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Count number of parameters in a model

Source: R/n_parameters.R
n_parameters.Rd

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 = "fixed", remove_nonestimable = FALSE, ...)

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

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

Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, the instrumental variables or marginal effects be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variables (so called fixed-effects regressions), or models with marginal effects (from mfx). See details in section Model Components .May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):

  • component = "all" returns all possible parameters.

  • If component = "location", location parameters such as conditional, zero_inflated, smooth_terms, or instruments are returned (everything that are fixed or random effects - depending on the effects argument - but no auxiliary parameters).

  • For component = "distributional" (or "auxiliary"), components like sigma, dispersion, beta or precision (and other auxiliary parameters) are returned.

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