R/sfplsim.kernel.R

In fsemipar: Estimation, Variable Selection and Prediction for Functional Semiparametric Models

print.sfplsim.kernel<-function(x,...){
cat("*** SFPLSIM fitted using penalized least-squares combined with kernel estimation with Nadaraya-Watson weights ***\n")
cat("\n-Call: ")
print(x$call)
cat("\n-Bandwidth (h): ")
cat(x$h.opt)
cat("\n-Theta coefficients in the B-spline basis: ")
cat(x$theta.est)
cat("\n-Linear coefficients (beta): ")
cat(x$beta.est)
cat("\n-Number of non-zero linear coefficients: ")
cat(length(x$indexes.beta.nonnull))
cat("\n-Indexes non-zero beta-coefficients: ")
cat(x$indexes.beta.nonnull)
cat("\n-Lambda: ")
cat(x$lambda.opt)
cat("\n-IC: ")
cat(x$IC)
cat("\n-Q: ")
cat(x$Q.opt)
cat("\n-Penalty: ")
cat(x$penalty)
cat("\n-Criterion: ")
cat(x$criterion)
cat("\n-vn: ")
cat(x$vn.opt)
cat("\n")
}


summary.sfplsim.kernel<-function(object,...){
x<-object
cat("*** SFPLSIM fitted using penalized least-squares combined with kernel estimation with Nadaraya-Watson weights ***\n")
cat("\n-Call: ")
print(x$call)
cat("\n-Bandwidth (h): ")
cat(x$h.opt)
cat("\n-Theta coefficients in the B-spline basis: ")
cat(x$theta.est)
cat("\n-Linear coefficients (beta): ")
cat(x$beta.est)
cat("\n-Number of non-zero linear coefficients: ")
cat(length(x$indexes.beta.nonnull))
cat("\n-Indexes non-zero beta-coefficients: ")
cat(x$indexes.beta.nonnull)
cat("\n-IC: ")
cat(x$IC)
cat("\n-Q: ")
cat(x$Q.opt)
cat("\n-Penalty: ")
cat(x$penalty)
cat("\n-Criterion: ")
cat(x$criterion)
cat("\n-vn: ")
cat(x$vn.opt)
cat("\n")
}


predict.sfplsim.kernel<- function(object,newdata.x=NULL,newdata.z=NULL,y.test=NULL,option=NULL, ...)
{
if(is.null(newdata.x)|is.null(newdata.z)){
	y <- fitted(object)
	out<-y
}
else{
	if(is.null(option)) option<-1
	x.test <- newdata.x
	z.test<- newdata.z
	pred.LR.n <- as.matrix(z.test)%*%object$beta.est
    y.new<-object$y - as.matrix(object$z)%*%object$beta.est		
	if (option==1) {
		pred.FSIM.n <- fsim.kernel.test(y=y.new,x=object$x, x.test=x.test,y.test=y.test, theta=object$theta.est, h=object$h.opt, 
						kind.of.kernel=object$kind.of.kernel, range.grid=object$range.grid, order.Bspline=object$order.Bspline, 
						nknot=object$nknot, nknot.theta=object$nknot.theta)
		pred.n <- pred.LR.n + pred.FSIM.n$y.estimated.test
		y1<-pred.n
		if(is.null(y.test)){
            MSEP.1<-NULL
            out<-y1
        }
        else {
			MSEP.1 <- mean((y1 - y.test)^2)
			out<-list(y=y1,MSEP.1=MSEP.1)       
		}
    }
	if (option==2) {
	    kernel<-get(object$kind.of.kernel)
		aux2 <-fsim.kernel.fit.fixedtheta(y=y.new,x=object$x, norm.diff=object$norm.diff,
				min.quantile.h=object$min.quantile.h,max.quantile.h=object$max.quantile.h,h.seq=object$h.seq,num.h=object$num.h,
				kind.of.kernel=object$kind.of.kernel)
		h.opt.2 <- aux2$h.opt
		pred.FSIM.n.2 <- fsim.kernel.test(y=y.new,x=object$x, x.test=x.test, y.test=y.test, theta=object$theta.est, h=h.opt.2, 
							kind.of.kernel=object$kind.of.kernel, range.grid=object$range.grid, order.Bspline=object$order.Bspline, 
							nknot=object$nknot,nknot.theta=object$nknot.theta)
		pred.n.2 <- pred.LR.n + pred.FSIM.n.2$y.estimated.test
		y2<-pred.n.2
		if(is.null(y.test)){
			MSEP.2<-NULL
			out<-y2
		}
		else{
			MSEP.2 <-mean((y2 - y.test)^2)
			out<-list(y=y2,MSEP.2=MSEP.2)
		} 		 
    }
}  
out
}


plot.sfplsim.kernel<-function(x,size=15,col1=1,col2=2,col3=4,...)
{

THETA<-x$theta.est
a<-x$range.grid[1]
b<-x$range.grid[2]
nknot.theta<-x$nknot.theta
order.Bspline<-x$order.Bspline
x.t <- seq(a, b, length=ncol(x$x))
Knot.theta<-seq(a, b, length = nknot.theta + 2)[ - c(1, nknot.theta + 2)]
delta.theta<-sort(c(rep(c(a, b),order.Bspline), Knot.theta))
Bspline.theta<-splineDesign(delta.theta,x.t,order.Bspline)
theta.rec<-Bspline.theta%*%THETA 
theta_df <- data.frame(x.t, theta.rec)
g1<-ggplot(theta_df, aes(x = x.t, y = theta.rec)) +
  geom_line(linewidth = 1.5, color = col1) +
  labs(x = "range.grid X", y = "", title = expression(widehat(theta)[0])) +
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5,size=size),axis.title.x=element_text(size=size))
fit<-fitted(x)
mod <- lm(x$y ~ fit)
res <- residuals(mod)
y<-x$y
x_df <- data.frame(fit, y)
g2<-ggplot(x_df, aes(x = fit, y = y)) +
  geom_point(colour = col1,shape=1, size=5) + 
  geom_smooth(method=lm,formula=y~x,colour =col2,  size = 1.5) +
  labs(x = "Fitted values", y = "y", title = "Response vs Fitted values") +
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5,size=size),axis.title.x=element_text(size=size))
x_df2 <- data.frame(fit, res)
g3<-ggplot(x_df2, aes(x = fit, y = res)) +
  geom_point(colour = col1,shape=1,size=5) + 
  geom_hline(yintercept = 0, linetype = "dashed", colour = 1, linewidth= 1) +
  geom_smooth(method=loess,formula=y~x,linewidth=1.5,col=col3)+
  labs(x = "Fitted values", y = "Residuals", title = "Residuals vs Fitted Values") +
  theme_bw()+
  theme(plot.title = element_text(hjust = 0.5,size=size),axis.title.x=element_text(size=size))
grid.arrange(g1, g2, g3,ncol = 3)
}