parameters (version 0.4.0)

model_parameters.gam: Parameters of Generalized Additive (Mixed) Models

Description

Extract and compute indices and measures to describe parameters of generalized additive models (GAM(M)s).

Usage

# S3 method for gam
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  standardize = NULL,
  exponentiate = FALSE,
  ...
)
# S3 method for rqss
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "smooth_terms", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  ...
)
# S3 method for cgam
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  component = c("conditional", "smooth_terms", "all"),
  standardize = NULL,
  exponentiate = FALSE,
  ...
)

Arguments

model

A gam/gamm model.

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 parameters_bootstrap()).

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 "refit", "posthoc", "smart", "basic" or NULL (default) for no standardization. See 'Details' in standardize_parameters.

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.

...

Arguments passed to or from other methods.

component

Model component for which parameters should be shown. May be one of "conditional", "precision" (betareg), "scale" (ordinal), "extra" (glmx) or "all".

Value

A data frame of indices related to the model's parameters.

See Also

standardize_names() to rename columns into a consistent, standardized naming scheme.

Examples

# NOT RUN {
library(parameters)
library(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)
# }