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Get statistic associated with estimates

Source: R/get_statistic.R
get_statistic.Rd

Returns the statistic (t, z, ...) for model estimates. In most cases, this is the related column from coef(summary()).

Usage

get_statistic(x, ...)

# Default S3 method
get_statistic(x, column_index = 3, verbose = TRUE, ...)

# S3 method for class 'glmmTMB'
get_statistic(x, component = "all", ...)

# S3 method for class 'emmGrid'
get_statistic(x, ci = 0.95, adjust = "none", merge_parameters = FALSE, ...)

# S3 method for class 'gee'
get_statistic(x, robust = FALSE, ...)

Arguments

x

A model.

...

Currently not used.

column_index

For model objects that have no defined get_statistic() method yet, the default method is called. This method tries to extract the statistic column from coef(summary()), where the index of the column that is being pulled is column_index. Defaults to 3, which is the default statistic column for most models' summary-output.

verbose

Toggle warnings.

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.

ci

Confidence Interval (CI) level. Default to 0.95 (95%). Currently only applies to objects of class emmGrid.

adjust

Character value naming the method used to adjust p-values or confidence intervals. See ?emmeans::summary.emmGrid for details.

merge_parameters

Logical, if TRUE and x has multiple columns for parameter names (like emmGrid objects may have), these are merged into a single parameter column, with parameters names and values as values.

robust

Logical, if TRUE, test statistic based on robust standard errors is returned.

Value

A data frame with the model's parameter names and the related test statistic.

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(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_statistic(m)
#>     Parameter  Statistic
#> 1 (Intercept) 11.3994647
#> 2          wt -4.1204121
#> 3         cyl -2.2234114
#> 4          vs  0.3221477