vargas.txe | R Documentation |
Treatment x environment interaction in agronomy trials
data("vargas.txe.covs")
data("vargas.txe.yield")
The 'vargas.txe.covs' data has 10 years of measurements on 28 environmental covariates:
year
year
MTD
mean maximum temperature in December
MTJ
mean maximum temperature in January
MTF
mean maximum temperature in February
MTM
mean maximum temperature in March
MTA
mean maximum temperature in April
mTD
mean minimum temperature in December
mTJ
mean minimum temperature in January
mTF
mean minimum temperature in February
mTM
mean minimum temperature in March
mTA
mean minimum temperature in April
mTUD
mean minimum temperature in December
mTUJ
mean minimum temperature in January
mTUF
mean minimum temperature in February
mTUM
mean minimum temperature in March
mTUA
mean minimum temperature in April
PRD
total monthly precipitation in December
PRJ
total monthly precipitation in Jan
PRF
total monthly precipitation in Feb
PRM
total monthly precipitation in Mar
SHD
sun hours per day in Dec
SHJ
sun hours per day in Jan
SHF
sun hours per day in Feb
EVD
total monthly evaporation in Dec
EVJ
total monthly evaporation in Jan
EVF
total monthly evaporation in Feb
EVM
total monthly evaporation in Mar
EVA
total monthly evaporation in Apr
The 'vargas.txe.yield' dataframe contains 240 observations on three variables
year
Year
trt
Treatment. See details section
yield
Grain yield, kg/ha
The treatment names indicate:
T | deep knife |
t | no deep knife |
S | sesbania |
s | soybean |
M | chicken manure |
m | no chicken manure |
0 | no nitrogen |
n | 100 kg/ha nitrogen |
N | 200 kg/ha nitrogen |
Used with permission of Jose Crossa.
Vargas, Mateo and Crossa, Jose and van Eeuwijk, Fred and Sayre, Kenneth D. and Reynolds, Matthew P. (2001). Interpreting Treatment x Environment Interaction in Agronomy Trials. Agron. J., 93, 949-960. Table A1, A3. https://doi.org/10.2134/agronj2001.934949x
## Not run:
library(agridat)
data(vargas.txe.covs)
data(vargas.txe.yield)
libs(reshape2)
libs(lattice)
redblue <- colorRampPalette(c("firebrick", "lightgray", "#375997"))
Z <- vargas.txe.yield
Z <- acast(Z, year ~ trt, value.var='yield')
levelplot(Z, col.regions=redblue,
main="vargas.txe.yield", xlab="year", ylab="treatment",
scales=list(x=list(rot=90)))
# Double-centered like AMMI
Z <- sweep(Z, 1, rowMeans(Z))
Z <- sweep(Z, 2, colMeans(Z))
# Vargas figure 1
biplot(prcomp(Z, scale.=FALSE), main="vargas.txe.yield")
# Now, PLS relating the two matrices
U <- vargas.txe.covs
U <- scale(U) # Standardized covariates
libs(pls)
m1 <- plsr(Z~U)
# Vargas Fig 2, flipped vertical/horizontal
biplot(m1, which="x", var.axes=TRUE)
## End(Not run)
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