durban.splitplot: Split-plot barley variety trial with fungicide treatments

Description Format Details Source Examples

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.

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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
coplot(resid~bed|row, data=dat, number=8, cex=.5,
       panel=function(x,y,...) panel.smooth(x,y,span=.4,...))
title("durban.splitplot")

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

## 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)
  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)

agridat documentation built on Nov. 30, 2017, 1:02 a.m.