Multi-environment trial of maize, dry matter content

data("vaneeuwijk.drymatter")

Format

A data frame with 168 observations on the following 5 variables.

year: year
site: site, 4 levels
variety: variety, 6 levels
y: dry matter percent

Details

Percent dry matter is given.

Site codes are soil type classifications: SS=Southern Sand, CS=Central Sand, NS=Northern Sand, RC=River Clay.

These data are a balanced subset of the data analyzed in van Eeuwijk, Keizer, and Bakker (1995b) and Kroonenberg, Basford, and Ebskamp (1995).

Used with permission of Fred van Eeuwijk.

Source

van Eeuwijk, Fred A. and Pieter M. Kroonenberg (1998). Multiplicative Models for Interaction in Three-Way ANOVA, with Applications to Plant Breeding Biometrics, 54, 1315-1333. https://doi.org/10.2307/2533660

References

Kroonenberg, P.M., Basford, K.E. & Ebskamp, A.G.M. (1995). Three-way cluster and component analysis of maize variety trials. Euphytica, 84(1):31-42. https://doi.org/10.1007/BF01677554

van Eeuwijk, F.A., Keizer, L.C.P. & Bakker, J.J. Van Eeuwijk. (1995b). Linear and bilinear models for the analysis of multi-environment trials: II. An application to data from the Dutch Maize Variety Trials Euphytica, 84(1):9-22. https://doi.org/10.1007/BF01677552

Hardeo Sahai, Mario M. Ojeda. Analysis of Variance for Random Models, Volume 1. Page 261.

Examples

# \dontrun{
  
  library(agridat)
  data(vaneeuwijk.drymatter)
  dat <- vaneeuwijk.drymatter
  dat <- transform(dat, year=factor(year))
  dat <- transform(dat, env=factor(paste(year,site)))

  libs(HH)
  HH::interaction2wt(y ~ year+site+variety,dat,rot=c(90,0),
                     x.between=0, y.between=0,
                     main="vaneeuwijk.drymatter")

  
  # anova model
  m1 <- aov(y ~ variety+env+variety:env, data=dat)
  anova(m1) # Similar to VanEeuwijk table 2
#> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
#> Analysis of Variance Table
#> 
#> Response: y
#>              Df  Sum Sq Mean Sq F value Pr(>F)
#> variety       5   80.18  16.036               
#> env          27 3008.21 111.415               
#> variety:env 135  161.87   1.199               
#> Residuals     0    0.00                       
  m2 <- aov(y ~ year*site*variety, data=dat)
  anova(m2) # matches Sahai table 5.5
#> Warning: ANOVA F-tests on an essentially perfect fit are unreliable
#> Analysis of Variance Table
#> 
#> Response: y
#>                   Df  Sum Sq Mean Sq F value Pr(>F)
#> year               6 1194.65  199.11               
#> site               3  962.19  320.73               
#> variety            5   80.18   16.04               
#> year:site         18  851.37   47.30               
#> year:variety      30   64.90    2.16               
#> site:variety      15   17.01    1.13               
#> year:site:variety 90   79.97    0.89               
#> Residuals          0    0.00                       
  
  # variance components model
  libs(lme4)
  libs(lucid)
  m3 <- lmer(y ~ (1|year) + (1|site) + (1|variety) +
               (1|year:site) + (1|year:variety) + (1|site:variety),
             data=dat)
  vc(m3) # matches Sahai page 266
#>           grp        var1 var2    vcov  sdcor
#>  year:variety (Intercept) <NA> 0.3187  0.5645
#>     year:site (Intercept) <NA> 7.734   2.781 
#>  site:variety (Intercept) <NA> 0.03503 0.1872
#>          year (Intercept) <NA> 6.273   2.505 
#>       variety (Intercept) <NA> 0.4867  0.6976
#>          site (Intercept) <NA> 6.504   2.55  
#>      Residual        <NA> <NA> 0.8886  0.9426
  ##          grp        var1 var2    vcov  sdcor
  ## year:variety (Intercept) <NA> 0.3187  0.5645
  ##    year:site (Intercept) <NA> 7.735   2.781 
  ## site:variety (Intercept) <NA> 0.03502 0.1871
  ##         year (Intercept) <NA> 6.272   2.504 
  ##      variety (Intercept) <NA> 0.4867  0.6976
  ##         site (Intercept) <NA> 6.504   2.55  
  ##     Residual        <NA> <NA> 0.8885  0.9426
  
# }