besag.bayesian {agridat} | R Documentation

## Spring barley in United Kingdom

### Description

An experiment with 75 varieties of barley, planted in 3 reps.

### 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.

### Source

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

Used with permission of David Higdon.

### References

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

### Examples

    data(besag.bayesian)
    dat <- besag.bayesian

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

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

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

    ## Not run:
      # asreml3
      require(asreml)

      # 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=~ar1v(rf))
      m1 <- update(m1)

      # Visualize trends, similar to Besag figure 2.
      dat$res <- m1$residuals
      dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)]
      dat <- transform(dat, fert=yield-geneff-res)
      xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s',
             main="Variety effects", ylim=c(5,15 ))
      xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s',
             main="Fertility", ylim=c(-2,2))
      xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s',
             main="Residuals", ylim=c(-4,4))

    ## End(Not run)

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

    ## Not run:
      ## require(asreml4)

      ## # 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=~ar1v(rf))
      ## m1 <- update(m1)
      ## m1 <- update(m1)
      ## m1 <- update(m1)
      ## m1 <- update(m1)
      ## m1 <- update(m1)
      ## m1 <- update(m1)

      ## # Visualize trends, similar to Besag figure 2.
      ## dat$res <- resid(m1)
      ## dat$geneff <- coef(m1)$fixed[as.numeric(dat$gen)]
      ## dat <- transform(dat, fert=yield-geneff-res)
      ## xyplot(geneff ~ rrow|col, dat, layout=c(1,3), type='s',
      ##        main="Variety effects", ylim=c(5,15 ))
      ## xyplot(fert ~ rrow|col, dat, layout=c(1,3), type='s',
      ##        main="Fertility", ylim=c(-2,2))
      ## xyplot(res ~ rrow|col, dat, layout=c(1,3), type='s',
      ##        main="Residuals", ylim=c(-4,4))

    ## End(Not run)

[Package agridat version 1.16 Index]