全基因组选择的交叉验证，BWGS包会直接提供一个预测结果（rrBLUP包也会提供），可以用来绘制相关性散点图来直观显示模型的预测性能
library(stringr)
library(ggplot2)
library(tidyr)
library(dplyr)
library(moments)
library(patchwork)
#需要读取BWGS的结果，或者基于其他GS软件包的内容同理，只需要一个包含表型值和预测值的数据框即可
plot_data_P55 <- as.data.frame(P55_gs$bv_table)
#计算相关性（准确性）r值
all_values_P55 <- c(plot_data_P55$bv_predict_mean, plot_data_P55$pheno)
#提取表型值和预测值，如果直接做好了数据框只需要计算r值后直接输入
all_values_P55 <- c(plot_data_P55$bv_predict_mean, plot_data_P55$pheno)
#基于表型值确定最佳的横纵轴范围区间
common_limits_P55 <- range(all_values_P55, na.rm = TRUE)
#绘制散点图
plot_P55 <- ggplot(plot_data_P55, aes(x = bv_predict_mean, y = pheno)) +
geom_point(color = "#E69F00", alpha = 0.6, size = 2) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray50", size = 0.8) +
geom_smooth(method = "lm", color = "#e74c3c", se = TRUE, fill = "gray80") +
coord_fixed(ratio = 1, xlim = common_limits_P55, ylim = common_limits_P55) +
annotate("text", x = common_limits_P55[1], y = common_limits_P55[2],
label = paste0("r = ", round(accuracy_r_P55, 3)),
hjust = 0, vjust = 1, size = 6, fontface = "italic") +
labs(title = "P5.5 (GBLUP)",
x = "Predicted Value",
y = "Observed Value") +
theme(
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(size = 14),
axis.title = element_text(size = 18),
legend.title = element_text(size = 18),
legend.text = element_text(size = 16),
plot.title = element_text(hjust = 0.5, size = 24, face = "bold"),
legend.position = "none",
strip.text = element_text(size = 12, face = "bold"),
panel.background = element_rect(fill = "white"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 0.5)
)