ANOVA table for linear model comparisons

anova() computes an incremental F-test across two or more fitted linear models. It dispatches on four input types:

Usage

anova(object, ..., test = "F")

Format

An object of class S7_external_generic of length 4.

Arguments

object

A multi_lazy, anova_lazy, model_lazy, or cld_exec object.

...

Additional model_lazy or cld_exec objects.

test

A string. One of "F" (default), "LRT", or "Chisq".

Value

A cld_anova object, invisibly.

Details

• A multi_lazy from write_models() |> prepare_model().

• An anova_lazy from write_models() |> prepare_model() (legacy path, kept for backward compatibility).

• One or more model_lazy objects from prepare_model().

• One or more cld_exec objects from conclude().

See also

write_models(), prepare_model(), conclude()

Examples

# via write_models()
LifeCycleSavings |>
    write_models(
        f1 = sr ~ 1,
        f2 = sr ~ pop15,
        f3 = sr ~ pop15 + pop75,
        f4 = sr ~ pop15 + pop75 + dpi,
        f5 = sr ~ pop15 + pop75 + dpi + ddpi
    ) |>
    prepare_model(LINEAR_REG) |>
    anova()
#> 
#> == ANOVA · F =================================================================== 
#> 
#> -- ANOVA Table -----------------------------------------------------------------
#> 
#> ───────────────────────────────────────────────────────────
#>   model  res_df  deviance  df  dev_diff  f_value  p_value  
#> ───────────────────────────────────────────────────────────
#>    f1      49    983.628                                   
#>    f2      48    779.511   1   204.118   14.116   <0.001   
#>    f3      47    726.168   1    53.343    3.689    0.061   
#>    f4      46    713.767   1    12.401    0.858    0.359   
#>    f5      45    650.713   1    63.054    4.360    0.042   
#> ───────────────────────────────────────────────────────────
#> 
#> 

# via model_lazy
mod1 = LifeCycleSavings |> define_model(sr ~ 1) |> prepare_model(LINEAR_REG)
mod2 = LifeCycleSavings |> define_model(sr ~ pop15) |> prepare_model(LINEAR_REG)

anova(mod1, mod2)
#> 
#> == ANOVA · F =================================================================== 
#> 
#> -- ANOVA Table -----------------------------------------------------------------
#> 
#> ───────────────────────────────────────────────────────────
#>   model  res_df  deviance  df  dev_diff  f_value  p_value  
#> ───────────────────────────────────────────────────────────
#>     1      49    983.628                                   
#>     2      48    779.511   1   204.118   12.569   <0.001   
#> ───────────────────────────────────────────────────────────
#> 
#> 

# via conclude()
mod1 = LifeCycleSavings |> define_model(sr ~ 1) |> prepare_model(LINEAR_REG) |> conclude()
mod2 = LifeCycleSavings |> define_model(sr ~ pop15) |> prepare_model(LINEAR_REG) |> conclude()

anova(mod1, mod2)
#> 
#> == ANOVA · F =================================================================== 
#> 
#> -- ANOVA Table -----------------------------------------------------------------
#> 
#> ───────────────────────────────────────────────────────────
#>   model  res_df  deviance  df  dev_diff  f_value  p_value  
#> ───────────────────────────────────────────────────────────
#>     1      49    983.628                                   
#>     2      48    779.511   1   204.118   12.569   <0.001   
#> ───────────────────────────────────────────────────────────
#> 
#> 
anova(mod1, mod2, test = "LRT")
#> 
#> == ANOVA · LRT ================================================================= 
#> 
#> -- ANOVA Table -----------------------------------------------------------------
#> 
#> ───────────────────────────────────────────────────────────────
#>   model  res_df  deviance  df  dev_diff  chisq_value  p_value  
#> ───────────────────────────────────────────────────────────────
#>     1      49    983.628                                       
#>     2      48    779.511   1   204.118     12.569     <0.001   
#> ───────────────────────────────────────────────────────────────
#> 
#>