RCB experiment of grape, disease incidence — hughes.grapes • agridat

Disease incidence on grape leaves in RCB experiment with 6 different treatments.

Format

A data frame with 270 observations on the following 6 variables.

block: Block factor, 1-3
trt: Treatment factor, 1-6
vine: Vine factor, 1-3
shoot: Shoot factor, 1-5
diseased: Number of diseased leaves per shoot
total: Number of total leaves per shoot

Details

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.

Source

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

References

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

Examples

if (FALSE) { # \dontrun{
  
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

} # }