yang.barley {agridat} | R Documentation

## Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta.

### Description

Yield of 6 barley varieties at 18 locations in Alberta.

### Usage

    data("yang.barley")

### Format

A data frame with 108 observations on the following 3 variables.

`site`

  site factor, 18 levels

`gen`

  genotype factor, 6 levels

`yield`

  yield, Mg/ha

### Details

From an experiment in 2003. Yang (2013) uses this data to illustrate a procedure for bootstrapping biplots.

site | long | lat
Beaverlodge | 119.43 | 55.21
BigLakes | 113.70 | 53.61
Calmar | 113.85 | 53.26
CdcNorth | 113.33 | 53.63
DawsonCreek | 120.23 | 55.76
FtKent | 110.61 | 54.31
FtStJohn | 120.85 | 56.25
Irricana | 113.60 | 51.32
Killam | 111.85 | 52.78
Lacombe | 113.73 | 52.46
LethbridgeDry | 112.81 | 49.70
LethbridgeIrr | 112.81 | 49.70
Lomond | 112.65 | 50.35
Neapolis | 113.86 | 51.65
NorthernSunrise | NA | NA
Olds | 114.09 | 51.78
StPaul | 111.28 | 53.98
Stettler | 112.71 | 52.31

### Source

Rong-Cai Yang (2007). Mixed-Model Analysis of Crossover Genotype-Environment Interactions. Crop Science, 47, 1051-1062. http://doi.org/10.2135/cropsci2006.09.0611

Used with permission of Rong-Cai Yang.

### References

Zhiqiu Hu and Rong-Cai Yang, (2013). Improved Statistical Inference for Graphical Description and Interpretation of Genotype x Environment Interaction. Crop Science, 53, 2400-2410. http://doi.org/10.2135/cropsci2013.04.0218

### Examples

    data(yang.barley)
    dat <- yang.barley

    if(require(reshape2)){
      dat <- acast(dat, gen~site, value.var='yield')
    }

    ## Not run:

      ## For bootstrapping of a biplot, see the non-cran packages:
      ## 'bbplot' and 'distfree.cr'
      ## http://statgen.ualberta.ca/index.html?open=software.html
      ## install.packages("http://statgen.ualberta.ca/download/software/bbplot_1.0.zip")
      ## install.packages("http://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip")

      ## require(SDMTools)
      ## require(distfree.cr)
      ## require(bbplot)

      ## d1 <- bbplot.boot(dat, nsample=2000) # bootstrap the data
      ## plot(d1) # plot distributions of principal components
      ## b1 <- bbplot(d1) # create data structures for the biplot
      ## plot(b1) # create the confidence regions on the biplot

    ## End(Not run)

[Package agridat version 1.16 Index]