# November 22, 2005	Nonlinear fitting example


##  airline data

eg1 <- function() {
    
    yr <- seq(1949+1/24,by=1/12,length=96)
    airline <- scan("airline.dat")
    
    year <- yr-1949
    airline.log <- log10(airline)
    air.eg <- data.frame(year,airline.log)
    
    attach(air.eg)
    
    plot(year, airline.log, pch=16, main="log airline passengers" )
    
    air.st <- c(b1=2.0733, b2=.058117, b3=.1, b4=0)
    ans <- nls( airline.log ~ b1 + b2*year + b3*sin(2*pi*year+b4),
       data=air.eg, start=air.st, trace=T )
    
    summary(ans) -> tmp; print(tmp); bb <- coef(tmp)[,1]
    
    xx <- seq(0,8,l=401)
    xx <- year
    yy <- bb[1] + bb[2]*xx + bb[3]*sin(2*pi*xx+bb[4])
    
    lines(xx,yy,lwd=2,col=2); abline(bb[1:2],col=2,lwd=2,lty=2); browser()
}


#		logistic regression

eg2 <- function() {

   ua <- read.table("APPENC04.txt")
   good <- ifelse( ua$gpa > 3.0 ,1, 0 );  pairs( cbind(ua, good) )
   poor <- ifelse( ua$gpa < 2.0 ,1, 0 );  pairs( cbind(ua, poor) )

  a1 <- glm(formula = good ~ rank + act + class, family = gaussian, data = ua)
  a2 <- glm(formula = good ~ rank + act + class, family = binomial, data = ua)

  print(summary(a1)); print(anova(a1))
  print(summary(a2)); print(anova(a2))

   browser()
}



##    Poisson regression  (book example)

eg3 <- function() {

   lumber <- read.table("CH14TA14.txt", header=T )
   pairs(lumber)

   ls.print(lsfit( scale(lumber[,-1]),scale(lumber[,1])))

   a1 <- glm( num ~ . , data=lumber, family = poisson )
   print(a1); print(summary(a1)); print(anova(a1))

  browser()
}