e_lm_model_criteria: Return table of model selection criteria

In erikerhardt/erikmisc: Erik Erhardt's miscellaneous functions for solving complex data analysis workflows

Return table of model selection criteria

Description

Calculates a large set of model selection criteria and returns them in a one-row tibble.

AIC and BIC criteria from the stats package:

• aic Akaike's An Information Criterion

• bic Bayesian Information Criterion (AIC with k = log(nobs(object)))

• caic Conditional Akaike Information for 'lme4' and 'lme'

• nobs number of observations used to fit lm_fit

Several criteria from the lm summary:

• r2 R-squared statistic

• r2adj Adjusted R-squared statistic

• f_stat F-statistic compared to grand mean model (NA if y ~ 1 intercept-only model)

• p F-statistic numerator degrees-of-freedom (number of parameters in the model)

• df F-statistic denominator degrees-of-freedom (number of df estimating the variance)

• f_stat_pval F-statistic p-value

• rse Residual standard error.

Several criteria are defined in ?modelr::`model-quality`:

Three summaries are immediately interpretible on the scale of the response variable:

• rmse is the root-mean-squared-error

• mae is the mean absolute error

• qae is quantiles of absolute error.

Other summaries have varying scales and interpretations:

• mape mean absolute percentage error.

• rsae is the relative sum of absolute errors.

• mse is the mean-squared-error.

• rsquare is the variance of the predictions divided by the variance of the response.

Usage

e_lm_model_criteria(lm_fit = NULL, dat_fit = NULL, model_id = NULL)

Arguments

lm_fit	fitted model object
dat_fit	data used for model fit
model_id	label for each model that is fit, helps to match up with original model

Value

     a tibble of model selection criteria

Examples

lm_form <- formula(mpg ~ cyl + carb + disp + hp + disp:hp + wt + vs + am + gear)
lm_fit <-
  stats::lm(
    formula = lm_form
  , data    = dat_mtcars_e
  )
lm_crit <-
  e_lm_model_criteria(
    lm_fit   = lm_fit
  , dat_fit  = dat_mtcars_e
  , model_id = 1
  )
lm_crit |> print(width = Inf)

# intercept-only model
lm_form <- formula(mpg ~ 1)
lm_fit <-
  stats::lm(
    formula = lm_form
  , data    = dat_mtcars_e
  )
lm_crit <-
  e_lm_model_criteria(
    lm_fit   = lm_fit
  , dat_fit  = dat_mtcars_e
  , model_id = 1
  )
lm_crit |> print(width = Inf)

erikerhardt/erikmisc documentation built on April 17, 2025, 10:48 a.m.