Yield of 6 barley varieties at 18 locations in Alberta.

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.

sitelonglat
Beaverlodge119.4355.21
BigLakes113.7053.61
Calmar113.8553.26
CdcNorth113.3353.63
DawsonCreek120.2355.76
FtKent110.6154.31
FtStJohn120.8556.25
Irricana113.6051.32
Killam111.8552.78
Lacombe113.7352.46
LethbridgeDry112.8149.70
LethbridgeIrr112.8149.70
Lomond112.6550.35
Neapolis113.8651.65
NorthernSunriseNANA
Olds114.0951.78
StPaul111.2853.98
Stettler112.7152.31

Used with permission of Rong-Cai Yang.

Source

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

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. https://doi.org/10.2135/cropsci2013.04.0218

Examples

# \dontrun{ library(agridat) data(yang.barley) dat <- yang.barley libs(reshape2) dat <- acast(dat, gen~site, value.var='yield') ## For bootstrapping of a biplot, see the non-cran packages: ## 'bbplot' and 'distfree.cr' ## https://statgen.ualberta.ca/index.html?open=software.html ## install.packages("https://statgen.ualberta.ca/download/software/bbplot_1.0.zip") ## install.packages("https://statgen.ualberta.ca/download/software/distfree.cr_1.5.zip") ## libs(SDMTools) ## libs(distfree.cr) ## libs(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 # }