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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> invisible(options(echo = TRUE))
> library(rpart)
> 
> target.lrn <- read.table("../lrn/num/472.dat",header=T,colClasses="numeric")
> target.val <- read.table("../val/num/472.dat",header=T,colClasses="numeric")
> target.tst <- read.table("../tst/num/472.dat",header=T,colClasses="numeric")
> 
>  
> y.lrn <- target.lrn[,1]
> y.val <- target.val[,1]
> y.tst <- target.tst[,1]
> y     <- c(y.lrn,y.val,y.tst)
> 
> n.lrn <- length(y.lrn)
> n.val <- length(y.val)
> n.tst <- length(y.tst)
> n     <- length(y)
> 
> rm(target.lrn,target.val,target.tst)
> 
> wts <- mat.or.vec(n,1) ; for (i in 1:n.lrn) wts[i]=1
> idx.lrn <- 1:n.lrn
> idx.val <- (n.lrn+1):(n.lrn+n.val)
> idx.tst <- (n.lrn+n.val+1):n
> 
> mod <- read.table("cty_usa.txt",
+          header=F,colClasses="character",col.names=c("file","feature","type"))
> 
> n.mod <- length(mod$file)
> 
> first.time <- TRUE
> 
> for (i in 1:n.mod) {
+ 
+   fn.lrn <- paste("../lrn/",mod$type[i],"/",mod$file[i],".dat",sep="")
+   fn.val <- paste("../val/",mod$type[i],"/",mod$file[i],".dat",sep="")
+   fn.tst <- paste("../tst/",mod$type[i],"/",mod$file[i],".dat",sep="")
+   
+   print(mod$feature[i])
+ 
+   if (mod$type[i]=="chr") {
+ 
+     f.lrn <- read.table(fn.lrn,
+                header=T,colClasses="character",blank.lines.skip=F)
+     f.val <- read.table(fn.val,
+                header=T,colClasses="character",blank.lines.skip=F)
+     f.tst <- read.table(fn.tst,
+                header=T,colClasses="character",blank.lines.skip=F)
+ 
+     f <- c(f.lrn[,1],f.val[,1],f.tst[,1])
+ 
+     if (mod$feature[i]=="STATE") {
+       f[f=="AS"|f=="DC"|f=="DE"|f=="MA"|f=="ME"|f=="NH"] <- "S1"
+       f[f=="OH"|f=="RI"|f=="VI"|f=="WV"]                 <- "S1"
+       f[f=="AA"|f=="AE"|f=="AP"|f=="CT"|f=="GU"|f=="MD"] <- "S2"
+       f[f=="NJ"|f=="NY"|f=="PA"|f=="PA"|f=="VA"|f=="VT"] <- "S2"
+       f[f=="WY"]                                         <- "S2"  
+       f[f=="AK"|f=="UT"|f=="MS"]                         <- "S3"
+       f[f=="NE"|f=="ND"]                                 <- "S4"
+       f[f=="SD"|f=="SC"]                                 <- "S5"
+     } 
+     
+     f <- as.factor(f)
+ 
+     n.lev <- nlevels(f)
+     f.name <- levels(f) 
+     if(n.lev==2) f.name <- c(mod$feature[i],mod$feature[i])
+     print(paste("  nlevels = ",n.lev))
+ 
+     f <- model.matrix(y ~ f - 1)      # Note: Intercept removed.
+     f <- f[,2:n.lev]                  # Note: First dummy deleted.
+     f.name <- f.name[2:n.lev]         # Note: First name deleted.
+ 
+   } else {
+     
+     f.lrn<-read.table(fn.lrn,
+              header=T,colClasses="numeric",blank.lines.skip=F)
+     f.val<-read.table(fn.val,
+              header=T,colClasses="numeric",blank.lines.skip=F)
+     f.tst<-read.table(fn.tst,
+              header=T,colClasses="numeric",blank.lines.skip=F)
+ 
+     f <- c(f.lrn[,1],f.val[,1],f.tst[,1])
+ 
+     f[is.na(f)] <- 0
+ 
+     f.name <- mod$feature[i]
+ 
+     if (mod$feature[i]=="DOB") { 
+       d <- f ; d[d>0] <- 1         # Note: Dummy for missing DOB
+       f <- cbind(d,f,f^2)          # Note: Quadratic term added to DOB.
+       rm(d)
+       f.name <- c("DOB_miss","DOB_linr","DOB_quad")
+     }
+ 
+   }
+ 
+   if (first.time) {
+     X <- f
+     X.names <- f.name
+     first.time <- FALSE
+   } else {
+     X <- cbind(prev.X,f)
+     X.names <- c(prev.X.names,f.name)
+   }
+ 
+   prev.X <- X
+   prev.X.names <- X.names
+ 
+ }
[1] "LASTGIFT"
[1] "PEPSTRFL"
[1] "  nlevels =  2"
[1] "LON"
[1] "LONSQR"
[1] "LAT"
[1] "LATSQR"
[1] "LONLAT"
[1] "HI"
[1] "AK"
[1] "TERRITORY"
[1] "RECP3"
[1] "  nlevels =  2"
[1] "DOB"
[1] "MAILCODE"
[1] "  nlevels =  2"
[1] "MHUC2"
[1] "LASTDATE"
[1] "MINRAMNT"
> 
> LAST.by.PEP <- X[,1]*X[,2]
> 
> X <- cbind(prev.X,LAST.by.PEP)
> X.names <- c(prev.X.names,"LAST.by.PEP")
> 
> rm(prev.X,prev.X.names)
> rm(f.lrn,f.val,f)
> 
> dimnames(X) <- list(NULL,X.names)
> 
> fit <- lm(y~X,weights=wts)
> aov <- anova.lm(fit) 
> 
> ehat <- fit$residuals
> mse.lrn <- sum(ehat[idx.lrn]^2)/n.lrn 
> mse.val <- sum(ehat[idx.val]^2)/n.val
> mse.tst <- sum(ehat[idx.tst]^2)/n.tst
> 
> print(paste("  rank    = ",fit$rank))
[1] "  rank    =  20"
> print(paste("  P-value = ",aov[1,5]))
[1] "  P-value =  1.00783101919317e-115"
> print(paste("  mse.lrn = ",mse.lrn)) 
[1] "  mse.lrn =  19.9187451580094"
> print(paste("  mse.val = ",mse.val)) 
[1] "  mse.val =  18.6023779400238"
> print(paste("  mse.tst = ",mse.tst)) 
[1] "  mse.tst =  17.7922581965671"
> 
> print(summary.lm(fit))

Call:
lm(formula = y ~ X, weights = wts)

Residuals:
     Min       1Q   Median       3Q      Max 
-21.9483  -0.8104  -0.5646   0.0000 199.2596 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -1.948e+01  3.419e+00  -5.697 1.22e-08 ***
XLASTGIFT     7.585e-03  1.767e-03   4.293 1.76e-05 ***
XPEPSTRFL    -3.310e-01  5.560e-02  -5.954 2.63e-09 ***
XLON          3.689e-02  1.479e-02   2.494 0.012643 *  
XLONSQR       2.273e-04  8.765e-05   2.594 0.009501 ** 
XLAT          8.273e-02  3.697e-02   2.238 0.025232 *  
XLATSQR      -1.108e-03  6.815e-04  -1.626 0.103866    
XLONLAT       5.561e-05  3.135e-04   0.177 0.859209    
XHI           3.925e-01  4.744e-01   0.827 0.407984    
XAK          -4.635e-01  5.133e-01  -0.903 0.366626    
XTERRITORY    6.006e-01  6.217e-01   0.966 0.334015    
XRECP3        6.033e-01  1.224e-01   4.927 8.36e-07 ***
XDOB_miss    -2.166e-01  9.623e-02  -2.251 0.024361 *  
XDOB_linr     1.882e-04  5.198e-05   3.621 0.000293 ***
XDOB_quad    -2.552e-08  6.782e-09  -3.762 0.000169 ***
XMAILCODE    -4.341e-01  1.450e-01  -2.993 0.002762 ** 
XMHUC2        5.983e-02  2.036e-02   2.939 0.003299 ** 
XLASTDATE     2.084e-03  3.556e-04   5.862 4.60e-09 ***
XMINRAMNT     3.760e-03  2.491e-03   1.509 0.131239    
XLAST.by.PEP  3.474e-02  2.689e-03  12.920  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 4.464 on 66880 degrees of freedom
Multiple R-Squared: 0.008979,	Adjusted R-squared: 0.008698 
F-statistic: 31.89 on 19 and 66880 DF,  p-value: < 2.2e-16 

> 
> outvec <- fit$fitted.values[idx.lrn]
> outlab <- "yhat.lrn"
> outfil <- "yhat_lrn_usa.dat"
> write(c(outlab,formatC(outvec,digits=17,width=26,format="e")),file=outfil)
> 
> outvec <- fit$fitted.values[idx.val]
> outlab <- "yhat.val"
> outfil <- "yhat_val_usa.dat"
> write(c(outlab,formatC(outvec,digits=17,width=26,format="e")),file=outfil)
> 
> outvec <- fit$fitted.values[idx.tst]
> outlab <- "yhat.tst"
> outfil <- "yhat_tst_usa.dat"
> write(c(outlab,formatC(outvec,digits=17,width=26,format="e")),file=outfil)
> 
> rm(outvec,outlab,outfil)
> 
> x0 <- c(0,n.lrn)
> y0 <- c(0,sum(y.lrn-0.68))
> 
> x1 <- (1:n.lrn)
> y1 <- fit$fitted.values[1:n.lrn]
> y1 <- y1-0.68
> y1 <- sort(y1)
> y1 <- y1[n.lrn:1]
> y1 <- cumsum(y1)
> 
> idx <- 1:n.lrn
> 
> print(paste("maximum profit in learning sample is ", max(y1)))
[1] "maximum profit in learning sample is  12055.5708077004"
> print(paste("maximum occurs at ", idx[y1==max(y1)]))
[1] "maximum occurs at  38159"
> 
> idx <- seq(1,n.lrn,length=200)
> 
> x1 <- x1[idx]
> y1 <- y1[idx]
> 
> source("psopts.r");
> postscript(file="cty_usa_lif.eps");
> 
> plot(x=c(x0,x1),y=c(y0,y1),ylab="dollars",xlab="size of mailing",type="n")
> lines(x=x0,y=y0,col="green")
> lines(x=x1,y=y1,col="red")
> 
> dev.off()
null device 
          1 
> proc.time()
[1] 39.57  4.59 50.93  0.00  0.01
>