akaike.all(x, y, interval = c(0.15, 1, 0.05))
akaike.l
for details. This procedure is less computation intensive than cross-validation, but the resulting parameters do not provide the LOESS regression that best fit the data. Instead, it selects the parameters that best fit the data conditioned to simplicity of the model.
Hurvich, C.M., and J.S. Simonoff. 1998. Smoothing parameters selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Society, Series B 60: 271-293.
Cleveland, W.S., and S.J. Devlin. 1988. Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83: 596-610.
loess
for details on LOESS regression, and akaike.l
for details on AIC.
data(modernq)
# Calculate percentages
perq<-percenta(modernq,first=2,last=39)[,2:55]
akaike.all(modernq[,1],perq[,1:10])
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