R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(58.972,59.249,63.955,53.785,52.760,44.795,37.348,32.370,32.717,40.974,33.591,21.124,58.608,46.865,51.378,46.235,47.206,45.382,41.227,33.795,31.295,42.625,33.625,21.538,56.421,53.152,53.536,52.408,41.454,38.271,35.306,26.414,31.917,38.030,27.534,18.387,50.556,43.901,48.572,43.899,37.532,40.357,35.489,29.027,34.485,42.598,30.306,26.451,47.460,50.104,61.465,53.726,39.477,43.895,31.481,29.896,33.842,39.120,33.702,25.094)
> x <- c(493.000,481.000,462.000,457.000,442.000,439.000,488.000,521.000,501.000,485.000,464.000,460.000,467.000,460.000,448.000,443.000,436.000,431.000,484.000,510.000,513.000,503.000,471.000,471.000,476.000,475.000,470.000,461.000,455.000,456.000,517.000,525.000,523.000,519.000,509.000,512.000,519.000,517.000,510.000,509.000,501.000,507.000,569.000,580.000,578.000,565.000,547.000,555.000,562.000,561.000,555.000,544.000,537.000,543.000,594.000,611.000,613.000,611.000,594.000,595.000)
> par7 = 'Y'
> par6 = 'Y'
> par5 = '0'
> par4 = '0'
> par3 = '0'
> par2 = '50'
> par1 = '50'
> ylab = 'y'
> xlab = 'x'
> main = 'Bivariate Kernel Density'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: WESSA, (2007), Bivariate Kernel Density Estimation (v1.0.5) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_bidensity.wasp/
> #Source of accompanying publication: Lucy, D. Aykroyd, R.G. & Pollard, A.M.(2002) Non-parametric calibration for age estimation. Applied Statistics 51(2): 183-196
> #Technical description: This code makes use of the GenKern package. The dataset is based on http://exploringdata.cqu.edu.au/datasets/chirps.tab : x = temperature (degrees F) and y = chirps per second of crickets (insects).
> par1 <- as(par1,'numeric')
> par2 <- as(par2,'numeric')
> par3 <- as(par3,'numeric')
> par4 <- as(par4,'numeric')
> par5 <- as(par5,'numeric')
> library('GenKern')
Loading required package: KernSmooth
KernSmooth 2.22 installed
Copyright M. P. Wand 1997

Loading GenKern version 1.1
Copyright Lucy and Aykroyd 2000
last update October 2004
requires KernSmooth


Package GenKern installed

> if (par3==0) par3 <- dpik(x)
> if (par4==0) par4 <- dpik(y)
> if (par5==0) par5 <- cor(x,y)
> if (par1 > 500) par1 <- 500
> if (par2 > 500) par2 <- 500
> postscript(file="/var/www/html/rcomp/tmp/1hztk1228471307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=par5, xbandwidth=par3, ybandwidth=par4)
> image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main=main,xlab=xlab,ylab=ylab)
> if (par6=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
> if (par7=='Y') points(x,y)
> (r<-lm(y ~ x))

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
   81.34739     -0.07921  

> abline(r)
> box()
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Bandwidth',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'x axis',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'y axis',header=TRUE)
> a<-table.element(a,par4)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Correlation',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'correlation used in KDE',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'correlation(x,y)',header=TRUE)
> a<-table.element(a,cor(x,y))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/22fna1228471307.tab") 
> 
> system("convert tmp/1hztk1228471307.ps tmp/1hztk1228471307.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.586   0.262   0.670