AndrewLJackson

rm(list=ls())
graphics.off()

# --------------------------------------------------------------------

# The Exponential decay function 
# http://en.wikipedia.org/wiki/Exponential_function

# a sequence of x-values from 0 to 1000
x <- seq(0, 100, length=100)

AndrewLJackson

X <- cbind(1:5, 6:10)

G <- 1:5


my.fun <- function(x,y){return(x+y)}


# ----------------------------------

AndrewLJackson

a <- list(x=rnorm(10), y=rnorm(10), xlab = "x", ylab = "y", col = "blue", pch = 19, bty = "L", cex =2)

do.call('plot', a)

AndrewLJackson

rm(list=ls())
graphics.off()
library(mnormt)
set.seed(1)

# start of notes on re-scaling
#if x and y are two correlated variables with covariance matrix S then
#let x.z and y.z be their independently z-score transformed values.

AndrewLJackson

# some data specifying the month and year
month <- c( "Sep-10", 
            "Oct-10",
            "Nov-10",
            "Jan-11")

# It would appear that strptime and related
# as.POSIXct functions require there to be a
# day specified. Here I prepend the month-year

AndrewLJackson

# example code to reclassify existing coded data

# some data 

mydata <- data.frame(orig.codes = c(1,1,1,2,2,3,4,4,5,6,7,7,7,8,8,9,10,10,10))


# a blank vector of NAs into which we will place the new codes
mydata$new.codes <- NA * numeric(length(mydata$orig.codes))

AndrewLJackson

print('hello world')