durban.splitplot {agridat} | R Documentation

## Split-plot barley variety trial with fungicide treatments

### Description

Split-plot barley variety trial with fungicide treatments.

### Format

A data frame with 560 observations on the following 6 variables.

`yield`

  yield, tonnes/ha

`block`

  block, 4 levels

`gen`

  genotype, 70 levels

`fung`

  fungicide, 2 levels

`row`

  row

`bed`

  bed (column)

### Details

Grown in 1995-1996 at the Scottish Crop Research Institute. Split-plot design with 4 blocks, 2 whole-plot fungicide treatments, and 70 barley varieties or variety mixes. Total area was 10 rows (north/south) by 56 beds (east/west).

### 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.splitplot)
    dat <- durban.splitplot

    if(require(desplot)){
      desplot(yield~bed*row, dat,
              out1=block, out2=fung, num=gen, # aspect unknown
              main="durban.splitplot")
    }

    # Durban 2003, Figure 2
    m20 <- lm(yield~gen*fung, data=dat)
    dat$resid <- m20$resid
    ## require(lattice)
    ## xyplot(resid~row, dat, type=c('p','smooth'), main="durban.splitplot")
    ## xyplot(resid~bed, dat, type=c('p','smooth'), main="durban.splitplot")

    # Figure 4 doesn't quite match due to different break points
    if(require(lattice)){
      xyplot(resid ~ bed|factor(row), data=dat,
             main="durban.splitplot",
             type=c('p','smooth'))
    }

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

    ## Not run:
      # Figure 6 - field trend
      # note, Durban used gam package like this
      # m2lo <- gam(yield ~ gen*fung + lo(row, bed, span=.082), data=dat)
      require(mgcv)
      m2lo <- gam(yield ~ gen*fung + s(row, bed,k=45), data=dat)
      new2 <- expand.grid(row=unique(dat$row), bed=unique(dat$bed))
      new2 <- cbind(new2, gen="G01", fung="F1")
      p2lo <- predict(m2lo, newdata=new2)
      require(lattice)
      wireframe(p2lo~row+bed, new2, aspect=c(1,.5), main="Field trend")

    ## End(Not run)

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

    ## Not run:
      # asreml3
      require(asreml)
      # Table 5, variance components.  Table 6, F tests

      dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
      dat <- dat[order(dat$rowf, dat$bedf),]
      m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
                     rcov=~ar1v(rowf):ar1(bedf))
      m2a2 <- update(m2a2)

      require(lucid)
      vc(m2a2)
      ##                effect component std.error z.ratio constr
      ##       block!block.var 0.0000001        NA      NA  bound
      ##  block:fung!block.var 0.01207    0.01513      0.8    pos
      ##       units!units.var 0.02463    0.002465    10      pos
      ##            R!variance 1                NA      NA    fix
      ##            R!rowf.cor 0.8836     0.03647     24    uncon
      ##            R!rowf.var 0.1262     0.04432      2.8    pos
      ##            R!bedf.cor 0.9202     0.02847     32    uncon

      anova(m2a2)

    ## End(Not run)

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

    ## Not run:
      ## require(asreml4)
      ## # Table 5, variance components.  Table 6, F tests

      ## dat <- transform(dat, rowf=factor(row), bedf=factor(bed))
      ## dat <- dat[order(dat$rowf, dat$bedf),]
      ## m2a2 <- asreml(yield ~ gen*fung, random=~block/fung+units, data=dat,
      ##                resid =~ar1v(rowf):ar1(bedf))
      ## m2a2 <- update(m2a2)

      ## require(lucid)
      ## vc(m2a2)
      ## ##             effect component std.error z.ratio bound
      ## ##              block   0              NA      NA     B  NA
      ## ##         block:fung   0.01206  0.01512      0.8     P   0
      ## ##              units   0.02463  0.002465    10       P   0
      ## ##       rowf:bedf(R)   1              NA      NA     F   0
      ## ## rowf:bedf!rowf!cor   0.8836   0.03646     24       U   0
      ## ## rowf:bedf!rowf!var   0.1261   0.04434      2.8     P   0
      ## ## rowf:bedf!bedf!cor   0.9202   0.02846     32       U   0

      ## wald(m2a2)

    ## End(Not run)

[Package agridat version 1.16 Index]