gilmour.slatehall {agridat} | R Documentation

## Slate Hall Farm 1978

### Description

Yields for a trial at Slate Hall Farm in 1978.

### Format

A data frame with 150 observations on the following 5 variables.

`row`

  row

`col`

  column

`yield`

  yield (grams/plot)

`gen`

  genotype factor, 25 levels

`rep`

  rep factor, 6 levels

### Details

The trial was of spring wheat at Slate Hall Farm in 1978. The experiment was a balanced lattice with 25 varieties in 6 replicates. The 'rep' labels are arbitrary (no rep labels appeared in the source data). Each row within a rep is an incomplete block. The plot size was 1.5 meters by 4 meters.

Field width: 10 plots x 4 m = 40 m

Field length: 15 plots x 1.5 meters = 22.5 m

### Source

Arthur R Gilmour and Brian R Cullis and Arunas P Verbyla (1997). Accounting for natural and extraneous variation in the analysis of field experiments. Journal of Agricultural, Biological, and Environmental Statistics, 2, 269-293. http://doi.org/10.2307/1400446

### References

None.

### Examples

    data(gilmour.slatehall)
    dat <- gilmour.slatehall

    if(require(desplot)){
      desplot(yield ~ col * row, dat,
              aspect=22.5/40, num=gen, out1=rep, cex=1,
              main="gilmour.slatehall")
    }

    # ----------------------------------------------------------------------------

    ## Not run:
      # Model 4 of Gilmour et al 1997
      # asreml3
      require(asreml)
      dat <- transform(dat, xf=factor(col), yf=factor(row))
      dat <- dat[order(dat$xf, dat$yf), ]
      m4 <- asreml(yield ~ gen + lin(row), data=dat,
                   random = ~ dev(row) + dev(col),
                   rcov = ~ ar1(xf):ar1(yf))
      coef(m4)$fixed[1] # linear row
      # [1] 31.72252 # (sign switch due to row ordering)

      require(lucid)
      vc(m4)
      ##      effect component std.error z.ratio constr
      ##    dev(row) 20290     10260         2      pos
      ##    dev(col)  2519      1959         1.3    pos
      ##  R!variance 23950      4616         5.2    pos
      ##    R!xf.cor     0.439     0.113     3.9  uncon
      ##    R!yf.cor     0.125     0.117     1.1  uncon

      plot(variogram(m4))

    ## End(Not run)

    # ----------------------------------------------------------------------------

    ## Not run:
      # Model 4 of Gilmour et al 1997
      ## require(asreml4)
      ## dat <- transform(dat, xf=factor(col), yf=factor(row))
      ## dat <- dat[order(dat$xf, dat$yf), ]
      ## m4 <- asreml(yield ~ gen + lin(row), data=dat,
      ##              random = ~ dev(row) + dev(col),
      ##              resid = ~ ar1(xf):ar1(yf))
      ## coef(m4)$fixed[1] # linear row
      ## # [1] 31.72252 # (sign switch due to row ordering)

      ## require(lucid)
      ## vc(m4)
      ## ##       effect component std.error z.ratio bound
      ## ##     dev(col)  2519      1959         1.3     P   0
      ## ##     dev(row) 20290     10260         2       P   0
      ## ##     xf:yf(R) 23950      4616         5.2     P   0
      ## ## xf:yf!xf!cor     0.439     0.113     3.9     U   0
      ## ## xf:yf!yf!cor     0.125     0.117     1.1     U   0

      ## plot(varioGram(m4))

    ## End(Not run)

[Package agridat version 1.16 Index]