Question

Columns and first rows of code 我在geom_smooth(method="glm")的同一geom_point图中有几行不同的ggplot2行。我正在寻找确定每条线的回归方程，包括斜率方程。我找到了similar post，但仍然遇到一些问题。我的代码是：

native <- read.csv("native.gather.C4C5C6C7.csv")

ggplot(native, aes(x=YearsPostRelease, y=PercentNative, col=FieldType, linetype=FieldType)) + 
    geom_point(size=0.7) + 
    geom_smooth(data = native, 
                method ="glm", alpha = 0, show.legend = FALSE, linetype = 'solid') +
    scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55)) +
    scale_y_continuous(limits = c(0, 100), 
                       breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)) + 
    ggtitle("Percent Native Through Time")

谢谢！

Answer 1

这是使用here定义的lm_eqn的方法。您可能遇到了问题，因为您的数据与函数的预期输入不匹配。由于没有您的数据，我在这里使用了mtcars，以探讨 cyl 组之间的 mpg 和 wt 之间的关系。在下面，请注意我正在研究的关系的自定义。

lm_eqn <- function(df){
  m <- lm(mpg ~ wt, df);
  eq <- substitute(italic(mpg) == a + b %.% italic(wt)*","~~italic(r)^2~"="~r2, 
                   list(a = format(coef(m)[1], digits = 2), 
                        b = format(coef(m)[2], digits = 2), 
                        r2 = format(summary(m)$r.squared, digits = 3)))
  as.character(as.expression(eq));                 
}

我们可以将其应用于手动定义的数据子集。可能有一种更聪明的方法，可以更自动地将此方法应用于多个组，但是由于难以自动执行智能标签的位置，这可能就足够了。

library(ggplot2); library(dplyr)
ggplot(mtcars, aes(x=wt, y=mpg, 
                   col=as.factor(cyl), linetype=as.factor(cyl))) + 
  geom_point() + 
  geom_smooth(data = mtcars, 
              method ="glm", alpha = 0, show.legend = FALSE, linetype = 'solid') +
  annotate("text", x = 3, y = 30, label = lm_eqn(mtcars %>% filter(cyl == 4)), parse = TRUE) +
  annotate("text", x = 4.3, y = 20, label = lm_eqn(mtcars %>% filter(cyl == 6)), parse = TRUE) +
  annotate("text", x = 4, y = 12, label = lm_eqn(mtcars %>% filter(cyl == 8)), parse = TRUE)

Answer 2

应用Jon所做的贡献，您可以按如下所示对此数据自定义此功能。

同样，很难完全了解基础数据的外观，但是假设您的字段 FieldType 包含三个因素：BSSFields，CSSFields，DSSFields。

# Load data
library(tidyverse)
native <- read.csv("native.gather.C4C5C6C7.csv")

# Define function
lm_eqn <- function(df){
  m <- lm(PercentNative ~ YearsPostRelease, df);
  eq <- substitute(italic(native) == a + b %.% 
italic(YearsPostRelease)*","~~italic(r)^2~"="~r2, 
                   list(a = format(coef(m)[1], digits = 2), 
                        b = format(coef(m)[2], digits = 2), 
                        r2 = format(summary(m)$r.squared, digits = 3)))
  as.character(as.expression(eq));                 
}

# Plot data
ggplot(native, aes(x = YearsPostRelease, 
                   y = PercentNative, 
                   col = FieldType, 
                   linetype = FieldType)) +
  geom_point(size=0.7) + 
  geom_smooth(data = native, 
              method ="glm", alpha = 0, show.legend = FALSE, linetype = 'solid') +
  scale_x_continuous(breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55)) +
  scale_y_continuous(limits = c(0, 100), 
                     breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100)) + 
  annotate("text", x = 3, y = 30, 
           label = lm_eqn(native %>% filter(FieldType == "BSSFields")), parse = TRUE) +
  annotate("text", x = 4, y = 20, 
           label = lm_eqn(native %>% filter(FieldType == "CSSFields")), parse = TRUE) +
  annotate("text", x = 5, y = 10, 
           label = lm_eqn(native %>% filter(FieldType == "DSSFields")), parse = TRUE)
  ggtitle("Percent Native Through Time")

请务必注意，这些回归方程的位置将根据 YearsPostRelease 和 PercentNative 的范围进行修改。另外，如果 FieldTypes 包含三个以上级别，则必须添加针对级别名称定制的相应annotate()调用。

在ggplot2中的同一geom_point图中确定多条线的斜率

2 个答案: