使用 stat_reg_line() 函数从 ggplot 中删除回归线方程的截距
removing the intercept from regression line equation from ggplot using stat_reg_line() function
我正在将回归线方程添加到我的 ggplot 中。但是,我想从图中删除截距并只保留斜率和 R^2。
这是我用来生成绘图和方程式的代码。您知道如何删除拦截吗?
library(ggpmisc)
df <- data.frame(x = c(1:100))
df$y <- 20 * c(0, 1) + 3 * df$x + rnorm(100, sd = 40)
df$group <- factor(rep(c("A", "B"), 50))
df <- df %>% group_by(group) %>% mutate(ymax = max(y))
df %>%
group_by(group) %>%
do(tidy(lm(y ~ x, data = .)))
p <- ggplot(data = df, aes(x = x, y = y, colour = group)) +
geom_smooth(method = "lm", se=FALSE, formula = y ~ x) +
stat_regline_equation(
aes( x = x, y = y , label = paste(..eq.label..,..rr.label.., sep = "~~~~")),
formula=y~x, size=3,
)
p
谢谢,
您可以使用 ggpmisc 包中的 stat_fit_tidy
:
df <- data.frame(x = c(1:100))
df$y <- 20 * c(0, 1) + 3 * df$x + rnorm(100, sd = 40)
df$group <- factor(rep(c("A", "B"), 50))
library(ggpmisc)
my_formula <- y ~ x
ggplot(df, aes(x = x, y = y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_fit_tidy(
method = "lm",
method.args = list(formula = my_formula),
mapping = aes(label = sprintf('slope~"="~%.3g',
after_stat(x_estimate))),
parse = TRUE)
编辑
如果您也想要 R 平方:
ggplot(df, aes(x = x, y = y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_fit_tidy(
method = "lm",
method.args = list(formula = my_formula),
mapping = aes(label = sprintf('slope~"="~%.3g',
after_stat(x_estimate))),
parse = TRUE) +
stat_poly_eq(formula = my_formula,
aes(label = ..rr.label..),
parse = TRUE,
label.x = 0.6)
编辑
另一种方式:
myformat <- "Slope: %s --- R²: %s"
ggplot(df, aes(x, y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_poly_eq(
formula = my_formula, output.type = "numeric",
mapping = aes(label =
sprintf(myformat,
formatC(stat(coef.ls)[[1]][[2, "Estimate"]]),
formatC(stat(r.squared)))),
vstep = 0.1
)
我正在将回归线方程添加到我的 ggplot 中。但是,我想从图中删除截距并只保留斜率和 R^2。
这是我用来生成绘图和方程式的代码。您知道如何删除拦截吗?
library(ggpmisc)
df <- data.frame(x = c(1:100))
df$y <- 20 * c(0, 1) + 3 * df$x + rnorm(100, sd = 40)
df$group <- factor(rep(c("A", "B"), 50))
df <- df %>% group_by(group) %>% mutate(ymax = max(y))
df %>%
group_by(group) %>%
do(tidy(lm(y ~ x, data = .)))
p <- ggplot(data = df, aes(x = x, y = y, colour = group)) +
geom_smooth(method = "lm", se=FALSE, formula = y ~ x) +
stat_regline_equation(
aes( x = x, y = y , label = paste(..eq.label..,..rr.label.., sep = "~~~~")),
formula=y~x, size=3,
)
p
谢谢,
您可以使用 ggpmisc 包中的 stat_fit_tidy
:
df <- data.frame(x = c(1:100))
df$y <- 20 * c(0, 1) + 3 * df$x + rnorm(100, sd = 40)
df$group <- factor(rep(c("A", "B"), 50))
library(ggpmisc)
my_formula <- y ~ x
ggplot(df, aes(x = x, y = y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_fit_tidy(
method = "lm",
method.args = list(formula = my_formula),
mapping = aes(label = sprintf('slope~"="~%.3g',
after_stat(x_estimate))),
parse = TRUE)
编辑
如果您也想要 R 平方:
ggplot(df, aes(x = x, y = y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_fit_tidy(
method = "lm",
method.args = list(formula = my_formula),
mapping = aes(label = sprintf('slope~"="~%.3g',
after_stat(x_estimate))),
parse = TRUE) +
stat_poly_eq(formula = my_formula,
aes(label = ..rr.label..),
parse = TRUE,
label.x = 0.6)
编辑
另一种方式:
myformat <- "Slope: %s --- R²: %s"
ggplot(df, aes(x, y, colour = group)) +
geom_point() +
geom_smooth(method = "lm", formula = my_formula, se = FALSE) +
stat_poly_eq(
formula = my_formula, output.type = "numeric",
mapping = aes(label =
sprintf(myformat,
formatC(stat(coef.ls)[[1]][[2, "Estimate"]]),
formatC(stat(r.squared)))),
vstep = 0.1
)