model_parameters.cgam  R Documentation 
Extract and compute indices and measures to describe parameters of generalized additive models (GAM(M)s).
## S3 method for class '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, verbose = TRUE, ... ) ## S3 method for class '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, verbose = TRUE, ... ) ## S3 method for class 'gamlss' model_parameters( model, ci = 0.95, ci_method = "residual", bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, p_adjust = NULL, keep = NULL, drop = NULL, verbose = TRUE, ... ) ## S3 method for class 'gamm' model_parameters( model, ci = 0.95, bootstrap = FALSE, iterations = 1000, verbose = TRUE, ... ) ## S3 method for class 'Gam' model_parameters( model, omega_squared = NULL, eta_squared = NULL, epsilon_squared = NULL, df_error = NULL, type = NULL, table_wide = FALSE, verbose = TRUE, ... ) ## S3 method for class 'scam' model_parameters( model, ci = 0.95, ci_method = "residual", bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, p_adjust = NULL, keep = NULL, drop = NULL, verbose = TRUE, ... ) ## S3 method for class 'vgam' model_parameters( model, ci = 0.95, ci_method = "residual", bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, p_adjust = NULL, keep = NULL, drop = NULL, verbose = TRUE, ... )
model 
A gam/gamm model. 
ci 
Confidence Interval (CI) level. Default to 
ci_method 
Method for computing degrees of freedom for
confidence intervals (CI) and the related pvalues. Allowed are following
options (which vary depending on the model class): 
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. 
standardize 
The method used for standardizing the parameters. Can be

exponentiate 
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use 
p_adjust 
Character vector, if not 
keep 
Character containing a regular expression pattern that
describes the parameters that should be included (for 
drop 
See 
verbose 
Toggle warnings and messages. 
... 
Arguments passed to or from other methods. For instance, when

omega_squared, eta_squared, epsilon_squared 
Deprecated. Please use 
df_error 
Denominator degrees of freedom (or degrees of freedom of the
error estimate, i.e., the residuals). This is used to compute effect sizes
for ANOVAtables from mixed models. See 'Examples'. (Ignored for

type 
Numeric, type of sums of squares. May be 1, 2 or 3. If 2 or 3,
ANOVAtables using 
table_wide 
Logical that decides whether the ANOVA table should be in
wide format, i.e. should the numerator and denominator degrees of freedom
be in the same row. Default: 
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 summaryoutput, 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).
A data frame of indices related to the model's parameters.
insight::standardize_names()
to rename
columns into a consistent, standardized naming scheme.
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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