Damage to potato tubers from lifting rods.

Usage

data("keen.potatodamage")

Format

A data frame with 1152 observations on the following 6 variables.

energy: energy factor
weight: weight class
gen: genotype/variety factor
rod: rod factor
damage: damage category
count: count of tubers in each combination of categories

Details

Experiments performed at Wageningen, Netherlands.

Potatoes can be damaged by the lifter. In this experiment, eight types of lifting rod were compared. Two energy levels, six genotypes/varieties and three weight classes were used. Most combinations of treatments involved about 20 potato tubers. Tubers were rated as undamaged (D1) to severely damaged (D4).

The main interest is in differences between rods, and not in interactions. The other factors (besides rod) were introduced to create variety in experimental conditions and are not of interest.

Keen and Engle estimated the following rod effects.

# Rod: 1 2 3 4 5 6 7 8

# Effect: 0 -1.26 -0.42 0.55 -1.50 -1.85 -1.76 -2.09

Used with permission of Bas Engel.

Source

A. Keen and B. Engel. Analysis of a mixed model for ordinal data by iterative re-weighted REML. Statistica Neerlandica, 51, 129–144. Table 2. https://doi.org/10.1111/1467-9574.00044

References

R. Larsson & Jesper Ryden (2021). Applications of discrete factor analysis. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2021.1964528

Examples

if (FALSE) { # \dontrun{

library(agridat)

data(keen.potatodamage)
dat <- keen.potatodamage

# Energy E1, Rod R4, Weight W1 have higher proportions of severe damage
# Rod 8 has the least damage
d2 <- xtabs(count~energy+rod+gen+weight+damage, data=dat)
mosaicplot(d2, color=c("lemonchiffon1","moccasin","lightsalmon1","indianred"),
           xlab="Energy / Genotype", ylab="Rod / Weight", main="keen.potatodamage")


# Not run because CRAN prefers examples less than 5 seconds.
libs(ordinal)
# Note, the clmm2 function can have only 1 random term.  Results are
# similar to Keen & Engle, but necessarily different (they had multiple
# random terms).
m1 <- clmm2(damage ~ rod + energy + gen + weight, data=dat,
            weights=count, random=rod:energy, link='probit')
round(coef(m1)[4:10],2)
## rodR2 rodR3 rodR4 rodR5 rodR6 rodR7 rodR8
## -1.19 -0.41  0.50 -1.46 -1.73 -1.67 -1.99

# Alternative
# m2 <- clmm(damage ~ rod + energy + gen + weight +
#          (1|rod:energy), data=dat, weights=count, link='probit')

} # }