1 set.seed(53)
   2 
   3 ## generate some faked up dependent variables
   4 nTrials <- 100
   5 nSubjects <- 100
   6 trials <- 1:nTrials
   7 trial <- rep(trials, nSubjects)
   8 ## 100 subjects
   9 subjects <- 1:nSubjects
  10 subject <- rep(subjects, rep(nTrials, nSubjects))
  11 ## each with an iq score
  12 iqs <- rnorm(nSubjects, mean=100, sd=15)
  13 iq <- rep(iqs, rep(nTrials, nSubjects))
  14 ## and with a random baseline
  15 baselines.rand <- as.numeric(scale(rnorm(nSubjects, mean=0, sd=20), scale=F))
  16 baseline.rand <- rep(baselines.rand, rep(nTrials, nSubjects))
  17 ## or a baseline that varies with IQ
  18 baselines.corr <- as.numeric(scale(rnorm(nSubjects, mean=0, sd=20) - iqs, scale=F))
  19 baseline.corr <- rep(baselines.corr, rep(nTrials, nSubjects))
  20 ## and a categorical variable
  21 conditions <- rep(c(0, 1), 50)
  22 condition <- rep(conditions, rep(100, 100))
  23 ## and some random noise
  24 noise <- rnorm(nTrials * nSubjects, mean=0, sd=5)
  25 
  26 
  27 ## independent variables are a function of the dependent variables
  28 log.RT.1 <-
  29   ## speed up over trials
  30   -.5 * log(trial) +
  31   ## higher IQ is faster
  32   -.1 * iq +
  33   ## second condition is slower
  34   10 * condition + 
  35   ## baseline for each subject
  36   baseline.rand +
  37   ## random noise
  38   noise
  39 
  40 log.RT.2 <-
  41   ## speed up over trials
  42   -.5 * log(trial) +
  43   ## higher IQ is faster
  44   -.1 * iq +
  45   ## second condition is slower
  46   10 * condition + 
  47   ## baseline for each subject
  48   baseline.corr +
  49   ## random noise
  50   noise
  51 
  52 d <- data.frame(subject, trial, iq, condition, log.RT.1, log.RT.2, baseline.rand, baseline.corr, noise)
  53 
  54 ## library(lattice)
  55 ## histogram(~ log.RT.1 | subject)
  56 ## histogram(~ log.RT.2 | subject)
  57 
  58 library(lme4)
  59 lm.1 <- lm(log.RT.1 ~ iq + condition + log(trial), data = d)
  60 lm.2 <- lm(log.RT.2 ~ iq + condition + log(trial), data = d)
  61 lmer.1 <- lmer(log.RT.1 ~ iq + condition + log(trial) + (1 | subject), data = d)
  62 lmer.2 <- lmer(log.RT.2 ~ iq + condition + log(trial) + (1 | subject), data = d)