metrics: Metrics for an object of class 'modeler'

In flexFitR: Flexible Non-Linear Least Square Model Fitting

Metrics for an object of class modeler

Description

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.

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}

Value

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

Examples

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

flexFitR documentation built on April 16, 2025, 5:09 p.m.