mead.cauliflower {agridat} | R Documentation

## Leaves for cauliflower plants at different times

### Description

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

    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")
    require(lattice)
    if(require(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")
    }

[Package agridat version 1.16 Index]