dat <- digby.jointregression
# Simple gen means, ignoring unbalanced data
round(tapply(dat$yield, dat$gen, mean),3)
n.gen <- nlevels(dat$gen)
n.env <- nlevels(dat$env)
# Estimate theta (env eff)
m0 <- lm(yield ~ -1 + env + gen, dat)
thetas <- coef(m0)[1:n.env]
thetas <- thetas-mean(thetas) # center env effects
# Add env effects to the data
dat$theta <- thetas[match(paste("env",dat$env,sep=""), names(thetas))]
# Initialize beta (gen slopes) at 1
betas <- rep(1, n.gen)
done <- FALSE
while(!done){
betas0 <- betas
# M1: Fix thetas (env effects), estimate beta (gen slope)
m1 <- lm(yield ~ -1 + gen + gen:theta, data=dat)
betas <- coef(m1)[-c(1:n.gen)]
dat$beta <- betas[match(paste("gen",dat$gen,":theta",sep=""), names(betas))]
# print(betas)
# M2: Fix betas (gen slopes), estimate theta (env slope)
m2 <- lm(yield ~ env:beta + gen -1, data=dat)
thetas <- coef(m2)[-c(1:n.gen)]
thetas[is.na(thetas)] <- 0 # Change last coefficient from NA to 0
dat$theta <- thetas[match(paste("env",dat$env,":beta",sep=""), names(thetas))]
print(thetas)
# Check convergence
chg <- sum(((betas-betas0)/betas0)^2)
cat("Relative change in betas",chg,"")
if(chg < .0001) done <- TRUE
}
# Dibgy Table 2, modified joint regression
round(betas,3)
# genG01 genG02 genG03 genG04 genG05 genG06 genG07 genG08 genG09 genG10
# 0.953 0.739 1.082 1.024 1.142 0.877 1.089 0.914 1.196 0.947
round(thetas,3)+1.164-.515 # re-parameterize to match Digby
# envE01 envE02 envE03 envE04 envE05 envE06 envE07 envE08 envE09 envE10
# -0.515 -0.578 -0.990 -1.186 1.811 1.696 -1.096 0.046 0.057 0.825
# envE11 envE12 envE13 envE14 envE15 envE16 envE17
# -0.576 1.568 -0.779 -0.692 0.836 -1.080 0.649Run the code above in your browser using DataLab