RCB experiment of grape, disease incidence
Disease incidence on grape leaves in RCB experiment with 6 different treatments.
A data frame with 270 observations on the following 6 variables.
blockBlock factor, 1-3
trtTreatment factor, 1-6
vineVine factor, 1-3
shootShoot factor, 1-5
diseasedNumber of diseased leaves per shoot
totalNumber of total leaves per shoot
These data come from a study of downy mildew on grapes. The experiment was conducted at Wooster, Ohio, on the experimental farm of the Ohio Agricultural Research and Development Center, Ohio State University.
There were 3 blocks with 6 treatments. Treatment 1 is the unsprayed control. On 30 Sep 1990, disease incidence was measured. For each plot, 5 randomly chosen shoots on each of the 3 vines were observed. The canopy was closed and shoots could be intertwined. On each shoot, the total number of leaves and the number of infected leaves were recorded.
Used with permission of Larry Madden.
Hughes, G. and Madden, LV. 1995. Some methods allowing for aggregated patterns of disease incidence in the analysis of data from designed experiments. Plant Pathology, 44, 927–943. https://doi.org/10.1111/j.1365-3059.1995.tb02651.x
Hans-Pieter Piepho. 1999. Analysing disease incidence data from designed experiments by generalized linear mixed models. Plant Pathology, 48, 668–684. https://doi.org/10.1046/j.1365-3059.1999.00383.x
## Not run:
library(agridat)
data(hughes.grapes)
dat <- hughes.grapes
dat <- transform(dat, rate = diseased/total, plot=trt:block)
# Trt 1 has higher rate, more variable, Trt 3 lower rate, less variable
libs(lattice)
foo <- bwplot(rate ~ vine|block*trt, dat, main="hughes.grapes",
xlab="vine")
libs(latticeExtra)
useOuterStrips(foo)
# Table 1 of Piepho 1999
tapply(dat$rate, dat$trt, mean) # trt 1 does not match Piepho
tapply(dat$rate, dat$trt, max)
# Piepho model 3. Binomial data. May not be exactly the same model
# Use the binomial count data with lme4
libs(lme4)
m1 <- glmer(cbind(diseased, total-diseased) ~ trt + block + (1|plot/vine),
data=dat, family=binomial)
m1
# Switch from binomial counts to bernoulli data
libs(aod)
bdat <- splitbin(cbind(diseased, total-diseased) ~ block+trt+plot+vine+shoot,
data=dat)$tab
names(bdat)[2] <- 'y'
# Using lme4
m2 <- glmer(y ~ trt + block + (1|plot/vine), data=bdat, family=binomial)
m2
# Now using MASS:::glmmPQL
libs(MASS)
m3 <- glmmPQL(y ~ trt + block, data=bdat,
random=~1|plot/vine, family=binomial)
m3
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