## ----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()