streibig.competition {agridat} | R Documentation

## Competition experiment between barley and sinapis.

### Description

Competition experiment between barley and sinapis, at different planting rates.

### Format

A data frame with 135 observations on the following 8 variables.

`pot`

  pot number

`bseeds`

  barley seeds sown

`sseeds`

  sinapis seeds sown

`block`

  block

`bfwt`

  barley fresh weight

`sfwt`

  sinapis fresh weight

`bdwt`

  barley dry weight

`sdwt`

  sinapis dry weight

### Details

The source data (in McCullagh) also contains a count of plants harvested (not included here) that sometimes is greater than the number of seeds planted.

### Source

Peter McCullagh, John A. Nelder. Generalized Linear Models, page 318-320.

Used with permission of Jens Streibig.

### References

Oliver Schabenberger and Francis J Pierce. 2002. Contemporary Statistical Models for the Plant and Soil Sciences, CRC Press. Page 370-375.

### Examples

    data(streibig.competition)
    dat <- streibig.competition

    # See Schaberger and Pierce, pages 370+
    # Consider only the mono-species barley data (no competition from sinapis)
    d1 <- subset(dat, sseeds<1)
    d1 <- transform(d1, x=bseeds, y=bdwt, block=factor(block))

    # Inverse yield looks like it will be a good fit for Gamma's inverse link
    require(lattice)
    xyplot(1/y~x, data=d1, group=block, auto.key=list(columns=3),
           xlab="Seeding rate", ylab="Inverse yield of barley dry weight",
           main="streibig.competition")

    # linear predictor is quadratic, with separate intercept and slope per block
    m1 <- glm(y ~ block + block:x + x+I(x^2), data=d1,
              family=Gamma(link="inverse"))
    # Predict and plot
    newdf <- expand.grid(x=seq(0,120,length=50), block=factor(c('B1','B2','B3')) )
    newdf$pred <- predict(m1, new=newdf, type='response')
    plot(y~x, data=d1, col=block, main="streibig.competition - by block",
         xlab="Barley seeds", ylab="Barley dry weight")
    for(bb in 1:3){
      newbb <- subset(newdf, block==c('B1','B2','B3')[bb])
      lines(pred~x, data=newbb, col=bb)
    }

[Package agridat version 1.16 Index]