wheat {gnm}R Documentation

Wheat Yields from Mexican Field Trials

Description

Data from a 10-year experiment at the CIMMYT experimental station located in the Yaqui Valley near Ciudad Obregon, Sonora, Mexico — factorial design using 24 treatments in all. In each of the 10 years the experiment was arranged in a randomized complete block design with three replicates.

Usage

data(wheat)

Format

A data frame with 240 observations on the following 33 variables.

yield
numeric, mean yield in kg/ha for 3 replicates
year
a factor with levels 1988:1997
tillage
a factor with levels T t
summerCrop
a factor with levels S s
manure
a factor with levels M m
N
a factor with levels 0 N n
MTD
numeric, mean max temp sheltered (deg C) in December
MTJ
same for January
MTF
same for February
MTM
same for March
MTA
same for April
mTD
numeric, mean min temp sheltered (deg C) in December
mTJ
same for January
mTF
same for February
mTM
same for March
mTA
same for April
mTUD
numeric, mean min temp unsheltered (deg C)in December
mTUJ
same for January
mTUF
same for February
mTUM
same for March
mTUA
same for April
PRD
numeric, total precipitation (mm) in December
PRJ
same for January
PRF
same for February
PRM
same for March
SHD
numeric, mean sun hours in December
SHJ
same for January
SHF
same for February
EVD
numeric, total evaporation (mm) in December
EVJ
same for January
EVF
same for February
EVM
same for March
EVA
same for April

Source

Tables A1 and A3 of Vargas, M, Crossa, J, van Eeuwijk, F, Sayre, K D and Reynolds, M P (2001). Interpreting treatment by environment interaction in agronomy trials. Agronomy Journal 93, 949–960.

Examples

set.seed(1)
data(wheat)

##  Scale yields to reproduce analyses reported in Vargas et al (2001)
yield.scaled <- wheat$yield * sqrt(3/1000)

##  Reproduce (up to error caused by rounding) Table 1 of Vargas et al (2001)
aov(yield.scaled ~ year*tillage*summerCrop*manure*N, data = wheat)
treatment <- interaction(wheat$tillage, wheat$summerCrop, wheat$manure,
                         wheat$N, sep = "")
mainEffects <- lm(yield.scaled ~ year + treatment, data = wheat)
svdStart <- residSVD(mainEffects, year, treatment, 3)
bilinear1 <- update(asGnm(mainEffects), . ~ . + 
                    Mult(year, treatment),
                    start = c(coef(mainEffects), svdStart[,1]))
bilinear2 <- update(bilinear1, . ~ . + 
                    Mult(year, treatment, inst = 2),
                    start = c(coef(bilinear1), svdStart[,2]))
bilinear3 <- update(bilinear2, . ~ . + 
                    Mult(year, treatment, inst = 3),
                    start = c(coef(bilinear2), svdStart[,3]))
anova(mainEffects, bilinear1, bilinear2, bilinear3)

##  Examine the extent to which, say, mTF explains the first bilinear term
bilinear1mTF <- gnm(yield.scaled ~ year + treatment + Mult(1 + mTF, treatment),
                    family = gaussian, data = wheat)
anova(mainEffects, bilinear1mTF, bilinear1)

[Package gnm version 0.9-8 Index]