agridat (version 1.23)

cochran.wireworms: Wireworms controlled by fumigants in a latin square

Description

Wireworms controlled by fumigants in a latin square

Format

A data frame with 25 observations on the following 4 variables.

row

row

col

column

trt

fumigant treatment, 5 levels

worms

count of wireworms per plot

Details

Plots were approximately 22 cm by 13 cm. Layout of the experiment was a latin square. The number of wireworms in each plot was counted, following soil fumigation the previous year.

References

Ron Snee (1980). Graphical Display of Means. The American Statistician, 34, 195-199. https://www.jstor.org/stable/2684060 https://doi.org/10.1080/00031305.1980.10483028

W. Cochran (1940). The analysis of variance when experimental errors follow the Poisson or binomial laws. The Annals of Mathematical Statistics, 11, 335-347. https://www.jstor.org/stable/2235680

G W Snedecor and W G Cochran, 1980. Statistical Methods, Iowa State University Press. Page 288.

Examples

if (FALSE) {

library(agridat)
data(cochran.wireworms)
dat <- cochran.wireworms

libs(desplot)
desplot(dat, worms ~ col*row,
        text=trt, cex=1, # aspect unknown
        main="cochran.wireworms")

# Trt K is effective, but not the others.  Really, this says it all.
libs(lattice)
bwplot(worms ~ trt, dat, main="cochran.wireworms", xlab="Treatment")

# Snedecor and Cochran do ANOVA on sqrt(x+1).
dat <- transform(dat, rowf=factor(row), colf=factor(col))
m1 <- aov(sqrt(worms+1) ~ rowf + colf + trt, data=dat)
anova(m1)

# Instead of transforming, use glm
m2 <- glm(worms ~ trt + rowf + colf, data=dat, family="poisson")
anova(m2)

# GLM with random blocking.
libs(lme4)
m3 <- glmer(worms ~ -1 +trt +(1|rowf) +(1|colf), data=dat, family="poisson")
summary(m3)
## Fixed effects:
##      Estimate Std. Error z value Pr(>|z|)    
## trtK   0.1393     0.4275   0.326    0.745    
## trtM   1.7814     0.2226   8.002 1.22e-15 ***
## trtN   1.9028     0.2142   8.881  < 2e-16 ***
## trtO   1.7147     0.2275   7.537 4.80e-14 ***

}