data("burgueno.rowcol")

## Not run: 

  library(agridat)
  data(burgueno.rowcol)
  dat <- burgueno.rowcol

  # Two contiguous reps in 8 rows, 16 columns
  libs(desplot)
  desplot(dat, yield ~ col*row,
          out1=rep, # aspect unknown
          text=gen, shorten="none", cex=.75,
          main="burgueno.rowcol")

  libs(lme4,lucid)
  
  # Random rep, row and col within rep
  # m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat)
  # vc(m1) # Match components of Burgueno p. 40
  ##      grp        var1 var2   vcov  sdcor
  ##  rep:col (Intercept) <NA> 0.2189 0.4679
  ##  rep:row (Intercept) <NA> 0.1646 0.4057
  ##      rep (Intercept) <NA> 0.1916 0.4378
  ## Residual        <NA> <NA> 0.1796 0.4238
  
  libs(asreml) # asreml4

  # AR1 x AR1 with linear row/col effects, random spline row/col
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  dat <- dat[order(dat$xf,dat$yf),]
  m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat,
               random = ~ spl(yf) + spl(xf),
               resid = ~ ar1(xf):ar1(yf))
  m2 <- update(m2) # More iterations
  
  # Scaling of spl components has changed in asreml from old versions
  libs(lucid)
  vc(m2) # Match Burgueno p. 42
  ##       effect component std.error z.ratio bound 
  ##      spl(yf)  0.09077    0.08252   1.1       P 0
  ##      spl(xf)  0.08107    0.08209   0.99      P 0
  ##     xf:yf(R)  0.1482     0.03119   4.8       P 0
  ## xf:yf!xf!cor  0.1152     0.2269    0.51      U 0.1
  ## xf:yf!yf!cor  0.009467   0.2414    0.039     U 0.9
  
  plot(varioGram(m2), main="burgueno.rowcol")


## End(Not run)