data("ChickWeight")
Dieta_1 = ChickWeight[ChickWeight$Diet==1,c('Time','weight')]
Dieta_2 = ChickWeight[ChickWeight$Diet==2,c('Time','weight')]
Dieta_3 = ChickWeight[ChickWeight$Diet==3,c('Time','weight')]
Dieta_4 = ChickWeight[ChickWeight$Diet==4,c('Time','weight')]

#Esta funcion calcula el h optimo por GCV, y grafica las la regresion con sus
#bandas de confianza.
#Inputs:
#vector y
#vector x,
#vector h: los distintos valores de h en donde evaluaremos
#numeric deg: ??
regressionPlot <- function(y, x, h, deg=1) {
    alphamat = matrix(0,ncol=2,nrow=length(h))
    alphamat[,2] = h
    gcvs = gcvplot(y~x, alpha=alphamat, deg=deg,maxk=1000)

    #h optimo por GCV:
    opth = max(gcvs$alpha[gcvs$values == min(gcvs$values),2])
    locfitopt = locfit(y ~ x, alpha=c(0,opth),deg=1,maxk=1000)
    # Bandas de confianza
    plot(locfitopt, newdata=NULL, tr=NULL, what="coef", band="global", get.data=FALSE, col="red")
}

par(mfrow=c(2,2)); #Que haga los 4 graficos en una misma ventana (grilla de 2x2)
regressionPlot(Dieta_1$weight, Dieta_1$Time, seq(1,6,0.1));
regressionPlot(Dieta_2$weight, Dieta_2$Time, seq(1,6,0.1));
regressionPlot(Dieta_3$weight, Dieta_3$Time, seq(1,6,0.1));
regressionPlot(Dieta_4$weight, Dieta_4$Time, seq(1,6,0.1));