| AICC {chemdeg} | R Documentation |
Akaike Information Criterion With Correction
Description
The function calculates the Akaike Information Criterion with correction for small samples size.
Usage
AICC(fit)
Arguments
fit |
a 'nls'-object |
Details
When the sample size is small, there is a substantial probability that AIC
(see stats::AIC() for more details)
will select models that have too many parameters, i.e. that AIC will
overfit. AICc is AIC with a correction for small sample sizes.
The AICc is computed as follows:
AICc=AIC+\frac{2\,k\,(k+1)}{n-k-1}
where n denotes the sample size and k denotes the number of parameters. Thus
, AICc is essentially AIC with an extra penalty term for the number of
parameters. Note that as n\rightarrow \infty, the extra penalty term
converges to 0, and thus AICc converges to AIC.
Value
Returns the AICc value
See Also
stats::AIC() for uncorrected AIC, stats::BIC(),
stats::sigma() ,chiquad_red() for other goodness of fit indicators.
goodness_of_fit()
Examples
t <- seq(0, 10, 1)
y <- 1 / (0.5 * exp(t) + 1) + stats::rnorm(length(t), 0, 0.05)
fit <- nls(y ~ 1 / (k * exp(t) + 1),
data = list(t = t, y = y),
start = list(k = 0.2)
)
AICC(fit)