lu.stability {agridat} R Documentation

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
```

[Package agridat version 1.18 Index]