Competition experiment between barley and sinapis.

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.

Used with permission of Jens Streibig.

Source

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

References

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

Examples

if (FALSE) { # \dontrun{
  
library(agridat)

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
libs(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)
}

} # }