Metrics for modeler

Computes various performance metrics for a modeler object. The function calculates Sum of Squared Errors (SSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R-squared).

Usage

metrics(x, by_grp = TRUE)

Arguments

x

An object of class `modeler` containing the necessary data to compute the metrics.

by_grp

Return the metrics by id? TRUE by default.

Value

A data frame containing the calculated metrics grouped by uid, metadata, and variables.

Details

Sum of Squared Errors (SSE): $$SSE = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$Mean Absolute Error (MAE): $$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$$Mean Squared Error (MSE): $$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$Root Mean Squared Error (RMSE): $$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$$Coefficient of Determination (R-squared): $$R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}$$

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(1:2)
  )
plot(mod_1, id = c(1:2))

print(mod_1)
#> 
#> Call:
#> Canopy ~ fn_linear_sat(DAP, t1, t2, k) 
#> 
#> Sum of Squares Error:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>  0.4489  1.7619  3.0748  3.0748  4.3878  5.7008 
#> 
#> Optimization Results `head()`:
#>  uid   t1   t2     k   sse
#>    1 38.5 61.7  99.8 0.449
#>    2 35.1 61.1 100.0 5.701
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       2 0.8843 secs        100% 461.5 (id)
#> 
metrics(mod_1)
#> # A tibble: 2 × 8
#>     uid var      SSE    MAE    MSE  RMSE r_squared     n
#>   <dbl> <chr>  <dbl>  <dbl>  <dbl> <dbl>     <dbl> <int>
#> 1     1 Canopy 0.449 0.0838 0.0561 0.237      1.00     8
#> 2     2 Canopy 5.70  0.475  0.713  0.844      1.00     8