Description Format Details Source References Examples

Leaves for cauliflower plants at different times in two years.

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

`year`

year factor

`degdays`

degree days above 32F

`leaves`

number of leaves

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.

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

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## Not run:
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
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")
## End(Not run)
``` |

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