agridat (version 1.23)

pederson.lettuce.repeated: Repeated measurements of lettuce growth

Description

Repeated measurements of lettuce growth for 3 treatments.

Usage

data("pederson.lettuce.repeated")

Format

A data frame with 594 observations on the following 4 variables.

plant

plant number

day

day of observation

trt

treatment

weight

weight

Details

Experiment conducted in a greenhouse in Silver Bay, Minnesota. Plants were grown hydroponically. Treatment 1 had 9 plants per raft. Treatment 2 had 18 plants, treatment 3 had 36 plants. The response variable is weight of plant, roots, soil, cup, and water. The plants were measured repeatedly beginning Dec 1, and ending Jan 9, when the plants were harvested.

References

None

Examples

if (FALSE) {

library(agridat)
data(pederson.lettuce.repeated)
dat <- pederson.lettuce.repeated

libs(lattice)
dat <- dat[order(dat$day),]
xyplot(weight ~ day|trt, dat, type='l', group=plant, layout=c(3,1),
main="pederson.lettuce.repeated")

# Pederson used this SAS MIXED model for unstructured covariance
# proc mixed data=Project.Spacingdata; 
# class trt plant day; 
# model weight=trt day trt*day; 
# repeated day / subject=plant type=un r rcorr;   

# This should give the same results as SAS, but does not.
libs(nlme)
dat <- transform(dat, plant=factor(plant), day=factor(day))
datg <- groupedData(weight ~ day|plant, data=dat)
un1 <- gls(weight ~ trt * day, data=datg,
           correlation=corSymm(value=rep(.6,55), form = ~ 1 | plant),
           control=lmeControl(opt="optim", msVerbose=TRUE,
                              maxIter=500, msMaxIter=500))
logLik(un1)*2 # nlme has 1955, SAS had 1898.6

# Comparing the SAS results in Pederson (page 16) and the nlme results, we notice
# the SAS correlations in table 5.2 are unusually low for the first
# column. The nlme results have a higher correlation in the first column
# and just "look" better
un1

}