Lecture2 <- function()
{
# Ex1()
# Ex2()  
# GenData() 
 Ex3()
  
  
}


# ============================================================================================================================================

Ex1 <- function()
{
 vals <- c(0.05,0.1,0.25,0.5,1,1.5,2,2.5,5)
 Ex1a(vals)

}

# ============================================================================================================================================

Ex1a <- function(periods)
{
 par(mfrow=c(3,3))
 xx <- seq(from=0,to=2*pi,length=100)
 for (ii in 1:length(periods))
  plot(xx,sin(xx/periods[ii]),main=periods[ii],type='l')

}

# ============================================================================================================================================

Ex2 <- function()
{
 Long <- NULL
 for (i in 1:1000)
  {
   Age <- 0; alive <- T
   # Continue incrementing age while the animal is still aline
   while(alive)
    {
     if (runif(1) < 0.3) 
      alive <- F
     else
      Age <- Age + 1 
    }
   # the age of the animl of the age before it died to the list 
   Long <- c(Long,Age) 
  }
 hist(Long) 

}

# ============================================================================================================================================

GenData <- function()
{
 FileName <- "K:\\Lect2.csv"
 set.seed(1)
 Npop <- 20
 Nsamp <- sample(c(1:8),Npop,replace=TRUE)
 print(Nsamp)
 IntC <- rep(0,length=Npop)
 Slope <- rnorm(Npop,3,0.5)
 print(Slope)
 Outs <- matrix(0,ncol=3,nrow=sum(Nsamp))
 
 Ipnt <- 0
 for (II in 1:Npop)
  for (JJ in 1:Nsamp[II])
   {
    Ipnt <- Ipnt + 1
    Outs[Ipnt,1] <- II
    Outs[Ipnt,2] <- JJ
    Outs[Ipnt,3] <- IntC[II] + Slope[II]*JJ+rnorm(1,0,1)
   }
 print(Outs)
 
 write.table(Outs,file=FileName,col.names=F,sep=',')
 
}

# ============================================================================================================================================

Ex3 <- function()
{

 # Read in the data
 FileName <- "I:\\Lect2.csv"
 TheData <- read.table(FileName,sep=',')
 Nmax <- max(TheData[,2])
 
 # Create a list of zero length
 Slopes <- NULL
 
 # Work through each animal in turn
 for (Ipop in 1:Nmax)
  {

   # Extract the data for this animal
   TheDat1 <- TheData[TheData[,2]==Ipop,]
   # If enough data
   if (length(TheDat1[,1]) > 1)
    {
     RegOut <- lm(TheDat1[,4]~TheDat1[,3])
     TheSlope <- coefficients(RegOut)[2]
     Slopes <- c(Slopes,TheSlope)
    }
  }
 hist(Slopes,xlab="Slope",main="")

}

# ============================================================================================================================================

Lecture2()