堆棧內存溢出
  簡體   English   中英

如何使用 R 在 Calibration plot 中添加黃土線、斜率和截距？

[英]How to add the loess line, slope and intercept in Calibration plot using R?

 原文  2021-11-24 20:21:50  1   1    r/ regression/ intercept/ calibration/ ggplot2

問題描述

我想將黃土線、斜率和截距值添加到校准圖中，如下例所示：

在此處輸入圖像描述 在此處輸入圖像描述

您能否向我解釋一下如何在底部“理想，非參數...”中添加這些信息：斜率、截距、圖例？

我的代碼如下：

Y <- c(0.4733333, 0.5133333, 0.5400000, 0.5066667, 0.4400000, 0.4733333, 0.4733333, 0.4600000, 0.3933333, 0.5000000, 0.5533333, 0.6266667, 0.5600000, 0.5800000, 0.6000000, 0.5133333, 0.5066667, 0.5933333, 0.5533333, 0.5266667, 0.6800000, 0.6400000, 0.6333333, 0.7266667, 0.6200000, 0.6400000, 0.6200000, 0.7266667, 0.5800000, 0.6066667, 0.6400000, 0.6600000, 0.6066667, 0.6400000, 0.6600000, 0.7266667, 0.6266667, 0.6933333, 0.7000000, 0.7266667, 0.6866667, 0.6933333, 0.6733333, 0.7666667, 0.7200000, 0.6733333, 0.7666667, 0.7266667, 0.6733333, 0.6733333, 0.7133333, 0.6800000, 0.6733333, 0.6866667, 0.7466667, 0.7533333, 0.7200000, 0.7066667, 0.7533333, 0.7933333, 0.8000000, 0.7466667, 0.7466667, 0.7133333, 0.8133333,
               0.7400000, 0.7666667, 0.6866667, 0.7933333, 0.6866667, 0.7533333, 0.7266667, 0.7533333, 0.7866667, 0.8000000, 0.8200000, 0.7600000, 0.7266667, 0.8200000, 0.8333333, 0.8066667, 0.8533333, 0.7600000, 0.8200000, 0.7600000, 0.8333333, 0.8066667, 0.8333333, 0.7733333, 0.8333333, 0.8600000, 0.8133333, 0.8466667, 0.8266667, 0.8533333, 0.8733333, 0.8666667, 0.8400000, 0.8200000, 0.7866667)

X <- c(0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150,
                0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 
                0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 
                0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 
                0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 
                0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 
                0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 
                0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 
                0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 
                0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516)
attr(X, "names") = rep(as.character(0:9),each = 10)
gr <- rep(1:10,each = 10)
DATA=data.frame(Y,X,gr)
DATA$x2 = as.numeric(as.character(DATA$X))
ggplot(DATA, aes(x2,Y, group = gr))+ 
  xlim(0,1)+
  ylim(0,1)+
  geom_boxplot()+
  geom_abline(slope = 1, intercept = 0)+
  geom_smooth(method = "loess", se=TRUE, aes(group=1))+
  theme_light()+ #scale_colour_gdocs()+
  theme(legend.position="bottom")+
  labs(title="Calibration plot",
       x ="Predicted values",
       y = "Observed values")+
  stat_summary(aes(group=X),fun.y=function(x){mean(x, na.rm = T)}, 
               geom="point", shape=23, col="red")

我得到以下圖表： 在此處輸入圖像描述

其中，紅色圓圈代表平均值，我想添加這些點的截距和斜率（y = a + bx）。 以及通過箱線圖中位數的曲線。

尊敬，

1 個解決方案

解決方案1
 0  2021-11-24 21:42:35

也許這就是你要找的。

library(ggplot2)
library(dplyr)

Y <- c(0.4733333, 0.5133333, 0.5400000, 0.5066667, 0.4400000, 0.4733333, 0.4733333, 0.4600000, 0.3933333, 0.5000000, 0.5533333, 0.6266667, 0.5600000, 0.5800000, 0.6000000, 0.5133333, 0.5066667, 0.5933333, 0.5533333, 0.5266667, 0.6800000, 0.6400000, 0.6333333, 0.7266667, 0.6200000, 0.6400000, 0.6200000, 0.7266667, 0.5800000, 0.6066667, 0.6400000, 0.6600000, 0.6066667, 0.6400000, 0.6600000, 0.7266667, 0.6266667, 0.6933333, 0.7000000, 0.7266667, 0.6866667, 0.6933333, 0.6733333, 0.7666667, 0.7200000, 0.6733333, 0.7666667, 0.7266667, 0.6733333, 0.6733333, 0.7133333, 0.6800000, 0.6733333, 0.6866667, 0.7466667, 0.7533333, 0.7200000, 0.7066667, 0.7533333, 0.7933333, 0.8000000, 0.7466667, 0.7466667, 0.7133333, 0.8133333,
               0.7400000, 0.7666667, 0.6866667, 0.7933333, 0.6866667, 0.7533333, 0.7266667, 0.7533333, 0.7866667, 0.8000000, 0.8200000, 0.7600000, 0.7266667, 0.8200000, 0.8333333, 0.8066667, 0.8533333, 0.7600000, 0.8200000, 0.7600000, 0.8333333, 0.8066667, 0.8333333, 0.7733333, 0.8333333, 0.8600000, 0.8133333, 0.8466667, 0.8266667, 0.8533333, 0.8733333, 0.8666667, 0.8400000, 0.8200000, 0.7866667)

X <- c(0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150, 0.4333150,
                0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 0.5448904, 
                0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 0.6054196, 
                0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 0.6522453, 
                0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 0.6934611, 
                0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 0.7327415, 
                0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 0.7666206, 
                0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 0.7984136, 
                0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 0.8300506, 
                0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516, 0.8732516)

attr(X, "names") = rep(as.character(0:9),each = 10)
gr <- rep(1:10,each = 10)
DATA=data.frame(Y,X,gr)
DATA$x2 = as.numeric(as.character(DATA$X))

DATA2 <- DATA %>%
         group_by(gr) %>%
         summarize(Y=mean(Y), x2=mean(x2))

ggplot(DATA, aes(x2,Y, group = gr))+ 
  xlim(0,1)+
  ylim(0,1)+
  geom_boxplot()+
  geom_smooth(data=DATA2, aes(x2, Y), formula=y~x, method="lm", color="blue", 
              size=1, linetype=1, inherit.aes=F, se=F) +
  geom_abline(slope = 1, intercept = 0)+
  theme_light()+ #scale_colour_gdocs()+
  theme(legend.position="bottom")+
  labs(title="Calibration plot",
       x ="Predicted values",
       y = "Observed values")+
  stat_summary(aes(group=X),fun.y=function(x){mean(x, na.rm = T)}, 
               geom="point", shape=23, col="red")

在此處輸入圖像描述

暫無
暫無

聲明:本站的技術帖子網頁，遵循CC BY-SA 4.0協議，如果您需要轉載，請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.

相關問題 如何將RMSE，斜率，截距，r ^ 2添加到R圖中？ 如何在R中的GAMM模型中添加隨機截距和隨機斜率項 如何在黃土和樣條回歸中添加協變量，然后在 r 中添加 plot 和 ggplot2 找出R內直線的角度，斜率和截距 如何使用R創建具有黃土和線的曲線線段 如何使用ggplot2標記LOESS曲線的斜率變化？ 如何在每個面板中使用不同的截距在多面板圖中添加固定斜率線 R Plotly：在子集上添加黃土線 使用 loess() 將平滑曲線添加到繪圖 如何在ts圖中繪制一條黃土線
相關標簽
 
粵ICP備18138465號  © 2020-2024 STACKOOM.COM