Count number of parameters in a model

Returns the number of parameters (coefficients) of a model.

Usage

n_parameters(x, ...)

# Default S3 method
n_parameters(x, remove_nonestimable = FALSE, ...)

# S3 method for class 'merMod'
n_parameters(
  x,
  effects = c("fixed", "random"),
  remove_nonestimable = FALSE,
  ...
)

# S3 method for class 'glmmTMB'
n_parameters(
  x,
  effects = c("fixed", "random"),
  component = c("all", "conditional", "zi", "zero_inflated"),
  remove_nonestimable = FALSE,
  ...
)

# S3 method for class 'zeroinfl'
n_parameters(
  x,
  component = c("all", "conditional", "zi", "zero_inflated"),
  remove_nonestimable = FALSE,
  ...
)

# S3 method for class 'gam'
n_parameters(
  x,
  component = c("all", "conditional", "smooth_terms"),
  remove_nonestimable = FALSE,
  ...
)

# S3 method for class 'brmsfit'
n_parameters(x, effects = "all", component = "all", ...)

Arguments

x

A statistical model.

...

Arguments passed to or from other methods.

remove_nonestimable

Logical, if TRUE, removes (i.e. does not count) non-estimable parameters (which may occur for models with rank-deficient model matrix).

effects

Should variables for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed models. May be abbreviated.

component

Should total number of parameters, number parameters for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated.

Value

The number of parameters in the model.

Note

This function returns the number of parameters for the fixed effects by default, as returned by find_parameters(x, effects = "fixed"). It does not include all estimated model parameters, i.e. auxiliary parameters like sigma or dispersion are not counted. To get the number of all estimated parameters, use get_df(x, type = "model").

Examples

data(iris)
model <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris)
n_parameters(model)
#> [1] 6