Evidence ratio for model comparisons with AIC, AICc or BIC
The evidence ratio
\frac{1}{exp(-0.5 \cdot (IC2 - IC1))}
is calculated for one of the information criteria IC = AIC, AICc, BIC either from two fitted models or two numerical values. Models can be compared that are not nested and where the f-test on residual-sum-of-squares is not applicable.
evidence(x, y, type = c("AIC", "AICc", "BIC"))x |
a fitted object or numerical value. |
y |
a fitted object or numerical value. |
type |
any of the three Information Criteria |
Small differences in values can mean substantial more 'likelihood' of one model over the other. For example, a model with AIC = -130 is nearly 150 times more likely than a model with AIC = -120.
A value of the first model x being more likely than the second model y. If large, first model is better. If small, second model is better.
Andrej-Nikolai Spiess
## Compare two four-parameter and five-parameter ## log-logistic models. m1 <- pcrfit(reps, 1, 2, l4) m2 <- pcrfit(reps, 1, 2, l5) evidence(m2, m1) ## Ratio of two AIC's. evidence(-120, -123)
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