Row-column design

Row-column design

data("burgueno.rowcol")

Format

A data frame with 128 observations on the following 5 variables.

rep

rep, 2 levels

row

row

col

column

gen

genotype, 64 levels

yield

yield, tons/ha

Details

A field experiment with two contiguous replicates in 8 rows, 16 columns.

The plot size is not given.

Electronic version of the data obtained from CropStat software.

Used with permission of Juan Burgueno.

Source

J Burgueno, A Cadena, J Crossa, M Banziger, A Gilmour, B Cullis (2000). User's guide for spatial analysis of field variety trials using ASREML. CIMMYT.

Examples

# \dontrun{

  library(agridat)
  data(burgueno.rowcol)
  dat <- burgueno.rowcol

  # Two contiguous reps in 8 rows, 16 columns
  libs(desplot)
  desplot(dat, yield ~ col*row,
          out1=rep, # aspect unknown
          text=gen, shorten="none", cex=.75,
          main="burgueno.rowcol")

  libs(lme4,lucid)
  
  # Random rep, row and col within rep
  # m1 <- lmer(yield ~ gen + (1|rep) + (1|rep:row) + (1|rep:col), data=dat)
  # vc(m1) # Match components of Burgueno p. 40
  ##      grp        var1 var2   vcov  sdcor
  ##  rep:col (Intercept) <NA> 0.2189 0.4679
  ##  rep:row (Intercept) <NA> 0.1646 0.4057
  ##      rep (Intercept) <NA> 0.1916 0.4378
  ## Residual        <NA> <NA> 0.1796 0.4238
  
  libs(asreml) # asreml4

  # AR1 x AR1 with linear row/col effects, random spline row/col
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  dat <- dat[order(dat$xf,dat$yf),]
  m2 <- asreml(yield ~ gen + lin(yf) + lin(xf), data=dat,
               random = ~ spl(yf) + spl(xf),
               resid = ~ ar1(xf):ar1(yf))
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:07:59 2021
#> Spline: design points closer than 0.0007 have been merged.
#> Spline: design points closer than 0.0015 have been merged.
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -11.7667      0.189040     62 17:07:59    0.0
#>  2      -10.9242      0.179307     62 17:07:59    0.0
#>  3      -10.0462      0.167806     62 17:07:59    0.0
#>  4       -9.4052      0.156150     62 17:07:59    0.0
#>  5       -9.2562      0.150710     62 17:07:59    0.0
#>  6       -9.2402      0.148710     62 17:07:59    0.0
#>  7       -9.2394      0.148344     62 17:07:59    0.0
#> Warning: Some components changed by more than 1% on the last iteration.
  m2 <- update(m2) # More iterations
#> Model fitted using the gamma parameterization.
#> ASReml 4.1.0 Mon Jan 11 17:07:59 2021
#> Spline: design points closer than 0.0007 have been merged.
#> Spline: design points closer than 0.0015 have been merged.
#>           LogLik        Sigma2     DF     wall    cpu
#>  1      -9.23934      0.148220     62 17:07:59    0.0
#>  2      -9.23934      0.148225     62 17:07:59    0.0
#> Warning: Some components changed by more than 1% on the last iteration.
  
  # Scaling of spl components has changed in asreml from old versions
  libs(lucid)
  vc(m2) # Match Burgueno p. 42
#>        effect component std.error z.ratio bound %ch
#>       spl(yf)   0.09077   0.08252   1.1       P 0  
#>       spl(xf)   0.08105   0.08198   0.99      P 0.1
#>       xf:yf!R   0.1482    0.03119   4.8       P 0  
#>  xf:yf!xf!cor   0.1153    0.2269    0.51      U 0.2
#>  xf:yf!yf!cor   0.00953   0.2414    0.039     U 1.9
  ##       effect component std.error z.ratio bound 
  ##      spl(yf)  0.09077    0.08252   1.1       P 0
  ##      spl(xf)  0.08107    0.08209   0.99      P 0
  ##     xf:yf(R)  0.1482     0.03119   4.8       P 0
  ## xf:yf!xf!cor  0.1152     0.2269    0.51      U 0.1
  ## xf:yf!yf!cor  0.009467   0.2414    0.039     U 0.9
  
  plot(varioGram(m2), main="burgueno.rowcol")

# }