RCB experiment of spring barley in United Kingdom

Format

A data frame with 225 observations on the following 4 variables.

col: column (also blocking factor)
row: row
yield: yield
gen: variety/genotype

Details

RCB design, each column is one rep.

Used with permission of David Higdon.

Source

Besag, J. E., Green, P. J., Higdon, D. and Mengersen, K. (1995). Bayesian computation and stochastic systems. Statistical Science, 10, 3-66. https://www.jstor.org/stable/2246224

References

Davison, A. C. 2003. Statistical Models. Cambridge University Press. Pages 534-535.

Examples

if (FALSE) { # \dontrun{
  
  library(agridat)
  data(besag.bayesian)
  dat <- besag.bayesian

  # Yield values were scaled to unit variance
  # var(dat$yield, na.rm=TRUE)
  # .999

  # Besag Fig 2. Reverse row numbers to match Besag, Davison
  dat$rrow <- 76 - dat$row
  libs(lattice)
  xyplot(yield ~ rrow|col, dat, layout=c(1,3), type='s',
         xlab="row", ylab="yield", main="besag.bayesian")

  if(require("asreml", quietly=TRUE)) {
    libs(asreml, lucid)

    # Use asreml to fit a model with AR1 gradient in rows  
    dat <- transform(dat, cf=factor(col), rf=factor(rrow))
    m1 <- asreml(yield ~ -1 + gen, data=dat, random= ~ rf)
    m1 <- update(m1, random= ~ ar1v(rf))
    m1 <- update(m1)
    m1 <- update(m1)
    m1 <- update(m1)
    lucid::vc(m1)
  
    # Visualize trends, similar to Besag figure 2.
    # Need 'as.vector' because asreml uses a named vector
    dat$res <- unname(m1$resid)
    dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)]
    dat <- transform(dat, fert=yield-geneff-res)
    libs(lattice)
    xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s',
           main="besag.bayesian - Variety effects", ylim=c(5,15 ))
    xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s',
           main="besag.bayesian - Fertility", ylim=c(-2,2))
    xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s',
           main="besag.bayesian - Residuals", ylim=c(-4,4))
  }
} # }