aic: aic

In valmetrics: Metrics and Plots for Model Evaluation

Description

Calculates the Akaike information criterion (AIC) from observed values, predicted values, the number of observations and the number of model parameters.

Usage

aic(o, p, k)

Arguments

o	A numeric vector. Observed values.
p	A numeric vector. Predicted values.
k	A number. The number of parameters in the model. Note that k includes the intercept, so for example, k is 2 for a linear regression model.

Details

Interpretation: smaller is better. Akaike information criterion (AIC) punishes complexity of models; a larger number of parameters (k) means a larger AIC value. As it is sensitive to the number of samples, AIC cannot easily be compared between datasets of different sizes.

Value

Akaike information criterion (AIC)

Author(s)

Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg, E-mail: [e-mail]

References

Piikki K., Wetterlind J., Soderstrom M., Stenberg B. (2021). Perspectives on validation in digital soil mapping of continuous attributes. A review. Soil Use and Management. doi: 10.1111/sum.12694

Examples

obs<-c(1:10)
pred<-c(1, 1 ,3, 2, 4, 5, 6, 8, 7, 10)
aic(o=obs, p=pred, k=2)

valmetrics documentation built on Jan. 16, 2021, 5:11 p.m.