agridat (version 1.16)

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

W. Cochran (1940). The analysis of variance when experimental errors follow the Poisson or binomial laws. The Annals of Mathematical Statistics, 11, 335-347.

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

Examples

# NOT RUN {
data(cochran.wireworms)
dat <- cochran.wireworms

if(require(desplot)){
  desplot(worms ~ col*row, data=dat,
          text=trt, cex=1, # aspect unknown
          main="cochran.wireworms")
}

# Trt K is effective, but not the others.  Really, this says it all.
require(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.
if(require(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 ***

}

# }