R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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>  # slr1.r
>  # first probe on SimStudy problem 2
>  # normality of L1 regression
>  #
>   y <- c(4.5,5.5,6.5,8,10,12)
>   x <- (1:6)
>  #
>   g <- function(b) {
+          res <- y - b*x
+          med <- median(res)
+          g <- sum(abs(res-med)) }
>  # 
>  # simple linear regression
>   slr <- function(y,x) {
+     xb <- mean(x)
+     yb <- mean(y)
+     sxx <- sum((x-xb)*(x-xb))
+     b1 <- sum((x-xb)*(y-yb))/sxx
+     b0 <- yb - b1*xb
+     sse <- sum((y-b0-b1*x)*(y-b0-b1*x))
+     slr <- c(b1,sqrt(sse/((length(x)-2)*sxx)))   }
>  #
>     rslt <- slr(y,x)
>     rslt
[1] 1.5000000 0.1195229
>     int <- c(rslt[1]-4*rslt[2],rslt[1]+4*rslt[2]) # +/- 4 se's
>     int
[1] 1.021909 1.978091
>  # use optimize on smoother function 
>     this <- optimize(g,int)
>     this
$minimum
[1] 1.5

$objective
[1] 2

>  # done
>   rm(list=ls())
>  q()