hughes.grapes {agridat} | R Documentation

## Disease incidence on grape leaves in RCB experiment

### Description

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.

### 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. http://doi.org/10.1111/j.1365-3059.1995.tb02651.x

Used with permission of Larry Madden.

### References

Hans-Pieter Piepho. 1999. Analysing disease incidence data from designed experiments by generalized linear mixed models. Plant Pathology, 48, 668–684. http://doi.org/10.1046/j.1365-3059.1999.00383.x

### Examples

    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
    require(lattice)
    foo <- bwplot(rate ~ vine|block*trt, dat, main="hughes.grapes",
                  xlab="vine")
    if(require(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)

    # ----------------------------------------------------------------------------

    ## Not run:
      # Piepho model 3.  Binomial data.  May not be exactly the same model

      # Use the binomial count data with lme4
      require(lme4)
      m1 <- glmer(cbind(diseased, total-diseased) ~ trt + block + (1|plot/vine),
                  data=dat, family=binomial)
      m1

      # Switch from binomial counts to bernoulli data
      require(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
      require(MASS)
      m3 <- glmmPQL(y ~ trt + block, data=bdat,
                    random=~1|plot/vine, family=binomial)
      m3

    ## End(Not run)

[Package agridat version 1.16 Index]