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Linear regression

Source: R/model-linear-reg.R
LINEAR_REG.Rd

Fits an ordinary least squares linear regression model. Accepts rel() or a formula as the variable mapper <var_id>.

Usage

LINEAR_REG(.var_id = NULL, .data = NULL, ...)

Arguments

.var_id

A variable mapper <var_id> from define_model(), or NULL to return a model_spec for use in prepare_model().

.data

A data frame. Used when .var_id is supplied directly.

...

Currently unused.

Value

A cld_exec object containing a class_lm_object, or a model_spec when .var_id = NULL.

Details

The result is an class_lm_object, which satisfies the anova() protocol and prints coefficients and model fit universally across all engines and variants.

Examples

# via rel()
cars |>
    define_model(rel(speed, dist)) |>
    prepare_model(LINEAR_REG) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : rel 
#> Args : speed ; dist 
#>     x_vars : 1 
#>     resp_vars : 1 
#> 
#> == Linear Regression =========================================================== 
#> 
#> -- Coefficients ----------------------------------------------------------------
#> 
#> ──────────────┬───────────────────────────────────────────
#>   term        │  estimate  std_error  statistic  p_value  
#> ──────────────┼───────────────────────────────────────────
#>   (Intercept) │  -17.579     6.758     -2.601     0.012   
#>   speed       │   3.932      0.416      9.464    <0.001   
#> ──────────────┴───────────────────────────────────────────
#> 
#> 
#> -- Model Fit -------------------------------------------------------------------
#> 
#> Warning: running command 'tput cols' had status 2
#> ------------------------------------------------------
#>   R Squared      :    0.65    F-statistic :    89.57
#>   Adj. R Squared :    0.64    df1         :        1
#>   Sigma          :   15.38    df2         :       48
#>   n              :      50    p-value     :   <0.001
#>   df (residual)  :      48                :         
#> ------------------------------------------------------
#> 
#> 

# via formula
cars |>
    define_model(dist ~ speed) |>
    prepare_model(LINEAR_REG) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : formula 
#> Args : dist ~ speed 
#>     left_var : 1 
#>     right_var : 1 
#> 
#> == Linear Regression =========================================================== 
#> 
#> -- Coefficients ----------------------------------------------------------------
#> 
#> ──────────────┬───────────────────────────────────────────
#>   term        │  estimate  std_error  statistic  p_value  
#> ──────────────┼───────────────────────────────────────────
#>   (Intercept) │  -17.579     6.758     -2.601     0.012   
#>   speed       │   3.932      0.416      9.464    <0.001   
#> ──────────────┴───────────────────────────────────────────
#> 
#> 
#> -- Model Fit -------------------------------------------------------------------
#> 
#> Warning: running command 'tput cols' had status 2
#> ------------------------------------------------------
#>   R Squared      :    0.65    F-statistic :    89.57
#>   Adj. R Squared :    0.64    df1         :        1
#>   Sigma          :   15.38    df2         :       48
#>   n              :      50    p-value     :   <0.001
#>   df (residual)  :      48                :         
#> ------------------------------------------------------
#> 
#> 

# write_models() pipeline
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   
#> ───────────────────────────────────────────────────────────
#> 
#> 

# individual conclude(), compare after
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   
#> ───────────────────────────────────────────────────────────
#> 
#>