agridat (version 1.8.1)

holshouser.splitstrip: Split strip plot on soybeans

Description

Split strip plot on soybeans

source

Schabenberger, Oliver and Francis J. Pierce. 2002. Contemporary Statistical Models for the Plant and Soil Sciences. CRC Press, Boca Raton, FL. Page 493. Used with permission of David Holshouser at Virginia Polytechnic.

Details

Within each block, cultivars were whole plots. Withing whole plots, spacing was applied in strips vertically, and population was applied in strips horizontally.

Examples

dat <- holshouser.splitstrip
dat$spacing <- factor(dat$spacing)
dat$pop <- factor(dat$pop)

# Experiment layout and field trends
desplot(spacing ~ col*row, data=dat, out1=block, out2=cultivar,
        col=cultivar, text=pop, cex=.8, shorten='none', col.regions=c('wheat','white'),
        main="holshouser.splitstrip experiment design")
desplot(yield ~ col*row, data=dat, out1=block,
        main="holshouser.splitstrip")

# Overall main effects and interactions
require(HH)
interaction2wt(yield~cultivar*spacing*pop, dat)

## Schabenberger's SAS model, page 497
## proc mixed data=splitstripplot;
##   class block cultivar pop spacing;
##   model yield = cultivar spacing spacing*cultivar pop pop*cultivar
##                 spacing*pop spacing*pop*cultivar / ddfm=satterth;
##   random block block*cultivar block*cultivar*spacing block*cultivar*pop;
## run;

## Now lme4. This design has five error terms--four are explicitly given.
require(lme4)
m1 <- lmer(yield ~ cultivar * spacing * pop +
           (1|block) + (1|block:cultivar) + (1|block:cultivar:spacing) +
           (1|block:cultivar:pop), data=dat)

## Variances match Schabenberger, page 498.
print(VarCorr(m1), comp=c("Variance","Std.Dev."))
## Groups                 Name        Variance Std.Dev.
## block:cultivar:pop     (Intercept) 2.42148  1.55611
## block:cultivar:spacing (Intercept) 1.24440  1.11553
## block:cultivar         (Intercept) 0.45225  0.67249
## block                  (Intercept) 3.03675  1.74263
## Residual                           3.92751  1.98180