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Calculate different model performance metrics

Source: R/adm_eval.R
adm_eval.Rd

This function, when supplied with observed and predicted values, calculates accuracy, discrimination, and precision between the two and returns their values as a tibble table. The accuracy is evaluated through mean absolute error. The discrimination is calculated using Spearman correlation, Pearson correlation, intercept and slope of a linear regression between observed and predicted values. The precision is obtained from the standard deviations of predicted and observed values.

Usage

adm_eval(obs, pred)

Arguments

obs

numeric. Observed abundance

pred

numeric. Predicted abundance

Value

a tibble with next columns: corr_spear, corr_pear, mae, inter, slope, and pdisp(see details)

Details

This function calculate metric related to the accuracy, discrimination, and precision of a model:

  • Accuracy: mean absolute error (mae)

  • Discrimination: Spearman's rank correlation (corr_spear)

  • Discrimination: Pearson's correlation (corr_pear)

  • Discrimination: regression intercept between observed and predicted values (inter)

  • Discrimination: regression slope between observed and predicted values (slope)

  • Precision: ratio between predicted and observed standard deviation (pdisp)

Further details see Waldock et al. (2022)

References

  • Waldock, C., Stuart-Smith, R.D., Albouy, C., Cheung, W.W.L., Edgar, G.J., Mouillot, D., Tjiputra, J., Pellissier, L., 2022. A quantitative review of abundance-based species distribution models. Ecography https://doi.org/10.1111/ecog.05694

Examples

if (FALSE) {
pred_a <- c(
  3, 2, 0, 0, 2, 5, 1, 3, 1, 2, 1, 1, 2, 5, 4,
  1, 2, 5, 3, 3, 4, 3, 2, 0, 2, 1, 2, 2, 1, 4,
  4, 2, 2, 1, 6, 1, 1, 3, 5, 0, 1, 1, 0, 1, 2
)
obs_a <- c(
  3, 1, 1, 3, 2, 3, 0, 3, 5, 3, 4, 2, 0, 5, 2,
  1, 2, 2, 3, 6, 3, 2, 4, 2, 1, 2, 3, 5, 0, 3,
  3, 2, 1, 2, 3, 2, 2, 1, 2, 3, 3, 1, 2, 1, 4
)

adm_eval(obs = obs_a, pred = pred_a)
}