agridat (version 1.27)

scott.augmented: Augmented RCB experiment using soybean

Description

Augmented RCB experiment using soybean

Usage

data("scott.augmented")

Format

A data frame with 30 observations on the following 4 variables.

gen

genotype

check

Check indicator Y/N

block

Block

yield

Yield

Details

Experiment conducted in 1991 at the South Dakota State University Agronomy Farm, Brookings, SD.

The experiment is an augmented Randomized Complete Block.

Four soybean genotypes were replicated 3 times. Also, 17 genotypes were included, but not replicated. These appeared randomly in the different blocks. Block 3 contained another replicate of one of the check geenotypes.

Data provenance. Typed by K.Wright.

References

Nonne

Examples

if (FALSE) {
library(agridat)
data(scott.augmented)
dat <- scott.augmented

# Create separate factors for check genotypes and new genotypes

library(dplyr)
dat <- mutate(dat, 
              gen=factor(gen),
              # gen_check is gen name for checks, 0 for non-checks
              gen_check = ifelse(check=="Y", as.character(gen),0),
              # gen_new is 0 for checks, gen name for non-checks
              gen_new = ifelse(check=="N", as.character(gen),0),
              gen_check=factor(gen_check),
              gen_new=factor(gen_new),
              block=factor(block))

# lmer version
library(lme4)
m1.lme <- lmer(yield ~ -1 + gen_check + (1|gen_new:gen_check) + (1|block), data=dat)
# The fixed effects match Scott Table 1
fixef(m1.lme)
# Random effect predictions
p1fix <- ranef(m1.lme)$"gen_new:gen_check" + fixef(m1.lme)[1]
p1fix |> filter(row_number() > 4) |> head()

# asreml version
#
# dat <- mutate(dat, check=factor(check))
# m2 <- asreml(yield~ at(check,"Y"):gen, data=dat,
#                   random= ~ block + at(check,"N"):gen)
# p2fix <- predict(m2,
#              classify="check:gen", levels=list(check="Y"))$pvals
# filter(p2fix, !is.na(predicted.value))
# p2ran <- predict(m2,
#                  classify="check:gen", levels=list(check="N"))$pvals
# filter(p2ran, !(entry 

}