[英]ggplot in R: add regression equation in a plot
不久前,我从杰登(Jayden)看到了关于将回归方程添加到绘图中的答案,我发现它非常有用。 但是我不想显示R ^ 2,因此我将代码更改为:
lm_eqn = function(m) {
l <- list(a = format(coef(m)[1], digits = 2),
b = format(abs(coef(m)[2]), digits = 2));
if (coef(m)[2] >= 0) {
eq <- substitute(italic(y) == a + b %.% italic(x))
} else {
eq <- substitute(italic(y) == a - b %.% italic(x))
}
as.character(as.expression(eq));
}
这设法将“ a + bx”或“ a-bx”绘制到该图上,但没有实际系数替换a和b。 有人知道如何解决该问题吗? 非常感谢!
杰登的答案:
lm_eqn = function(m) {
l <- list(a = format(coef(m)[1], digits = 2),
b = format(abs(coef(m)[2]), digits = 2),
r2 = format(summary(m)$r.squared, digits = 3));
if (coef(m)[2] >= 0) {
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,l)
} else {
eq <- substitute(italic(y) == a - b %.% italic(x)*","~~italic(r)^2~"="~r2,l)
}
as.character(as.expression(eq));
}
看来您缺少了l
substitute()
的l
。 也就是说,使用substitute(yourFormula, l)
。 这是一个不带r^2
的MWE,它与您正在查看的MWE相似(我认为这是在图上添加回归线方程和R2 )。
library(ggplot2)
# Function to generate correlated data.
GenCorrData = function(mu, Sig, n = 1000) {
U <- chol(Sig)
Z <- matrix(rnorm(n*length(mu)), nrow = length(mu))
Y <- crossprod(U,Z) + mu
Y <- as.data.frame(t(Y))
names(Y) <- c("x", "y")
return(Y)
}
# Function to add text
LinEqn = function(m) {
l <- list(a = format(coef(m)[1], digits = 2),
b = format(abs(coef(m)[2]), digits = 2));
if (coef(m)[2] >= 0) {
eq <- substitute(italic(y) == a + b %.% italic(x),l)
} else {
eq <- substitute(italic(y) == a - b %.% italic(x),l)
}
as.character(as.expression(eq));
}
# Example
set.seed(700)
n1 <- 1000
mu1 <- c(4, 5)
Sig1 <- matrix(c(1, .8, .8, 1), nrow = length(mu1))
df1 <- GenCorrData(mu1, Sig1, n1)
scatter1 <- ggplot(data = df1, aes(x, y)) +
geom_point(shape = 21, color = "blue", size = 3.5) +
scale_x_continuous(expand = c(0, 0), limits = c(0, 8)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 8))
scatter.line1 <- scatter1 +
geom_smooth(method = "lm", formula = y ~ x, se = FALSE,
color="black", size = 1) +
annotate("text", x = 2, y = 7, color = "black", size = 5,
label = LinEqn(lm(y ~ x, df1)), parse = TRUE)
scatter.line1
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