Find names of model parameters

Returns the names of model parameters, like they typically appear in the summary() output. For Bayesian models, the parameter names equal the column names of the posterior samples after coercion from as.data.frame(). See the documentation for your object's class:

• Bayesian models (rstanarm, brms, MCMCglmm, ...)

• Generalized additive models (mgcv, VGAM, ...)

• Marginal effects models (mfx)

• Estimated marginal means (emmeans)

• Mixed models (lme4, glmmTMB, GLMMadaptive, ...)

• Zero-inflated and hurdle models (pscl, ...)

• Models with special components (betareg, MuMIn, ...)

Usage

find_parameters(x, ...)

# Default S3 method
find_parameters(x, flatten = FALSE, verbose = TRUE, ...)

Arguments

x

A fitted model.

...

Currently not used.

flatten

Logical, if TRUE, the values are returned as character vector, not as list. Duplicated values are removed.

verbose

Toggle messages and warnings.

Value

A list of parameter names. For simple models, only one list-element, conditional, is returned.

Model components

Possible values for the component argument depend on the model class. Following are valid options:

• "all": returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component.

• "conditional": only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component.

• "smooth_terms": returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms).

• "zero_inflated" (or "zi"): returns the zero-inflation component.

• "dispersion": returns the dispersion model component. This is common for models with zero-inflation or that can model the dispersion parameter.

• "instruments": for instrumental-variable or some fixed effects regression, returns the instruments.

• "nonlinear": for non-linear models (like models of class nlmerMod or nls), returns staring estimates for the nonlinear parameters.

• "correlation": for models with correlation-component, like gls, the variables used to describe the correlation structure are returned.

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

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

Special models

Some model classes also allow rather uncommon options. These are:

• mhurdle: "infrequent_purchase", "ip", and "auxiliary"

• BGGM: "correlation" and "intercept"

• BFBayesFactor, glmx: "extra"

• averaging:"conditional" and "full"

• mjoint: "survival"

• mfx: "precision", "marginal"

• betareg, DirichletRegModel: "precision"

• mvord: "thresholds" and "correlation"

• clm2: "scale"

• selection: "selection", "outcome", and "auxiliary"

For models of class brmsfit (package brms), even more options are possible for the component argument, which are not all documented in detail here.

Parameters, Variables, Predictors and Terms

There are four functions that return information about the variables in a model: find_predictors(), find_variables(), find_terms() and find_parameters(). There are some differences between those functions, which are explained using following model. Note that some, but not all of those functions return information about the dependent and independent variables. In this example, we only show the differences for the independent variables.

model <- lm(mpg ~ factor(gear), data = mtcars)

• find_terms(model) returns the model terms, i.e. how the variables were used in the model, e.g. applying transformations like factor(), poly() etc. find_terms() may return a variable name multiple times in case of multiple transformations. The return value would be "factor(gear)".

• find_parameters(model) returns the names of the model parameters (coefficients). The return value would be "(Intercept)", "factor(gear)4" and "factor(gear)5".

• find_variables() returns the original variable names. find_variables() returns each variable name only once. The return value would be "gear".

• find_predictors() is comparable to find_variables() and also returns the original variable names, but excluded the dependent (response) variables. The return value would be "gear".

Examples

data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
find_parameters(m)
#> $conditional
#> [1] "(Intercept)" "wt"          "cyl"         "vs"         
#>