R version 2.10.1 (2009-12-14)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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[R.app GUI 1.31 (5537) x86_64-apple-darwin9.8.0]

> install.packages('UsingR')
--- Please select a CRAN mirror for use in this session ---
trying URL 'http://www.revolution-computing.com/cran/bin/macosx/leopard/contrib/2.10/UsingR_0.1-12.tgz'
Content type 'application/x-gzip' length 1143093 bytes (1.1 Mb)
opened URL
==================================================
downloaded 1.1 Mb


The downloaded packages are in
	/var/folders/vX/vXSkZhgYGjmQee019Jt4OU+++TY/-Tmp-//RtmpE0JDzT/downloaded_packages
> library(UsingR)

> x=homedata$y1970
> y=homedata$y2000
> plot(x,y)
> cor(x,y)
[1] 0.8962155
> help(cor)
starting httpd help server ... done
> cor(x,y,method='pearson')
[1] 0.8962155
> cor(x,y,method='spearman')
[1] 0.8878185
> sum( ((x-mean(x))/sd(x)) * ((y-mean(y))/sd(y)) ) / (length(x)-1)
[1] 0.8962155
> mean( ((x-mean(x))/sd(x)) * ((y-mean(y))/sd(y)) )
[1] 0.8960845
> mean( ((x-mean(x))/sd(x)) * ((y-mean(y))/sd(y)) )
[1] 0.8960845
> cor(x,y,method='kendall')
[1] 0.7240265

> x=rnorm(1000)   
> y=rnorm(1000)
> plot(x,y)
> par(mfrow=c(2,1))     
> plot(x,y)         
> qqplot(x,y)     
> y=cfb$VEHIC  
> hist(x)
> hist(y)
> par(mfrow=c(1,1))        
> qqplot(x,y)

> x=jitter(1:100,50)
> y=jitter(1:100,50)
> plot(x,y)
> model=lm(y~x)
> abline(model)
> coef(model)
(Intercept)           x 
  1.9966944   0.9653584 
> params=coef(model)
> names(params)=c('b','m')
> params
        b         m 
1.9966944 0.9653584 
> x=homedata$y1970
> y=homedata$y2000
> plot(x,y)
> model=lm(y~x)
> abline(model)
> z=identify(x,y,n=1)
> c(x[6655],y[6655])
[1] 149700 360000
> c(x[z],y[z])
[1] 149700 360000
> params=coef(model)   
> names(params)=c('b','m')
> predicted=params['m']*x[z] + params['b']
> predicted
       m 
683113.7 
> params
            b             m 
-1.040047e+05  5.257972e+00 
> predict(model,data.frame(x=x[z]))
       1 
683113.7 
> y[z]-predicted
        m 
-323113.7 
> residuals(model)[x==x[z]]   
      4742       6655 
 -73913.73 -323113.73 
> sum(residuals(model)^2)   
[1] 2.300683e+13
> sum(residuals(model)^2)/length(x)
[1] 3363079303
> sum( (y-predict(model,data.frame(x)))^2 )
[1] 2.300683e+13
> x=jitter(1:100,50)   
> y=jitter((1:100)^2,50)
> plot(x,y)
> model=lm(y~x)
> abline(model)
> sum(residuals(model)^2)     
[1] 92459073
> sum(residuals(model)^2)/length(x)
[1] 924590.7
> y2=y      
> y2[y2<0]=0
> plot(x,sqrt(y2))
> model = lm(sqrt(y2)~x)
> abline(model)
> sum(residuals(model)^2)/length(x)
[1] 38.5025
> sum( (y - predict(model,data.frame(x))^2 )^2 )/length(x)
[1] 426696.3
> x=emissions$perCapita
> y=emissions$CO2
> plot(x,y)
> model=lm(y~x)
> abline(model)
> model=lm(y~x,subset=-1)
> abline(model,lty=2)
> library(MASS)
> abline(lqs(y~x,data=emissions),lty=3)    
> abline(rlm(y~x,data=emissions,method="MM"),lty=5)
> x=five.yr.temperature$days
> y=five.yr.temperature$temps
> plot(x,y)
> scatter.smooth(y~x,col='gray')   
> lines(smooth.spline(y~x),col='blue')
> lines(supsmu(x,y),col='red')
> legend(80,lty=rep(1,3),col=c('black','blue','red'),legend=c('scatter.smooth','smooth.spline','supsmu'))