agridat (version 1.16)

burgueno.unreplicated: Field experiment with unreplicated genotypes plus one repeated check.

Description

Field experiment with unreplicated genotypes plus one repeated check.

Usage

data("burgueno.unreplicated")

Format

A data frame with 434 observations on the following 4 variables.

gen

genotype, 281 levels

col

column

row

row

yield

yield, tons/ha

Details

A field experiment with 280 new genotypes. A check genotype is planted in every 4th column.

The plot size is not given.

Examples

# NOT RUN {
data(burgueno.unreplicated)
dat <- burgueno.unreplicated

# Define a 'check' variable for colors
dat$check <- ifelse(dat$gen=="G000", 2, 1)
# Every fourth column is the 'check' genotype
if(require(desplot)){
  desplot(yield ~ col*row, data=dat,
          col=check, num=gen, #text=gen, cex=.3, # aspect unknown
          main="burgueno.unreplicated")
}

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

# }
# NOT RUN {
  # asreml3
  require(asreml)
  require(lucid)

  # AR1 x AR1 with random genotypes
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  dat <- dat[order(dat$xf,dat$yf),]
  m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
               rcov = ~ ar1(xf):ar1(yf))
  vc(m2)
  # Note the strong saw-tooth pattern in the variogram.  Seems to
  # be column effects.
  plot(variogram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
       main="burgueno.unreplicated - AR1xAR1")
  # library(lattice) # Show how odd columns are high
  # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)

  # Define an even/odd column factor as fixed effect
  # dat$oddcol <- factor(dat$col <!-- %% 2) -->
  # The modulus operator throws a bug, so do it the hard way.
  dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )

  m3 <- update(m2, yield ~ 1 + oddcol)
  m3$loglik # Matches Burgueno table 3, line 3
  plot(variogram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
       main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
  # Much better-looking variogram

# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  ## require(asreml4)
  ## require(lucid)

  ## # AR1 x AR1 with random genotypes
  ## dat <- transform(dat, xf=factor(col), yf=factor(row))
  ## dat <- dat[order(dat$xf,dat$yf),]
  ## m2 <- asreml(yield ~ 1, data=dat, random = ~ gen,
  ##              resid = ~ ar1(xf):ar1(yf))
  ## vc(m2)
  ## ##       effect component std.error z.ratio bound <!-- %ch -->
  ## ##          gen    0.9122   0.127       7.2     P 0  
  ## ##     xf:yf(R)    0.4993   0.05601     8.9     P 0  
  ## ## xf:yf!xf!cor   -0.2431   0.09156    -2.7     U 0  
  ## ## xf:yf!yf!cor    0.1255   0.07057     1.8     U 0.1

  ## # Note the strong saw-tooth pattern in the variogram.  Seems to
  ## # be column effects.
  ## plot(varioGram(m2), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
  ##      main="burgueno.unreplicated - AR1xAR1")
  ## # library(lattice) # Show how odd columns are high
  ## # bwplot(resid(m2) ~ col, data=dat, horizontal=FALSE)

  ## # Define an even/odd column factor as fixed effect
  ## # dat$oddcol <- factor(dat$col <!-- %% 2) -->
  ## # The modulus operator throws a bug, so do it the hard way.
  ## dat$oddcol <- factor(dat$col - floor(dat$col / 2) *2 )

  ## m3 <- update(m2, yield ~ 1 + oddcol)
  ## m3$loglik # Matches Burgueno table 3, line 3
  
  ## plot(varioGram(m3), xlim=c(0,15), ylim=c(0,9), zlim=c(0,0.5),
  ##      main="burgueno.unreplicated - AR1xAR1 + Even/Odd")
  ## # Much better-looking variogram

# }
# NOT RUN {
# }