lu.stability: Multi-environment trial of maize, to illustrate stability...

In kwstat/agridat: Agricultural Datasets

Multi-environment trial of maize, to illustrate stability statistics

Description

Multi-environment trial to illustrate stability statistics

Usage

data("lu.stability")

Format

A data frame with 120 observations on the following 4 variables.

yield

yield

gen

genotype factor, 5 levels

env

environment factor, 6 levels

block

block factor, 4 levels

Details

Data for 5 maize genotypes in 2 years x 3 sites = 6 environments.

Source

H.Y. Lu and C. T. Tien. (1993) Studies on nonparametric method of phenotypic stability: II. Selection for stability of agroeconomic concept. J. Agric. Assoc. China 164:1-17.

References

Hsiu Ying Lu. 1995. PC-SAS Program for Estimating Huehn's Nonparametric Stability Statistics. Agron J. 87:888-891.

Kae-Kang Hwu and Li-yu D Liu. (2013) Stability Analysis Using Multiple Environment Trials Data by Linear Regression. (In Chinese) Crop, Environment & Bioinformatics 10:131-142.

Examples

## Not run: 
  
  library(agridat)
  data(lu.stability)
  dat <- lu.stability

  # GxE means. Match Lu 1995 table 1
  libs(reshape2)
  datm <- acast(dat, gen~env, fun=mean, value.var='yield')
  round(datm, 2)
  # Gen/Env means. Match Lu 1995 table 3
  apply(datm, 1, mean)
  apply(datm, 2, mean)
  
  
  # Traditional ANOVA. Match Hwu table 2
  # F value for gen,env
  m1 = aov(yield~env+gen+Error(block:env+env:gen), data=dat)
  summary(m1)   
  # F value for gen:env, block:env
  m2 <- aov(yield ~ gen + env + gen:env + block:env, data=dat) 
  summary(m2)

  # Finlay Wilkinson regression coefficients
  # First, calculate env mean, merge in
  libs(dplyr)
  dat2 <- group_by(dat, env)
  dat2 <- mutate(dat2, locmn=mean(yield))
  m4 <- lm(yield ~ gen -1 + gen:locmn, data=dat2)
  coef(m4) # Match Hwu table 4

# Table 6: Shukla's heterogeneity test
  dat2$ge = paste0(dat2$gen, dat2$env) # Create a separate ge interaction term  
  m6 <- lm(yield ~ gen + env + ge + ge:locmn, data=dat2)
  m6b <- lm( yield ~ gen + env + ge + locmn, data=dat2)
  anova(m6, m6b) # Non-significant difference

  # Table 7 - Shukla stability
  # First, environment means
  emn <- group_by(dat2, env)
  emn <- summarize(emn, ymn=mean(yield))
  # Regress GxE terms on envt means
  getab = (model.tables(m2,"effects")$tables)$'gen:env'
  getab
  for (ll in 1:nrow(getab)){
    m7l <- lm(getab[ll, ] ~ emn$ymn)
    cat("\n\n*************** Gen ",ll," ***************\n") 
    cat("Regression coefficient: ",round(coefficients(m7l)[2],5),"\n") 
    print(anova(m7l)) 
  } # Match Hwu table 7.


## End(Not run) # dontrun

kwstat/agridat documentation built on Nov. 2, 2024, 6:19 a.m.