Stanford University
#> Simon Jackman
#> hurdle and zeroinfl functions by Achim Zeileis
#> # Fixed Effects
#>
#> Parameter | Log-Mean | SE | 95% CI | z | p
#> ---------------------------------------------------------------------
#> (Intercept) | 0.56 | 0.07 | [ 0.43, 0.69] | 8.26 | < .001
#> fem [Women] | -0.23 | 0.06 | [-0.34, -0.11] | -3.91 | < .001
#> mar [Married] | 0.14 | 0.07 | [ 0.01, 0.27] | 2.07 | 0.038
#> kid5 | -0.17 | 0.05 | [-0.26, -0.07] | -3.43 | < .001
#> ment | 0.02 | 2.12e-03 | [ 0.02, 0.03] | 10.05 | < .001
#>
#> # Zero-Inflated
#>
#> Parameter | Log-Odds | SE | 95% CI | z | p
#> --------------------------------------------------------------
#> (Intercept) | -0.93 | 0.43 | [-1.78, -0.08] | -2.14 | 0.032
#> kid5 | 0.05 | 0.22 | [-0.38, 0.47] | 0.21 | 0.831
#> phd | -0.25 | 0.14 | [-0.51, 0.02] | -1.84 | 0.065