this function permits the estimation of the AIC for models for which the function 'AIC' from the 'stats' package does not work.
AICmodel(model = NULL, residuals = NULL, np = NULL)Arguments
- model
if provided, it is an R object from where the residuals and model parameters can be retrieved using resid(model) and coef(model), respectively.
- residuals
if provided, it is numerical vector with the residuals: residuals = observed.values - predicted.values, where predicted values are estimated from the model. If the parameter 'model' is not provided, then this parameter must be provided.
- np
number of model parameters. If the parameter 'model' is not provided, then 'np' and 'residuals' must be provided.
Value
AIC numerical value
Details
if for a given model 'm' AIC(m) works, then AICmodel(m) = AIC(m).
Examples
## Build a set of point holding an exponential decay
## and adding random noise.
set.seed(77)
x = runif(100, 1, 5)
y = 2 * exp(-0.5 * x) + runif(100, 0, 0.1)
plot(x, y)
## Non-linear regression
nlm <- nls(Y ~ a * exp( b * X), data = data.frame(X=x, Y=y),
start=list(a=1.5, b=-0.7),
control=nls.control(maxiter=10^4, tol=1e-05),
algorithm='port')
## Estimations of Akaike information criteria given by 'AIC' function
## from stats' R package and from 'AICmodel' function are equal.
round(AICmodel(nlm), 3) == round(AIC(nlm), 3)
#> [1] TRUE
## Now, using residuals from the fitted model:
res = y - coef(nlm)[1] * exp(coef(nlm)[2] * x)
round(AICmodel(residuals = res, np = 2), 3) == round(AIC(nlm), 3)
#> [1] TRUE