Leaves for cauliflower plants at different times

Leaves for cauliflower plants at different times in two years.

Format

A data frame with 14 observations on the following 4 variables.

year: year factor
degdays: degree days above 32F
leaves: number of leaves

Details

Numbers of leaves for 10 cauliflower plants in each of two years, and temperature degree-days above 32F, divided by 100.

The year is 1956-57 or 1957-58.

Over the data range shown, the number of leaves is increasing linearly. Extrapolating backwards shows that a linear model is inappropriate, and so a glm is used.

Source

Roger Mead, Robert N Curnow, Anne M Hasted. 2002. Statistical Methods in Agriculture and Experimental Biology, 3rd ed. Chapman and Hall. Page 251.

References

Mick O'Neill. Regression & Generalized Linear (Mixed) Models. Statistical Advisory & Training Service Pty Ltd.

Examples

# \dontrun{

library(agridat)
data(mead.cauliflower)
dat <- mead.cauliflower

dat <- transform(dat, year=factor(year))

m1 <- glm(leaves ~ degdays + year, data=dat, family=poisson)
coef(m1)
#> (Intercept)     degdays    year1957 
#>  3.49492453  0.08512651  0.21688760 
## (Intercept)     degdays    year1957
##  3.49492453  0.08512651  0.21688760

dat$pred <- predict(m1, type="response")
libs(lattice)
libs(latticeExtra)
xyplot(leaves~degdays, data=dat, groups=year, type=c('p'),
       auto.key=list(columns=2),
       main="mead.cauliflower - observed (symbol) & fitted (line)",
       xlab="degree days", ylab="Number of leaves", ) +
  xyplot(pred~degdays, data=dat, groups=year, type=c('l'), col="black")

# }