plsRglm (version 0.3.3)

AICpls: AIC functions for plsR models

Description

This function provides AIC computations for an univariate plsR model.

Usage

AICpls(nn, ncomp, residpls)
AICpls2(ncomp, dataYpls, YChapeaupls, residpls)

Arguments

nn

Number of rows of the dataset

ncomp

Number of components

dataYpls

Vector of the response

YChapeaupls

Fitted values of a plsR univariate plsR model

residpls

Residuals of a fitted univariate plsR model

Value

• real
  AIC value

Details

AIC functions for plsR models with univariate response.

References

Baibing Li, Julian Morris, Elaine B. Martin, Model selection for partial least squares regression, Chemometrics and Intelligent Laboratory Systems 64 (2002) 79-89. http://dx.doi.org/10.1016/S0169-7439(02)00051-5

See Also

loglikpls and loglikpls2 and AIC for loglikelihood computations for linear models

Examples

data(pine)
ypine <- pine[,11]
Xpine <- pine[,1:10]
(Pinscaled <- as.data.frame(cbind(scale(log(ypine)),scale(as.matrix(Xpine)))))
colnames(Pinscaled)[1] <- "yy"

lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

modpls <- plsR(log(ypine),Xpine,10)
modpls$Std.Coeffs
lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)

AIC(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))
print(logLik(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))

sum(dnorm(modpls$RepY, modpls$Std.ValsPredictY, sqrt(mean(modpls$residY^2)), log=TRUE))
sum(dnorm(Pinscaled$yy,fitted(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)),sqrt(mean(residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled))^2)), log=TRUE))
loglikpls2(modpls$RepY,modpls$Std.ValsPredictY,modpls$residY)
loglikpls2(Pinscaled$yy,fitted(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)),residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
loglikpls(33,10,modpls$residY)
loglikpls(33,10,residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(33,10,residuals(lm(yy~x1+x2+x3+x4+x5+x6+x7+x8+x9+x10,data=Pinscaled)))
AICpls(33,10,modpls$residY)