streibig.competition | R Documentation |
Competition experiment between barley and sinapis, at different planting rates.
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
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.
Peter McCullagh, John A. Nelder. Generalized Linear Models, page 318-320.
Oliver Schabenberger and Francis J Pierce. 2002. Contemporary Statistical Models for the Plant and Soil Sciences. CRC Press. Page 370-375.
## Not run:
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)
}
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.