Generalized linear model

A modified GLM for {statim} pipeline passed through stats::glm().

Usage

GLM(.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.

...

Additional arguments passed to stats::glm().

Value

A cld_exec object in a class_glm_object, or a model_spec when .var_id = NULL.

Details

Additional arguments are passed to stats::glm(). The most important is family, which controls the error distribution and link function (e.g. stats::binomial(), stats::poisson()). Defaults to stats::gaussian() when omitted.

Examples

# logistic regression
mtcars |>
    define_model(am ~ wt + hp) |>
    prepare_model(GLM) |>
    update(family = binomial()) |>
    conclude()
#> 
#> == Model ======================================================================= 
#> 
#> Variable Mapper : formula 
#> Args : am ~ wt + hp 
#>     left_var : 1 
#>     right_var : 2 
#> 
#> == Generalized Linear Model ==================================================== 
#> 
#> -- Coefficients ----------------------------------------------------------------
#> 
#> ──────────────┬───────────────────────────────────────────
#>   term        │  estimate  std_error  statistic  p_value  
#> ──────────────┼───────────────────────────────────────────
#>   (Intercept) │   18.866     7.444      2.535     0.011   
#>   wt          │   -8.083     3.069     -2.634    <0.001   
#>   hp          │   0.036      0.018      2.044     0.041   
#> ──────────────┴───────────────────────────────────────────
#> 
#> 
#> -- Model Fit -------------------------------------------------------------------
#> 
#> ────────────────────────────────────────────────────────────────────────
#>    family   link   null_deviance  deviance  df_residual   aic    n_obs  
#> ────────────────────────────────────────────────────────────────────────
#>   binomial  logit     43.230       10.059       29       16.059   32    
#> ────────────────────────────────────────────────────────────────────────
#> 
#> 

if (FALSE) { # \dontrun{
# model comparison via anova()
mod1 = mtcars |>
    define_model(am ~ 1) |>
    prepare_model(GLM) |>
    update(family = binomial()) |>
    conclude()
mod2 = mtcars |>
    define_model(am ~ wt) |>
    prepare_model(GLM) |>
    update(family = binomial()) |>
    conclude()
mod3 = mtcars |>
    define_model(am ~ wt + hp) |>
    prepare_model(GLM) |>
    update(family = binomial()) |>
    conclude()

anova(mod1, mod2, mod3)
} # }