burgueno.rowcol {agridat} | R Documentation

## Row-column design

### Description

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.

### 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.

Electronic version of the data obtained from CropStat software.

Used with permission of Juan Burgueno.

### Examples

    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

    ## End(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")

    ## End(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
      ## ##      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)

[Package agridat version 1.16 Index]