holshouser.splitstrip {agridat} | R Documentation

## Split strip plot on soybeans

### Description

Split strip plot on soybeans

### Format

A data frame with 160 observations on the following 8 variables.

`block`

  block factor, 4 levels

`plot`

  plot number

`cultivar`

  cultivar factor, 4 levels

`spacing`

  row spacing

`pop`

  population (thousand per acre)

`yield`

  yield

`row`

  row

`col`

  column

### Details

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

### 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.

### Examples

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

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

    # Overall main effects and interactions
    if(require(HH)){
      interaction2wt(yield~cultivar*spacing*pop, dat,
                     x.between=0, y.between=0,
                     main="holshouser.splitstrip")
    }

    ## 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;

    ## Not run:
      ## Now lme4. This design has five error terms--four are explicitly given.
      require(lme4)
      require(lucid)
      m1 <- lmer(yield ~ cultivar * spacing * pop +
                   (1|block) + (1|block:cultivar) + (1|block:cultivar:spacing) +
                   (1|block:cultivar:pop), data=dat)
      vc(m1) ## Variances match Schabenberger, page 498.
      ##                    grp        var1 var2   vcov  sdcor
      ##     block:cultivar:pop (Intercept) <NA> 2.421  1.556
      ## block:cultivar:spacing (Intercept) <NA> 1.244  1.116
      ##         block:cultivar (Intercept) <NA> 0.4523 0.6725
      ##                  block (Intercept) <NA> 3.037  1.743
      ##               Residual        <NA> <NA> 3.928  1.982

    ## End(Not run)

[Package agridat version 1.16 Index]