burgueno.alpha: Incomplete block alpha design

In agridat: Agricultural Datasets

Incomplete block alpha design

Description

Incomplete block alpha design

Usage

data("burgueno.alpha")

Format

A data frame with 48 observations on the following 6 variables.

rep

rep, 3 levels

block

block, 12 levels

row

row

col

column

gen

genotype, 16 levels

yield

yield

Details

A field experiment with 3 reps, 4 blocks per rep, laid out as an alpha design.

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. https://books.google.com/books?id=PR_tYCFyLCYC&pg=PA1

Examples

## Not run: 

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

  libs(desplot)
  desplot(dat, yield~col*row,
          out1=rep, out2=block, # aspect unknown
          text=gen, cex=1,shorten="none",
          main='burgueno.alpha')


  libs(lme4,lucid)
  # Inc block model
  m0 <- lmer(yield ~ gen + (1|rep/block), data=dat)
  vc(m0) # Matches Burgueno p. 26
  ##        grp        var1 var2   vcov sdcor
  ##  block:rep (Intercept) <NA>  86900 294.8
  ##        rep (Intercept) <NA> 200900 448.2
  ##   Residual        <NA> <NA> 133200 365  


  libs(asreml)
    
  dat <- transform(dat, xf=factor(col), yf=factor(row))
  dat <- dat[order(dat$xf, dat$yf),]                 
  
  # Sequence of models on page 36
  
  m1 <- asreml(yield ~  gen, data=dat)
  m1$loglik # -232.13
  
  m2 <- asreml(yield ~  gen, data=dat,
               random = ~ rep)
  m2$loglik # -223.48
  
  # Inc Block model
  m3 <- asreml(yield ~  gen, data=dat,
               random = ~ rep/block)
  m3$loglik # -221.42
  m3$coef$fixed # Matches solution on p. 27
  
  # AR1xAR1 model
  m4 <- asreml(yield ~ 1 + gen, data=dat,
               resid = ~ar1(xf):ar1(yf))
  m4$loglik # -221.47
  plot(varioGram(m4), main="burgueno.alpha") # Figure 1
  
  m5 <- asreml(yield ~ 1 + gen, data=dat,
               random= ~ yf, resid = ~ar1(xf):ar1(yf))
  m5$loglik # -220.07
  
  m6 <- asreml(yield ~ 1 + gen + pol(yf,-2), data=dat,
               resid = ~ar1(xf):ar1(yf))
  m6$loglik # -204.64
  
  m7 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
               random= ~ spl(yf), resid = ~ar1(xf):ar1(yf))
  m7$loglik # -212.51
  
  m8 <- asreml(yield ~ 1 + gen + lin(yf), data=dat,
               random= ~ spl(yf))
  m8$loglik # -213.91
  
  # Polynomial model with predictions
  m9 <- asreml(yield ~ 1 + gen + pol(yf,-2) + pol(xf,-2), data=dat,
               random= ~ spl(yf),
               resid = ~ar1(xf):ar1(yf))
  m9 <- update(m9)
  m9$loglik # -191.44 vs -189.61
  p9 <- predict(m9, classify="gen:xf:yf", levels=list(xf=1,yf=1)) 
  p9
  
  m10 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
                resid = ~ar1(xf):ar1(yf))
  m10$loglik # -211.56
  
  m11 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
                random= ~ spl(yf),
                resid = ~ar1(xf):ar1(yf))
  m11$loglik # -208.90
  
  m12 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
                random= ~ spl(yf)+spl(xf),
                resid = ~ar1(xf):ar1(yf))
  m12$loglik # -206.82
  
  m13 <- asreml(yield ~ 1 + gen + lin(yf)+lin(xf), data=dat,
                random= ~ spl(yf)+spl(xf))
  m13$loglik # -207.52


## End(Not run)

agridat documentation built on Aug. 25, 2023, 5:18 p.m.