agridat (version 1.16)

cox.stripsplit: Strip-split plot of barley with fertilizer, calcium, and soil factors.

Description

Strip-split plot of barley with fertilizer, calcium, and soil factors.

Format

A data frame with 96 observations on the following 5 variables.

rep

replicate, 4 levels

soil

soil, 3 levels

fert

fertilizer, 4 levels

calcium

calcium, 2 levels

yield

yield of winter barley

Details

Four different fertilizer treatments are laid out in vertical strips, which are then split into subplots with different levels of calcium. Soil type is stripped across the split-plot experiment, and the entire experiment is then replicated three times.

Sometimes called a split-block design.

References

SAS/STAT(R) 9.2 User's Guide, Second Edition. Example 23.5 Strip-Split Plot. http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_anova_sect030.htm

Examples

# NOT RUN {
data(cox.stripsplit)
dat <- cox.stripsplit

# Raw means
aggregate(yield ~ calcium, data=dat, mean)
aggregate(yield ~ soil, data=dat, mean)
aggregate(yield ~ calcium, data=dat, mean)

if(require(HH)) {
  interaction2wt(yield ~ rep + soil + fert + calcium, dat,
                 x.between=0, y.between=0,
                 main="cox.stripsplit")
}

# Traditional AOV
m1 <- aov(yield~ fert*calcium*soil +
          Error(rep/(fert+soil+calcium:fert+soil:fert)),
          data=dat)
summary(m1)

# With balanced data, the following are all basically identical

if(require(lme4)){
  # The 'rep:soil:fert' term causes problems...so we drop it.
  m2 <- lmer(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) +
               (1|rep:soil) + (1|rep:fert:calcium), data=dat)
}

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

# }
# NOT RUN {
  # afex uses Kenword-Rogers approach for denominator d.f.
  if(require(afex)){
    mixed(yield ~ fert*soil*calcium + (1|rep) + (1|rep:fert) +
            (1|rep:soil) + (1|rep:fert:calcium) + (1|rep:soil:fert), data=dat,
          control=lmerControl(check.nobs.vs.rankZ="ignore"))
    ##              Effect      stat ndf     ddf F.scaling p.value
    ## 1       (Intercept) 1350.8113   1  3.0009         1  0.0000
    ## 2              fert    3.5619   3  9.0000         1  0.0604
    ## 3              soil    3.4659   2  6.0000         1  0.0999
    ## 4           calcium    1.8835   1 12.0000         1  0.1950
    ## 5         fert:soil    1.2735   6 18.0000         1  0.3179
    ## 6      fert:calcium    4.4457   3 12.0000         1  0.0255
    ## 7      soil:calcium    0.2494   2 24.0000         1  0.7813
    ## 8 fert:soil:calcium    0.3504   6 24.0000         1  0.9027
  }
# }
# NOT RUN {
# }