kempton.barley.uniformity {agridat} | R Documentation

## Uniformity trial of barley

### Description

Uniformity trial of barley at Cambridge, England, 1978.

### Format

A data frame with 196 observations on the following 3 variables.

`row`

  row

`col`

  column

`yield`

  grain yield, kg

### Details

A uniformity trial of spring barley planted in 1978. Conducted by the Plant Breeding Institute in Cambridge, England.

Each plot is 5 feet wide, 14 feet long.

Field width: 7 plots * 14 feet = 98 feet

Field length: 28 plots * 5 feet = 140 feet

### Source

R. A. Kempton and C. W. Howes (1981). The use of neighbouring plot values in the analysis of variety trials. Applied Statistics, 30, 59–70. http://doi.org/10.2307/2346657

### References

McCullagh, P. and Clifford, D., (2006). Evidence for conformal invariance of crop yields, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science. 462, 2119–2143.

### Examples

    data(kempton.barley.uniformity)
    dat <- kempton.barley.uniformity

    if(require(desplot)){
      desplot(yield~col*row, dat,
              aspect=140/98, tick=TRUE, # true aspect
              main="kempton.barley.uniformity")
    }

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

    ## Not run:

      # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91

      dat <- transform(dat, xf = factor(col), yf=factor(row))
      # asreml3
      require(asreml)
      m1 <- asreml(yield ~ 1, data=dat, rcov=~ar1(xf):ar1(yf))

      require(lucid)
      vc(m1)
      ##      effect component std.error z.ratio constr
      ##  R!variance    0.1044   0.022       4.7    pos
      ##    R!xf.cor    0.2458   0.07484     3.3  uncon
      ##    R!yf.cor    0.8186   0.03822    21    uncon

      # asreml estimates auto-regression correlations of 0.25, 0.82
      # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91

    ## End(Not run)

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

    ## Not run:

      # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91

      ## dat <- transform(dat, xf = factor(col), yf=factor(row))
      ## require(asreml4)
      ## m1 <- asreml(yield ~ 1, data=dat, residual = ~ar1(xf):ar1(yf))

      ## require(lucid)
      ## vc(m1)
      ## ##       effect component std.error z.ratio bound
      ## ##     xf:yf(R)    0.1044   0.022       4.7     P   0
      ## ## xf:yf!xf!cor    0.2458   0.07484     3.3     U   0
      ## ## xf:yf!yf!cor    0.8186   0.03822    21       U   0

      ## # asreml estimates auto-regression correlations of 0.25, 0.82
      ## # Kempton estimated auto-regression coefficients b1=0.10, b2=0.91

    ## End(Not run)

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

    # Kempton defines 4 blocks, randomly assigns variety codes 1-49 in each block, fits
    # RCB model, computes mean squares for variety and residual.  Repeat 40 times.
    # Kempton's estimate: variety = 1032, residual = 1013
    # Our estimate: variety = 825, residual = 1080
    fitfun <- function(dat){
      dat <- transform(dat, block=factor(ceiling(row/7)),
                       gen=factor(c(sample(1:49),sample(1:49),sample(1:49),sample(1:49))))
      m2 <- lm(yield*100 ~ block + gen, dat)
      anova(m2)[2:3,'Mean Sq']
    }
    set.seed(251)
    out <- replicate(50, fitfun(dat))
    rowMeans(out) # 826 1079

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

[Package agridat version 1.16 Index]