Augment a modeler object with influence diagnostics

This function computes various influence diagnostics, including standardized residuals, studentized residuals, and Cook's distance, for an object of class modeler.

Usage

augment(x, id = NULL, metadata = TRUE, ...)

Arguments

x

An object of class modeler.

id

Optional unique identifier to filter by a specific group. Default is NULL.

metadata

Logical. If TRUE, metadata is included with the predictions. Default is FALSE

...

Additional parameters for future functionality.

Value

A tibble containing the following columns:

uid

Unique identifier for the group.

fn_name

Function name associated with the model.

x

Predictor variable values.

y

Observed response values.

.fitted

Fitted values from the model.

.resid

Raw residuals (observed - fitted).

.hat

Leverage values for each observation.

.cooksd

Cook's distance for each observation.

.std.resid

Standardized residuals.

.stud.resid

Studentized residuals.

Author

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 40),
    subset = 2
  )
print(mod_1)
#> 
#> Call:
#> Canopy ~ fn_logistic(DAP, L, k, t0) 
#> 
#> Residuals (`Standardized`):
#>     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
#> -1.53735 -0.60292  0.01037 -0.19574  0.07725  1.17113 
#> 
#> Optimization Results `head()`:
#>  uid   L     k   t0  sse
#>    2 100 0.198 48.3 22.4
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 1.9569 secs        100%  2102 (id)
#> 
augment(mod_1)
#> # A tibble: 8 × 10
#>     uid fn_name         x      y  .fitted   .resid      .hat  .cooksd .std.resid
#>   <dbl> <chr>       <dbl>  <dbl>    <dbl>    <dbl>     <dbl>    <dbl>      <dbl>
#> 1     2 fn_logistic     0   0     0.00684 -0.00684   3.42e-6 1.19e-11   -0.00323
#> 2     2 fn_logistic    29   0     2.11    -2.11      4.96e-2 1.82e- 2   -0.997  
#> 3     2 fn_logistic    36   4.70  7.95    -3.25      2.82e-1 4.32e- 1   -1.54   
#> 4     2 fn_logistic    42  24.6  22.1      2.48      7.16e-1 4.05e+ 0    1.17   
#> 5     2 fn_logistic    56  81.0  82.0     -0.997     9.54e-1 3.39e+ 1   -0.472  
#> 6     2 fn_logistic    76 100    99.5      0.460     3.13e-1 1.05e- 2    0.218  
#> 7     2 fn_logistic    92 100    99.9      0.0645    3.42e-1 2.45e- 4    0.0305 
#> 8     2 fn_logistic   100 100    99.9      0.0507    3.43e-1 1.53e- 4    0.0240 
#> # ℹ 1 more variable: .stud.resid <dbl>