agridat (version 1.16)

burgueno.rowcol: Row-column design

Description

Row-column design

Usage

data("burgueno.rowcol")

Arguments

Format

A data frame with 128 observations on the following 5 variables.

rep

rep, 2 levels

row

row

col

column

gen

genotype, 64 levels

yield

yield, tons/ha

Details

A field experiment with two contiguous replicates in 8 rows, 16 columns.

The plot size is not given.

Examples

Run this code
# NOT RUN {
data(burgueno.rowcol)
dat <- burgueno.rowcol

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

# }
# NOT RUN {
  require(lme4)
  require(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  
# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  # asreml3
  require(asreml)
  # 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),
               rcov = ~ ar1(xf):ar1(yf))
  m2 <- update(m2) # More iterations

  # Scaling of spl components has changed in asreml from old versions
  require(lucid)
  vc(m2) # Match Burgueno p. 42
  ##      effect component std.error z.ratio constr
  ##     spl(yf)  0.09077    0.08252   1.1      pos
  ##     spl(xf)  0.08108    0.0821    0.99     pos
  ##  R!variance  0.1482     0.03119   4.8      pos
  ##    R!xf.cor  0.1152     0.2269    0.51   uncon
  ##    R!yf.cor  0.009436   0.2414    0.039  uncon
  ##   plot(variogram(m2), main="burgueno.rowcol")

# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  ## require(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
  ## require(lucid)
  ## vc(m2) # Match Burgueno p. 42
  ## ##       effect component std.error z.ratio bound <!-- %ch -->
  ## ##      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")

# }
# NOT RUN {
# }

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