R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("JLLprod-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('JLLprod')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Blocc"
> 
> ### * Blocc
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Blocc
> ### Title: Fast Local Constant Regression for Bivariate Data
> ### Aliases: Blocc
> ### Keywords: smooth regression
> 
> ### ** Examples
> 
> #A very simple case
> n <- 1000
> x <- runif(n); z <- runif(n); e <- rnorm(n,sd=0.2)
> G <- function(x){(1/2)*sin(2*pi*x)}
> F <- function(z){-1/3+2*z-2*(z^2)}
> y <- G(x)+F(z) + e
> xgrid <- seq(0,1,length=30); zgrid <- seq(0,1,length=30)
> 
> m <- Blocc(xx=x,zz=z,yy=y,ev=cbind(xgrid,zgrid))
> GF <- matrix(G(m$xxe),nr=30,nc=30,byrow=FALSE)+matrix(F(m$zze)
+              ,nr=30,nc=30,byrow=TRUE)
> 
> #win.graph()
> layout(matrix(c(1,2),nr=1,nc=2,byrow=TRUE))
> persp(x=m$zze,y=m$xxe,z=t(GF),theta= 320, phi=17,xlab="z"
+       ,ylab="x",zlab="",main="True G(x)+F(z)")
> persp(x=m$zze,y=m$xxe,z=t(m$r),theta= 320, phi=17,xlab="z"
+       ,ylab="x",zlab="",main="Estimated G(x)+F(z)")
> 
> 
> 
> cleanEx(); ..nameEx <- "JLL"
> 
> ### * JLL
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: JLL
> ### Title: Generalized Homothetic Production Function: Main Estimator
> ### Aliases: JLL
> ### Keywords: smooth regression
> 
> ### ** Examples
> 
> #A simple simulation
> d<- 1;  n=400
> xx <- runif(n^d); zz <- runif(n^d);
> x <- cbind(xx,zz)
> e <- rnorm(n^d,sd=1)
> G <- function(x){(1/2)*sin(2*pi*x)}
> F <- function(z){-2*(z^2)+2*z-1/3}
> M <- function(x,z){G(x)+F(z)}
> H <- function(m){1+(16/7)*m}
> z0<-1/4; r0<-H(0);
> y <- H(M(x[,1],x[,2])) + e
> m <- JLL(xx=x[,1],zz=x[,2],yy=y,p1=3,p2=1,pstar=1,h1=0.15,h2=0.7,ngrid=10,
+          z0=z0,r0=r0)
> 
> #win.graph()
> layout(matrix(c(1,2,1,2),2,2, byrow=TRUE),respect=TRUE)
> plot(m$x,m$Ghat,type="l",lty=1)
> lines(m$x,G(m$x),type="l",lty=2,col="blue")
> plot(m$z,m$Fhat,type="l",lty=1)
> lines(m$z,F(m$z),type="l",lty=2,col="blue")
> 
> 
> 
> cleanEx(); ..nameEx <- "JLL.plot"
> 
> ### * JLL.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: JLL.plot
> ### Title: Plots of Nonparametric Elements of a Generalized Homothetic
> ###   Production Function
> ### Aliases: JLL.plot
> ### Keywords: smooth
> 
> ### ** Examples
> 
> library(JLLprod)
> data(ecu)
> ##This part simply does some data sorting & trimming
> xlnK <- ecu$lnk
> xlnL <- ecu$lnl
> xlnY <- ecu$lny
> xqKL <- quantile(xlnK-xlnL,  probs=c(2.5,97.5)/100)
> yx <- cbind(xlnY,xlnK,xlnL)
> tlnklnl <- yx[((yx[,2]-yx[,3])>=xqKL[1]) & ((yx[,2]-yx[,3])<=xqKL[2]),]
> tlnklnl[,2]<-tlnklnl[,2]-tlnklnl[,3]
> g<-seq(min(tlnklnl[,3]),max(tlnklnl[,3]),length=29)
> 
> #This part does the actual calculations & plots
> m.JLL <- JLL(xx=tlnklnl[,2],zz=tlnklnl[,3],yy=tlnklnl[,1],p1=2,p2=2,
+              pstar=0,h1=3.125,h2=5.25,ngrid=30,z0=g[25],r0=7.5)
> JLL.plot(m.JLL)
> 
> 
> 
> cleanEx(); ..nameEx <- "JLLp"
> 
> ### * JLLp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: JLLp
> ### Title: Parametric Generalized Homothetic Production Function
> ### Aliases: JLLp
> ### Keywords: regression
> 
> ### ** Examples
> 
> library(JLLprod)
> data(ecu)
> ##This part simply does some data sorting & trimming
> xlnK <- ecu$lnk
> xlnL <- ecu$lnl
> xlnY <- ecu$lny
> xqKL <- quantile(xlnK-xlnL,  probs=c(2.5,97.5)/100)
> yx <- cbind(xlnY,xlnK,xlnL)
> tlnklnl <- yx[((yx[,2]-yx[,3])>=xqKL[1]) & ((yx[,2]-yx[,3])<=xqKL[2]),]
> tlnklnl[,2]<-tlnklnl[,2]-tlnklnl[,3]
> 
> bb<-list(b0=11,b1=1,b2=0,g=-0.15,a=0.4)
> Y <- tlnklnl[,1]; K <- tlnklnl[,2]; L <- tlnklnl[,3]
> pJLL<-JLLp(Y,K,L,theta=bb,model=2)
> print(summary(pJLL))

Formula: lnY ~ b0 + b1 * (a * lnk + log(exp(lnL) + g)) + b2 * (a * lnk + 
    log(exp(lnL) + g))^2

Parameters:
     Estimate Std. Error t value Pr(>|t|)    
b0    6.96420    1.99646   3.488 0.000543 ***
b1    0.04620    0.51711   0.089 0.928861    
b2    0.09922    0.03812   2.603 0.009608 ** 
g  -558.04861  198.04679  -2.818 0.005089 ** 
a     0.30006    0.03676   8.164 4.87e-15 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 0.7325 on 379 degrees of freedom

Correlation of Parameter Estimates:
   b0 b1 b2 g
b1 B  1      
b2 B  B  1   
g  +  ,  ,  1
a  ,  .  ,  ,
attr(,"legend")
[1] 0 ' ' 0.3 '.' 0.6 ',' 0.8 '+' 0.9 '*' 0.95 'B' 1

> 
> 
> 
> cleanEx(); ..nameEx <- "LL"
> 
> ### * LL
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LL
> ### Title: Homothetic Production Function: Main Estimator
> ### Aliases: LL
> ### Keywords: smooth regression
> 
> ### ** Examples
> 
> 
> data(ecu)
> ##This part simply does some data sorting & trimming
> xlnK <- ecu$lnk
> xlnL <- ecu$lnl
> xlnY <- ecu$lny
> xqKL <- quantile(exp(xlnK)/exp(xlnL),  probs=c(2.5,97.5)/100)
> yx <- cbind(xlnY,xlnK,xlnL)
> tlnklnl <- yx[((exp(yx[,2])/exp(yx[,3]))>=xqKL[1]) 
+               & ((exp(yx[,2])/exp(yx[,3]))<=xqKL[2]),]
> Y<-tlnklnl[,1]
> K<-exp(tlnklnl[,2])/median(exp(tlnklnl[,2]))
> L<-exp(tlnklnl[,3])/median(exp(tlnklnl[,3]))
> 
> LLb<-LL(xx=K,zz=L,yy=Y,xxo=median(K),zzo=median(L),k=80,j=100)
> 
> #win.graph()
> nf <- layout(matrix(c(1,2,1,2),2,2, byrow=TRUE),respect=TRUE)
> plot(log(K)-log(L),log(LLb$g)-log(L),pch=3,xlab="ln(K/L)"
+      ,ylab="ln(g(K/L,1))",main="Homogeneous Component g")
> plot(log(LLb$g),LLb$h,xlab="ln(g)",pch=3,ylab="h(g)"
+      ,main="Nonhomogeneous Component h",ylim=c(min(min(LLb$h)
+      ,min(LLb$r)),max(max(LLb$h),max(LLb$r))))
> points(log(LLb$g),LLb$r,type="p",pch=1,col="blue",lwd=2)
> legend(-0.5,15.5,c("Nonparametric","Kernel Regression")
+        ,merge=TRUE,lty=c(1,-1),pch=c(3,1),lwd=c(1,2)
+        ,col=c("black","blue"),cex=0.95)
> 
> 
> 
> cleanEx(); ..nameEx <- "LLef"
> 
> ### * LLef
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LLef
> ### Title: Homothetic Production Function: Most Efficient Estimator
> ### Aliases: LLef
> ### Keywords: smooth regression
> 
> ### ** Examples
> 
> 
> data(ecu)
> ##This part simply does some data sorting & trimming
> xlnK <- ecu$lnk
> xlnL <- ecu$lnl
> xlnY <- ecu$lny
> xqKL <- quantile(exp(xlnK)/exp(xlnL),  probs=c(2.5,97.5)/100)
> yx <- cbind(xlnY,xlnK,xlnL)
> tlnklnl <- yx[((exp(yx[,2])/exp(yx[,3]))>=xqKL[1]) 
+               & ((exp(yx[,2])/exp(yx[,3]))<=xqKL[2]),]
> Y<-tlnklnl[,1]
> K<-exp(tlnklnl[,2])/median(exp(tlnklnl[,2]))
> L<-exp(tlnklnl[,3])/median(exp(tlnklnl[,3]))
> 
> LLb<-LL(xx=K,zz=L,yy=Y,xxo=median(K),zzo=median(L),k=80,j=100)
> LLbef <- LLef(xx=K,zz=L,yy=Y,h0=1,LLob=LLb)
> 
> #win.graph()
> nf <- layout(matrix(c(1,2,1,2),2,2, byrow=TRUE),respect=TRUE)
> plot(log(K)-log(L),log(LLbef$gef)-log(L),pch=3,xlab="ln(K/L)"
+      ,ylab="ln(g(K/L,1))",main="Homogeneous Component g")
> plot(log(LLbef$gef),LLbef$hef,xlab="ln(g)",pch=3,ylab="h(g)"
+      ,main="Nonhomogeneous Component h",ylim=c(min(min(LLbef$hef)
+      ,min(LLb$r)),max(max(LLbef$hef),max(LLb$r))))
> points(log(LLbef$gef),LLb$r,type="p",pch=1,col="blue",lwd=2)
> legend(-0.5,15.6,c("Nonparametric","Kernel Regression")
+        ,merge=TRUE,lty=c(1,-1),pch=c(3,1),lwd=c(1,2)
+        ,col=c("black","blue"),cex=0.95)
> 
> 
> 
> cleanEx(); ..nameEx <- "ecu"
> 
> ### * ecu
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ecu
> ### Title: Production Data: Ecuador (2002)
> ### Aliases: ecu
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(ecu)
> #some plots
> #win.graph()
> layout(matrix(c(1,2,1,2),2,2, byrow=TRUE),respect=TRUE)
> plot(ecu$lnk-ecu$lnl,ecu$lny,xlab="ln(K/L)",ylab="ln(y)",main="")
> plot(ecu$lnl,ecu$lny,xlab="ln(L)",ylab="ln(y)",main="")
> par(oma=c(1,1,1.85,1),new=TRUE,font=2,cex=1)
> mtext(outer=TRUE,"Petroleum, Chemical & Plastics - 2002",side=3,cex=1)
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "locpoly"
> 
> ### * locpoly
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: locpoly
> ### Title: Local Polynomial Regression
> ### Aliases: locpoly
> ### Keywords: smooth regression
> 
> ### ** Examples
> 
> #A very simple case
> n <- 5000
> x <- runif(n); e <- rnorm(n,sd=1)
> G <- function(x){(1/2)*sin(2*pi*x)}
> y <- G(x) + e
> xgrid <- seq(0,1,length=30)
> m <- locpoly(y=y,x=x,h=0.08,p=1,targmat=xgrid,der=0,nobmin=5,kernel="gauss")
> plot(xgrid,G(xgrid),xlab="x",ylab="G",main="",col="black",type="l",lty=1)
> lines(xgrid,m$yhat,col="red",type="l",lty=2)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  164.36 16.92 182.21 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')