什么是R方？这6张图会让你终身难忘~
什么是R2 ？
在回归模型中，因变量（y）总的方差（信息）可以被称作总平方和（Total sum of squares，TSS），它由两部分组成[1]：
1. 模型可以解释的那部分信息（Model sum of squares, MSS）
2. 模型解释不了的那部分信息，也称为error（Residual sum of squares, RSS）
R2 指的是模型可以解释的那部分信息所占的百分比，即MSS/TSS。
如果R2越大，那该模型能解释的部分也就越多，模型当然就越佳。
上述的概念看上去枯燥，并不是那么有意思。
所以，小编接下来将会用图片呈现6个不同大小的R2，有助于了解不同R2到底“长”什么样，一定让你终身难忘~
首先载入所需R包：
# install.packages("correlation")
# install.packages("ggplot2")
# install.packages("patchwork")
library(correlation) # 用于创建数据
library(ggplot2)
library(patchwork)马上开始作图
。
第一张图：R2 = 0%
mydata_0 <- simulate_simpson(n = 500, r = 0, groups = 1)
p1 <- ggplot(mydata_0, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "firebrick1") +
geom_smooth(method = "lm", se = FALSE, color = "firebrick1") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 0%") +
labs(x = "", y = "")
p1
第二张图： R2 = 10%
mydata_0.1 <- simulate_simpson(n = 500, r = sqrt(0.1), groups = 1)
p2 <- ggplot(mydata_0.1, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "deepskyblue3") +
geom_smooth(method = "lm", se = FALSE, color = "deepskyblue3") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 10%") +
labs(x = "", y = "")
p2
第三张图： R2 = 50%
mydata_0.5 <- simulate_simpson(n = 500, r = sqrt(0.5), groups = 1)
p3 <- ggplot(mydata_0.5, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "goldenrod1") +
geom_smooth(method = "lm", se = FALSE, color = "goldenrod1") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 50%") +
labs(x = "", y = "")
p3
第四张图： R2 = 70%
mydata_0.7 <- simulate_simpson(n = 500, r = sqrt(0.7), groups = 1)
p4 <- ggplot(mydata_0.7, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "mediumpurple1") +
geom_smooth(method = "lm", se = FALSE, color = "mediumpurple1") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 70%") +
labs(x = "", y = "")
p4
第五张图： R2 = 90%
mydata_0.9 <- simulate_simpson(n = 500, r = sqrt(0.9), groups = 1)
p5 <- ggplot(mydata_0.9, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "orange3") +
geom_smooth(method = "lm", se = FALSE, color = "orange3") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 90%") +
labs(x = "", y = "")
p5
第六张图： R2 = 100%
mydata_1 <- simulate_simpson(n = 500, r = sqrt(1), groups = 1)
p6 <- ggplot(mydata_1, aes(V1, V2)) +
geom_point(shape = 1, fill = "white", color = "palegreen4") +
geom_smooth(method = "lm", se = FALSE, color = "palegreen4") +
theme_minimal() +
annotate("text", x = 3, y = -3, label = "R-squared: 100%") +
labs(x = "", y = "")
p6
最后，将6张图片合并，然后点击收藏
：
(p1 + p2 + p3) / (p4 + p5 + p6)
好啦，今天的内容就到这里。
如果有帮助，记得分享给需要的人
！
参考文献
[1]. The Elements of Statistical Learning
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▌编辑：June
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