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