# aic

0th

Percentile

##### aic

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)

##### 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. 10.1111/sum.12694

• aic
##### Examples
# NOT RUN {
obs<-c(1:10)
pred<-c(1, 1 ,3, 2, 4, 5, 6, 8, 7, 10)
aic(o=obs, p=pred, k=2)

# }

Documentation reproduced from package valmetrics, version 1.0.0, License: MIT + file LICENSE

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