1 ## Birth data
   2 load("birthsraw.rdata")
   3 load("births.rdata")
   4 
   5 
   6 births <- transform(births,
   7       lowbw = factor(lowbw, labels=c("normal","low")),
   8       preterm = factor(preterm, labels=c("normal","preterm")),
   9       hyp = factor(hyp, labels=c("normal","hyper")),
  10       sex = factor(sex, labels=c("M","F")),
  11       gest4 = cut(gestwks, breaks=c(20,35,37,39,45),
  12       right=F))
  13 
  14 
  15 ## relationship bweight ~ gestwks
  16 m <- lm(bweight ~ gestwks, data=births)
  17 
  18 summary(m)
  19 coef(m)
  20 confint(m)
  21 
  22 
  23 ## relationship bweight ~ hyp
  24 m <- lm(bweight ~ hyp, data=births)
  25 summary(m)
  26 coef(m)
  27 confint(m)
  28 
  29 
  30 
  31 ## relationship bweight ~ hyp + gestwks
  32 m <- lm(bweight ~ hyp + gestwks, data=births)
  33 summary(m)
  34 coef(m)
  35 confint(m)
  36 
  37 
  38 ## relationship bweight ~ hyp * gestwks
  39 m <- lm(bweight ~ hyp + gestwks, data=births)
  40 summary(m)
  41 coef(m)
  42 confint(m)
  43 
  44 ## center variable to get meaningful intercept
  45 births$gwsc <- births$gestwks-40
  46 m <- lm(bweight ~ hyp * gwsc, data=births)
  47 summary(m)
  48 coef(m)
  49 confint(m)
  50