Row-column design

Row-column design

Usage

data("burgueno.rowcol")

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.

Electronic version of the data obtained from CropStat software.

Used with permission of Juan Burgueno.

Source

J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis (2000). User's guide for spatial analysis of field variety trials using ASREML. CIMMYT.

Examples

if (FALSE) { # \dontrun{

  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
  
  if(require("asreml", quietly=TRUE)) {
    libs(asreml,lucid)
    
    # 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
    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")
  }
  
} # }