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Find model parameters from models with special components

Source: R/find_parameters_other.R
find_parameters.averaging.Rd

Returns the names of model parameters, like they typically appear in the summary() output.

Usage

# S3 method for class 'averaging'
find_parameters(x, component = "conditional", flatten = FALSE, ...)

Arguments

x

A fitted model.

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.

flatten

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

...

Currently not used.

Value

A list of parameter names. The returned list may have following elements, usually requested via the component argument:

  • conditional, the "fixed effects" part from the model.

  • full, parameters from the full model.

  • precision for models of class betareg.

  • survival for model of class mjoint.

  • extra for models of class glmx.

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. It can be any pre-defined or arbitrary distributional parameter, like mu, ndt, kappa, etc.

Examples

data("GasolineYield", package = "betareg")
m <- betareg::betareg(yield ~ batch + temp, data = GasolineYield)
find_parameters(m)
#> $conditional
#>  [1] "(Intercept)" "batch1"      "batch2"      "batch3"      "batch4"     
#>  [6] "batch5"      "batch6"      "batch7"      "batch8"      "batch9"     
#> [11] "temp"       
#> 
#> $precision
#> [1] "(phi)"
#> 
find_parameters(m, component = "precision")
#> $precision
#> [1] "(phi)"
#>