agridat (version 1.17)

bridges.cucumber: Multi-environment trial of cucumbers in a latin square design

Description

Cucumber yields in latin square design at two locs.

Format

A data frame with 32 observations on the following 5 variables.

loc

location

gen

genotype/cultivar

row

row

col

column

yield

weight of marketable fruit per plot

Details

Conducted at Clemson University in 1985. four cucumber cultivars were grown in a latin square design at Clemson, SC, and Tifton, GA.

Separate variances are modeled each location.

Plot dimensions are not given.

Bridges (1989) used this data to illustrate fitting a heterogeneous mixed model.

Used with permission of William Bridges.

Examples

# NOT RUN {
  library(agridat)
  data(bridges.cucumber)
  dat <- bridges.cucumber
  dat <- transform(dat, rowf=factor(row), colf=factor(col))

  libs(desplot)
  desplot(dat, yield~col*row|loc,
          # aspect unknown
          text=gen, cex=1,
          main="bridges.cucumber")

  # Graphical inference test for heterogenous variances
  libs(nullabor)
  # Create a lineup of datasets
  fun <- null_permute("loc")
  dat20 <- lineup(fun, dat, n=20, pos=9)

  # Now plot
  libs(lattice)
  bwplot(yield ~ loc|factor(.sample), dat20,
         main="bridges.cucumber - graphical inference")


  ## libs(asreml)
  ## if( utils::packageVersion("asreml") < "4") {
  ##   # asreml3
  
  ##   ## Random row/col/resid. Same as Bridges 1989, p. 147
  ##   m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
  ##                random = ~ rowf:loc + colf:loc, data=dat)

  ##   libs(lucid)
  ##   vc(m1)
  ##   ##             effect component std.error z.ratio constr
  ##   ##  rowf:loc!rowf.var     31.62     23.02     1.4    pos
  ##   ##  colf:loc!colf.var     18.08     15.32     1.2    pos
  ##   ##         R!variance     31.48     12.85     2.4    pos
  
  ##   ## Random row/col/resid at each loc. Matches p. 147
  ##   m2 <- asreml(yield ~ 1 + gen + loc + loc:gen,
  ##                random = ~ at(loc):rowf + at(loc):colf, data=dat,
  ##                rcov = ~at(loc):units)
  ##   vc(m2)
  ##   ##                          effect component std.error z.ratio constr
  ##   ##  at(loc, Clemson):rowf!rowf.var     32.32    36.58     0.88    pos
  ##   ##   at(loc, Tifton):rowf!rowf.var     30.92    28.63     1.1     pos
  ##   ##  at(loc, Clemson):colf!colf.var     22.55    28.78     0.78    pos
  ##   ##   at(loc, Tifton):colf!colf.var     13.62    14.59     0.93    pos
  ##   ##            loc_Clemson!variance     46.85    27.05     1.7     pos
  ##   ##             loc_Tifton!variance     16.11     9.299    1.7     pos
  
  ##   predict(m2, data=dat, classify='loc:gen')$predictions$pvals
  ##   ##     loc      gen Predicted Std Err    Status
  ##   ## Clemson   Dasher     45.55   5.043 Estimable
  ##   ## Clemson Guardian     31.62   5.043 Estimable
  ##   ## Clemson Poinsett     21.42   5.043 Estimable
  ##   ## Clemson   Sprint     25.95   5.043 Estimable
  ##   ##  Tifton   Dasher     50.48   3.894 Estimable
  ##   ##  Tifton Guardian     38.72   3.894 Estimable
  ##   ##  Tifton Poinsett     33.01   3.894 Estimable
  ##   ##  Tifton   Sprint     39.18   3.894 Estimable
    
  ##   # Is a heterogeneous model justified? Maybe not.
  ##   # m1$loglik
  ##   ## -67.35585
  ##   # m2$loglik
  ##   ## -66.35621
  ## }

  libs(asreml)
  if( utils::packageVersion("asreml") >  "4") {
    # asreml4
    
    ## Random row/col/resid. Same as Bridges 1989, p. 147
    m1 <- asreml(yield ~ 1 + gen + loc + loc:gen,
                 random = ~ rowf:loc + colf:loc, data=dat)

  libs(lucid)
  lucid::vc(m1)
  ##   effect component std.error z.ratio bound <!-- %ch -->
  ## rowf:loc     31.62     23.02     1.4     P   0
  ## colf:loc     18.08     15.32     1.2     P   0
  ## units(R)     31.48     12.85     2.4     P   0
  
  ## Random row/col/resid at each loc. Matches p. 147
  m2 <- asreml(yield ~ 1 + gen + loc + loc:gen,
               random = ~ at(loc):rowf + at(loc):colf, data=dat,
               resid = ~ dsum( ~ units|loc))
  lucid::vc(m2)
  ##                effect component std.error z.ratio bound <!-- %ch -->
  ## at(loc, Clemson):rowf     32.32    36.58     0.88     P   0
  ##  at(loc, Tifton):rowf     30.92    28.63     1.1      P   0
  ## at(loc, Clemson):colf     22.55    28.78     0.78     P   0
  ##  at(loc, Tifton):colf     13.62    14.59     0.93     P   0
  ##        loc_Clemson(R)     46.85    27.05     1.7      P   0
  ##         loc_Tifton(R)     16.11     9.299    1.7      P   0
  
  predict(m2, data=dat, classify='loc:gen')$pvals
  ##       loc      gen predicted.value std.error    status
  ## 1 Clemson   Dasher            45.6      5.04 Estimable
  ## 2 Clemson Guardian            31.6      5.04 Estimable
  ## 3 Clemson Poinsett            21.4      5.04 Estimable
  ## 4 Clemson   Sprint            26        5.04 Estimable
  ## 5  Tifton   Dasher            50.5      3.89 Estimable
  ## 6  Tifton Guardian            38.7      3.89 Estimable
  ## 7  Tifton Poinsett            33        3.89 Estimable
  ## 8  Tifton   Sprint            39.2      3.89 Estimable

  # Is a heterogeneous model justified? Maybe not.
  # m1$loglik
  ## -67.35585
  # m2$loglik
  ## -66.35621

  }
# }