AICmodel: Akaike's Information Criterion (AIC)

In genomaths/MethylIT: Methylation Analysis Based on Signal Detection

Akaike's Information Criterion (AIC)

Description

this function permits the estimation of the AIC for models for which the function 'AIC' from the 'stats' package does not work.

Usage

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.

Details

if for a given model 'm' AIC(m) works, then AICmodel(m) = AIC(m).

Value

AIC numerical value

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.
AICmodel(nlm) == AIC(nlm)

## Now, using residuals from the fitted model:
res = y - coef(nlm)[1] * exp(coef(nlm)[2] * x)

AICmodel(residuals=res, np=2) == AIC(nlm)

genomaths/MethylIT documentation built on Feb. 3, 2024, 1:24 a.m.