durban.rowcol {agridat} | R Documentation

## Row column design of a spring barley trial with many varieties

### Description

Row column design of a spring barley trial with many varieties

### Format

A data frame with 544 observations on the following 5 variables.

`row`

  row

`bed`

  bed (column)

`rep`

  rep, 2 levels

`gen`

  genotype, 272 levels

`yield`

  yield, tonnes/ha

### Details

Spring barley variety trial of 272 entries (260 new varieties, 12 control). Grown at the Scottish Crop Research Institute in 1998. Row-column design with 2 reps, 16 rows (north/south) by 34 beds (east/west). The land sloped downward from row 16 to row 1. Plot yields were converted to tonnes per hectare.

Plot dimensions are not given.

### Source

Durban, Maria and Hackett, Christine and McNicol, James and Newton, Adrian and Thomas, William and Currie, Iain. 2003. The practical use of semiparametric models in field trials, Journal of Agric Biological and Envir Stats, 8, 48-66. http://doi.org/10.1198/1085711031265

Retrieved from: ftp://ftp.bioss.sari.ac.uk/pub/maria

Used with permission of Maria Durban.

### Examples

    data(durban.rowcol)
    dat <- durban.rowcol

    if(require(desplot)){
      desplot(yield~bed*row, dat,
              out1=rep, num=gen, # aspect unknown
              main="durban.rowcol")
    }

    # Durban 2003 Figure 1
    m10 <- lm(yield~gen, data=dat)
    dat$resid <- m10$resid
    ## require(lattice)
    ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.rowcol")
    ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.rowcol")

    # Figure 3
    if(require(lattice)){
      xyplot(resid ~ bed|factor(row), data=dat,
             main="durban.rowcol",
             type=c('p','smooth'))
    }

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

    ## Not run:
      # Figure 5 - field trend
      # note, Durban used gam package like this
      # m1lo <- gam(yield ~ gen + lo(row, span=10/16) + lo(bed, span=9/34), data=dat)
      require(mgcv)
      m1lo <- gam(yield ~ gen + s(row) + s(bed, k=5), data=dat)
      new1 <- expand.grid(row=unique(dat$row),bed=unique(dat$bed))
      new1 <- cbind(new1, gen="G001")
      p1lo <- predict(m1lo, newdata=new1)
      require(lattice)
      wireframe(p1lo~row+bed, new1, aspect=c(1,.5), main="Field trend") # Figure 5

    ## End(Not run)

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

    ## Not run:
      # Figure 7 - variograms

      # asreml3
      require(asreml)
      dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
      dat <- dat[order(dat$rowf, dat$bedf),]

      m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
                     random=~spl(rowf) + spl(bedf) + units,
                     family=asreml.gaussian(dispersion=1))
      m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
                     random=~spl(rowf) + spl(bedf) + units, rcov=~ar1(rowf):ar1(bedf))
      m1a3 <- asreml(yield~gen, data=dat, random=~units, rcov=~ar1(rowf):ar1(bedf))

      require(lattice)
      v7a <- asreml.variogram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals)
      wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a

      v7b <- asreml.variogram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals)
      wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b

      v7c <- asreml.variogram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals)
      wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c

    ## End(Not run)

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

    ## Not run:
      ## require(asreml4)
      ## dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
      ## dat <- dat[order(dat$rowf, dat$bedf),]

      ## m1a1 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
      ##                random=~spl(rowf) + spl(bedf) + units,
      ##                family=asr_gaussian(dispersion=1))
      ## m1a2 <- asreml(yield~gen + lin(rowf) + lin(bedf), data=dat,
      ##                random=~spl(rowf) + spl(bedf) + units,
      ##                resid = ~ar1(rowf):ar1(bedf))
      ## m1a2 <- update(m1a2)
      ## m1a3 <- asreml(yield~gen, data=dat, random=~units,
      ##                resid = ~ar1(rowf):ar1(bedf))

      ## # Figure 7
      ## require(lattice)
      ## v7a <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a3$residuals)
      ## wireframe(gamma ~ x*y, v7a, aspect=c(1,.5)) # Fig 7a

      ## v7b <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1a2$residuals)
      ## wireframe(gamma ~ x*y, v7b, aspect=c(1,.5)) # Fig 7b

      ## v7c <- asr_varioGram(x=dat$bedf, y=dat$rowf, z=m1lo$residuals)
      ## wireframe(gamma ~ x*y, v7c, aspect=c(1,.5)) # Fig 7c

    ## End(Not run)

[Package agridat version 1.16 Index]