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

Description Usage Format Details Source References Examples

Description

Multi-environment trial to illustrate stability statistics

Usage

1
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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
data(lu.stability)
dat <- lu.stability

## Not run: 
  
# GxE means. Match Lu 1995 table 1
require(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
require(dplyr)
  dat2 <- group_by(dat, env)
  dat2 <- mutate(dat2, loc.mean=mean(yield))
m4 <- lm(yield ~ gen -1 + gen:loc.mean, data=dat2)
coef(m4) # Match Hwu table 4

# Table 6: Shukla's heterogeneity test
dat2$ge = gl(5,6) # Create a separate ge interaction term  
m6 <- lm(yield ~ gen + env + ge + ge:loc.mean, data=dat2)
m6b <- lm( yield ~ gen + env + ge + loc.mean, 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

agridat documentation built on May 2, 2019, 4:01 p.m.