Extract and compute indices and measures to describe parameters of generalized additive models (GAM(M)s).
# S3 method for cgam
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)# S3 method for gam
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)
# S3 method for rqss
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
parameters = keep,
verbose = TRUE,
...
)
A gam/gamm model.
Confidence Interval (CI) level. Default to 0.95 (95%).
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 in
model_parameters() for further details. When ci_method=NULL, in most
cases "wald" is used then.
Should estimates be based on bootstrapped model? If
TRUE, then arguments of Bayesian regressions apply (see also
bootstrap_parameters()).
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
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' in effectsize::standardize_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 use standardize="basic" or standardize the data
with datawizard::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 than NULL) of standardized parameters only
works when standardize="refit".
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.
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).
Character containing a regular expression pattern that
describes the parameters that should be included (for keep) or excluded
(for drop) 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. If keep is a character vector, every parameter
name in the "Parameter" column that matches the regular expression in
keep 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.
See keep.
Deprecated, alias for keep.
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().
A data frame of indices related to the model's parameters.
The reporting of degrees of freedom for the spline terms
slightly differs from the output of summary(model), for example in the
case of mgcv::gam(). The estimated degrees of freedom, column
edf in the summary-output, is named df in the returned data
frame, while the column df_error in the returned data frame refers to
the residual degrees of freedom that are returned by df.residual().
Hence, the values in the the column df_error differ from the column
Ref.df from the summary, which is intentional, as these reference
degrees of freedom “is not very interpretable”
(web).
insight::standardize_names() to rename
columns into a consistent, standardized naming scheme.
# NOT RUN {
library(parameters)
if (require("mgcv")) {
dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
model <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
model_parameters(model)
}
# }
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