Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(warning = F, message = F, out.width = "60%")
library(dplyr)
## ---- eval=F------------------------------------------------------------------
# install.packages("geneticae")
## ---- eval=F------------------------------------------------------------------
# # install.packages("devtools")
# devtools::install_github("jangelini/geneticae")
## -----------------------------------------------------------------------------
library(geneticae)
## -----------------------------------------------------------------------------
library(agridat)
data(yan.winterwheat)
head(yan.winterwheat)
## -----------------------------------------------------------------------------
data(plrv)
head(plrv)
## ---- fig.align='center', fig.cap='Figure 1: GE biplot based on yield data of 1993 Ontario winter wheat performance trials. The 71.66% of GE variability is explained by the first two multiplicative terms. Cultivars are shown in lowercase and environments in uppercase.'----
rAMMI(yan.winterwheat, genotype = "gen", environment = "env",
response = "yield", type = "AMMI", footnote = F, titles = F)
## -----------------------------------------------------------------------------
GGE1 <- GGEmodel(yan.winterwheat, genotype = "gen", environment = "env",
response = "yield")
## ---- fig.align='center', fig.cap='Figure 2: GGE biplot based on yield data of 1993 Ontario winter wheat performance trials. The scaling method used is symmetrical singular value partitioning (by default). The 78% of G + GE variability is explained by the first two multiplicative terms. Cultivars are shown in lowercase and environments in uppercase.'----
GGEPlot(GGE1, type = "Biplot", footnote = F, titles = F)
## ---- fig.align='center', fig.cap='Figure 3: comparison of cultivar performance in a selected environment (OA93). The scaling method used is symmetrical singular value partitioning (by default). The 78% of G + GE variability is explained by the first two multiplicative terms.'----
GGEPlot(GGE1, type = "Selected Environment", selectedE = "OA93",
footnote = F, titles = F)
## ---- fig.align='center', fig.cap='Figure 4: comparison of the performance of cultivar Luc in different environments. The scaling method used is symmetrical singular value partitioning (by default). The 78% of G + GE variability is explained by the first two multiplicative terms. '----
GGEPlot(GGE1, type = "Selected Genotype", selectedG = "Kat",
footnote = F, titles = F)
## ---- fig.align='center', fig.cap='Figure 5: comparison of the cultivars _Kat_ and _Cas_. The scaling method used is symmetrical singular value partitioning (by default). The 78% of G + GE variability is explained by the first two multiplicative terms. Cultivars are shown in lowercase and environments in uppercase.'----
GGEPlot(GGE1, type = "Comparison of Genotype",
selectedG1 = "Kat", selectedG2 = "Cas",
footnote = F, titles = F, axis_expand = 1.5)
## ---- fig.align='center', fig.cap='Figure 6: polygon view of the GGE biplot, showing which cultivars presented highest yield in each environment. The scaling method used is symmetrical singular value partitioning (by default). The 78% of G + GE variability is explained by the first two multiplicative terms. Cultivars are shown in lowercase and environments in uppercase.'----
GGEPlot(GGE1, type = "Which Won Where/What", footnote = F,
titles = F, axis_expand = 1.5)
## ---- fig.align='center', fig.cap='Figure 7: average environment view of the GGE biplot based on genotype-focused scaling, showing mean yield and stability of genotypes. '----
data <- yan.winterwheat[yan.winterwheat$env %in% c("BH93", "EA93","HW93", "ID93",
"NN93", "RN93", "WP93"), ]
data <- droplevels(data)
GGE2 <- GGEmodel(data, genotype = "gen", environment = "env",
response = "yield", SVP = "row")
GGEPlot(GGE2, type = "Mean vs. Stability", footnote = F, titles = F, sizeEnv = 0)
## ---- fig.align='center', fig.cap='Figure 8: Classification of genotypes with respect to the ideal genotype. Genotype-focused scaling is used.', warning=FALSE----
GGEPlot(GGE2, type = "Ranking Genotypes", footnote = F, titles = F, sizeEnv = 0)
## ---- fig.align='center', fig.cap='Figure 9: Relationship between environments. Environment-focused scaling is used.'----
GGE3 <- GGEmodel(data, genotype = "gen", environment = "env",
response = "yield", SVP = "column")
GGEPlot(GGE3, type = "Relationship Among Environments", footnote = F, titles = F)
## ---- fig.align='center', fig.cap='Figure 10: classification of environments with respect to the ideal environment. Environment-focused scaling is used.'----
GGEPlot(GGE3, type = "Ranking Environments", footnote = F, titles = F)
## -----------------------------------------------------------------------------
# Generating missing data
yan.winterwheat[1,3] <- NA
yan.winterwheat[3,3] <- NA
yan.winterwheat[2,3] <- NA
## -----------------------------------------------------------------------------
imputation(yan.winterwheat, nPC = 2, genotype = "gen", environment = "env",
response = "yield", type = "EM-AMMI")
## ---- echo = FALSE------------------------------------------------------------
sessionInfo()
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