1 load("gewichte.rdata")
   2 
   3 head(gewichte)
   4 
   5 ## how many rows, how many columns?
   6 
   7 
   8 
   9 ## how many persons? 
  10 
  11 
  12 
  13 ## what is the min/max/mean age/weight/gestational age?
  14 
  15 
  16 
  17 require(lme4)
  18 
  19 
  20 m0 <- lmer(wert ~ 1 + (1 | personennr), data = gewichte)
  21 
  22 require(arm)
  23 display(m0)
  24 
  25 mean(gewichte$wert)
  26 fixef(m0)
  27 
  28 m1 <- lmer(wert ~ age + (1 | personennr), data = gewichte)
  29 display(m1)
  30 
  31 ## difference to the linear model:
  32 m.lm <- lm(wert ~ age , data = gewichte)
  33 summary(m.lm)
  34 
  35 ## closer look at the mixed model
  36 coef(m1)
  37 
  38 coefficienten <- coef(m1)[[1]]
  39 names(coefficienten)[1] <- "intercept"
  40 
  41 
  42 require(ggplot2)
  43 ggplot() +
  44     geom_point(data = gewichte,aes( x = age, y = wert)) +
  45     geom_abline(data = coefficienten, aes( intercept = intercept, slope = age))
  46 
  47 ggsave("mixedmodel1.png")
  48 
  49 ## Exercises
  50 
  51 
  52 
  53 
  54 ## add sex
  55 
  56 
  57 
  58 
  59 ## add gestational age
  60