metan_start.R
In metan: Multi Environment Trials Analysis

## ----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()