Count number of parameters in a model
Returns the number of parameters (coefficients) of a model.
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", ...)x |
A statistical model. |
... |
Arguments passed to or from other methods. |
remove_nonestimable |
Logical, if |
effects |
Should number of parameters for fixed effects, random effects or both 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. |
The number of parameters in the model.
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").
data(iris) model <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris) n_parameters(model)
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