Nothing
## ----global_options, include = FALSE------------------------------------------
knitr::opts_chunk$set(comment = "#", collapse = TRUE)
## ---- message=FALSE, warning=FALSE--------------------------------------------
library(metan)
inspect(data_ge)
## -----------------------------------------------------------------------------
ge_details(data_ge,
env = ENV,
gen = GEN,
resp = everything())
## ----fig.height=4, fig.width=5------------------------------------------------
ge_plot(data_ge, GEN, ENV, GY)
## -----------------------------------------------------------------------------
mge <- ge_means(data_ge,
env = ENV,
gen = GEN,
resp = everything())
# Genotype-environment means
get_model_data(mge) %>% round_cols()
# Environment means
get_model_data(mge, what = "env_means") %>% round_cols()
# Genotype means
get_model_data(mge, what = "gen_means") %>% round_cols()
## -----------------------------------------------------------------------------
ammi_model <- performs_ammi(data_ge, ENV, GEN, REP, resp = c(GY, HM))
waas_index <- waas(data_ge, ENV, GEN, REP, GY, verbose = FALSE)
## ---- fig.height=12, fig.width=5, message=FALSE, warning=FALSE---------------
a <- plot_scores(ammi_model)
b <- plot_scores(ammi_model,
type = 2,
second = "PC3")
c <- plot_scores(ammi_model,
type = 2,
polygon = TRUE,
col.gen = "black",
col.env = "gray70",
col.segm.env = "gray70",
axis.expand = 1.5)
arrange_ggplot(a, b, c, tag_levels = "a", ncol = 1)
## -----------------------------------------------------------------------------
predicted <- predict(ammi_model, naxis = c(4, 6))
predicted %>%
subset(TRAIT == "GY") %>%
make_mat(GEN, ENV, YpredAMMI) %>%
round_cols()
## ----warning=FALSE------------------------------------------------------------
model2 <- gamem_met(data_ge, ENV, GEN, REP, everything())
## ----fig.height=12, fig.width=4, message=FALSE, warning=FALSE-----------------
plot(model2, which = c(1, 2, 7), ncol = 1)
## ----fig.height=12, fig.width=4-----------------------------------------------
plot(model2, type = "re", nrow = 3)
## -----------------------------------------------------------------------------
get_model_data(model2) %>% round_cols(digits = 3)
## ---- fig.height=8, fig.width=4-----------------------------------------------
library(ggplot2)
d <- plot_blup(model2)
e <- plot_blup(model2,
prob = 0.1,
col.shape = c("gray20", "gray80")) +
coord_flip()
arrange_ggplot(d, e, tag_levels = list(c("d", "e")), ncol = 1)
## -----------------------------------------------------------------------------
get_model_data(model2, what = "blupge") %>%
round_cols()
## -----------------------------------------------------------------------------
model3 <- waasb(data_ge, ENV, GEN, REP, everything(), verbose = FALSE)
get_model_data(model3, what = "WAASB") %>%
round_cols()
## -----------------------------------------------------------------------------
index <- blup_indexes(model3)
get_model_data(index) %>% round_cols()
## ----echo = TRUE--------------------------------------------------------------
gge_model <- gge(data_ge, ENV, GEN, GY)
## ----echo = TRUE, fig.width = 4, fig.height=8, message=F, warning=F-----------
f <- plot(gge_model)
g <- plot(gge_model, type = 2)
arrange_ggplot(e, f, tag_levels = list(c("e", "f")), ncol = 1)
## -----------------------------------------------------------------------------
stat_ge <- ge_stats(data_ge, ENV, GEN, REP, GY)
get_model_data(stat_ge) %>%
round_cols()
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