有没有一种巧妙的方法可以用 geom_quantile() 中的方程和其他统计数据来标记 ggplot 图？
Is there a neat approach to label a ggplot plot with the equation and other statistics from geom_quantile()?
我想以类似于 geom_smooth(method="lm") 拟合线性回归（我之前使用 ggpmisc 即 awesome)。例如，这段代码：
# quantile regression example with ggpmisc equation
# basic quantile code from here:
# https://ggplot2.tidyverse.org/reference/geom_quantile.html
library(tidyverse)
library(ggpmisc)
# see ggpmisc vignette for stat_poly_eq() code below:
# https://cran.r-project.org/web/packages/ggpmisc/vignettes/user-guide.html#stat_poly_eq
my_formula <- y ~ x
#my_formula <- y ~ poly(x, 3, raw = TRUE)
# linear ols regression with equation labelled
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m +
geom_smooth(method = "lm", formula = my_formula) +
stat_poly_eq(aes(label = paste(stat(eq.label), "*\" with \"*",
stat(rr.label), "*\", \"*",
stat(f.value.label), "*\", and \"*",
stat(p.value.label), "*\".\"",
sep = "")),
formula = my_formula, parse = TRUE, size = 3)
生成这个：
对于分位数回归，您可以将 geom_smooth() 换成 geom_quantile() 并绘制一条可爱的分位数回归线（在本例中为中位数）：
# quantile regression - no equation labelling
m +
geom_quantile(quantiles = 0.5)
您如何将摘要统计信息输出到标签中，或者如何在旅途中重新创建它们？ （即除了在调用 ggplot 之前进行回归，然后将其传递给然后注释（例如，类似于线性回归所做的 or ？
@mark-neal stat_fit_glance() 确实适用于 quantreg::rq()。然而，使用 stat_fit_glance() 涉及更多。此统计数据不“知道”从 glance() 中得到什么，因此必须手动 assemble 和 label。
需要知道对此有什么可用的。可以 运行 在 ggplot 之外拟合模型并使用 glance() 找出它 return 的列，或者可以在包 [=67= 的帮助下在 ggplot 中执行此操作].我将展示这个替代方案，从上面的代码示例继续。
library(gginnards)
m +
geom_quantile(quantiles = 0.5) +
stat_fit_glance(method = "rq", method.args = list(formula = y ~ x), geom = "debug")
geom_debug() 默认情况下只是将它的输入打印到 R 控制台，它的输入就是统计 returns.
# A tibble: 1 x 11
npcx npcy tau logLik AIC BIC df.residual x y PANEL group
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <fct> <int>
1 NA NA 0.5 816. -1628. -1621. 232 1.87 0.0803 1 -1
我们可以使用 after_stat()（早期版本为 stat() 并包含名称 ...）访问每一列。我们需要使用 ... 的编码符号进行格式化=26=]。如果像本例我们assemble一个需要解析成表达式的字符串，那么还需要parse = TRUE。
m +
geom_quantile(quantiles = 0.5) +
stat_fit_glance(method = "rq", method.args = list(formula = y ~ x),
mapping = aes(label = sprintf('italic(tau)~"="~%.2f~~AIC~"="~%.3g~~BIC~"="~%.3g',
after_stat(tau), after_stat(AIC), after_stat(BIC))),
parse = TRUE)
此示例生成以下图。
对于 stat_fit_tidy()，同样的方法应该有效。但是，在 'ggpmisc' (<= 0.3.7) 中，它使用“lm”而不是“rq”。此错误已在 'ggpmisc' (>= 0.3.8) 中修复，现在在 CRAN 中。
下面的示例仅适用于 'ggpmisc' (>= 0.3.8)
剩下的问题是glance()或tidy()return中的tibble是否包含了想要添加到剧情中的信息，这似乎不是tidy.qr() 的情况，至少在默认情况下是这样。但是，tidy.rq() 有一个参数 se.type，用于确定 return 在 tibble 中编辑的值。修订后的 stat_fit_tidy() 接受要传递给 tidy() 的命名参数，使以下成为可能。
m +
geom_quantile(quantiles = 0.5) +
stat_fit_tidy(method = "rq",
method.args = list(formula = y ~ x),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('y~"="~%.3g~+~%.3g~x*", with "*italic(P)~"="~%.3f',
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE)
此示例生成以下图。
定义一个新的统计数据 stat_rq_eq() 会使这更简单：
stat_rq_eqn <- function(formula = y ~ x, tau = 0.5, ...) {
stat_fit_tidy(method = "rq",
method.args = list(formula = formula, tau = tau),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('y~"="~%.3g~+~%.3g~x*", with "*italic(P)~"="~%.3f',
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE,
...)
}
答案变为：
m +
geom_quantile(quantiles = 0.5) +
stat_rq_eqn(tau = 0.5)
请将此视为 Pedro 出色回答的附录，他在其中完成了大部分繁重的工作 - 这增加了一些演示调整（颜色和线型）和代码以简化多个分位数，生成如下图：
library(tidyverse)
library(ggpmisc) #ensure version 0.3.8 or greater
library(quantreg)
library(generics)
my_formula <- y ~ x
#my_formula <- y ~ poly(x, 3, raw = TRUE)
# base plot
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
# function for labelling
# Doesn't neatly handle P values (e.g return "P<0.001 where appropriate)
stat_rq_eqn <- function(formula = y ~ x, tau = 0.5, colour = "red", label.y = 0.9, ...) {
stat_fit_tidy(method = "rq",
method.args = list(formula = formula, tau = tau),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('italic(tau)~"="~%.3f~";"~y~"="~%.3g~+~%.3g~x*", with "~italic(P)~"="~%.3g',
after_stat(x_tau),
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE,
colour = colour,
label.y = label.y,
...)
}
# This works, though with double entry of plot specs
# custom colours and linetype
#
#
m +
geom_quantile(quantiles = c(0.1, 0.5, 0.9),
aes(colour = as.factor(..quantile..),
linetype = as.factor(..quantile..))
)+
scale_color_manual(values = c("red","purple","darkgreen"))+
scale_linetype_manual(values = c("dotted", "dashed", "solid"))+
stat_rq_eqn(tau = 0.1, colour = "red", label.y = 0.9)+
stat_rq_eqn(tau = 0.5, colour = "purple", label.y = 0.95)+
stat_rq_eqn(tau = 0.9, colour = "darkgreen", label.y = 1.0)+
theme(legend.position = "none") # suppress legend
# not a good habit to have double entry above
# modified with reference to tibble for plot specs,
# though still a stat_rq_eqn call for each quantile and manual vertical placement
# https://www.r-bloggers.com/2019/06/curly-curly-the-successor-of-bang-bang/
my_tau = c(0.1, 0.5, 0.9)
my_colours = c("red","purple","darkgreen")
my_linetype = c("dotted", "dashed", "solid")
quantile_plot_specs <- tibble(my_tau, my_colours, my_linetype)
m +
geom_quantile(quantiles = {{quantile_plot_specs$my_tau}},
aes(colour = as.factor(..quantile..),
linetype = as.factor(..quantile..))
)+
scale_color_manual(values = {{quantile_plot_specs$my_colours}})+
scale_linetype_manual(values = {{quantile_plot_specs$my_linetype}})+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[1]}}, colour = {{quantile_plot_specs$my_colours[1]}}, label.y = 0.9)+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[2]}}, colour = {{quantile_plot_specs$my_colours[2]}}, label.y = 0.95)+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[3]}}, colour = {{quantile_plot_specs$my_colours[3]}}, label.y = 1.0)+
theme(legend.position = "none")
Package 'ggpmisc' (>= 0.4.5) 允许更简单的答案，这更接近@MarkNeal 在他关于中值回归的问题中所希望的解决方案。当使用最新版本的 'ggpmisc' 时，这个答案应该优于早期的答案。未显示：将 se = FALSE 传递给 stat_quant_line() 会禁用置信带。
library(ggplot2)
library(ggpmisc)
#> Loading required package: ggpp
#>
#> Attaching package: 'ggpp'
#> The following object is masked from 'package:ggplot2':
#>
#> annotate
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m +
stat_quant_line(quantiles = 0.5) +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
quantiles = 0.5,
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03
默认绘制中位数和四分位数。
m +
stat_quant_line() +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03
我们还可以轻松地将分位数映射到 color 和 linetype 美学。
m +
stat_quant_line(aes(linetype = after_stat(quantile.f),
color = after_stat(quantile.f))) +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = ""),
color = after_stat(quantile.f)),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03
我们还可以使用 stat_quant_band() 而不是 stat_quant_line() 将四分位数绘制为带。
m +
stat_quant_band() +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03
我想以类似于 geom_smooth(method="lm") 拟合线性回归（我之前使用 ggpmisc 即 awesome)。例如，这段代码：
# quantile regression example with ggpmisc equation
# basic quantile code from here:
# https://ggplot2.tidyverse.org/reference/geom_quantile.html
library(tidyverse)
library(ggpmisc)
# see ggpmisc vignette for stat_poly_eq() code below:
# https://cran.r-project.org/web/packages/ggpmisc/vignettes/user-guide.html#stat_poly_eq
my_formula <- y ~ x
#my_formula <- y ~ poly(x, 3, raw = TRUE)
# linear ols regression with equation labelled
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m +
geom_smooth(method = "lm", formula = my_formula) +
stat_poly_eq(aes(label = paste(stat(eq.label), "*\" with \"*",
stat(rr.label), "*\", \"*",
stat(f.value.label), "*\", and \"*",
stat(p.value.label), "*\".\"",
sep = "")),
formula = my_formula, parse = TRUE, size = 3)
生成这个：
对于分位数回归，您可以将 geom_smooth() 换成 geom_quantile() 并绘制一条可爱的分位数回归线（在本例中为中位数）：
# quantile regression - no equation labelling
m +
geom_quantile(quantiles = 0.5)
您如何将摘要统计信息输出到标签中，或者如何在旅途中重新创建它们？ （即除了在调用 ggplot 之前进行回归，然后将其传递给然后注释（例如，类似于线性回归所做的
@mark-neal stat_fit_glance() 确实适用于 quantreg::rq()。然而，使用 stat_fit_glance() 涉及更多。此统计数据不“知道”从 glance() 中得到什么，因此必须手动 assemble 和 label。
需要知道对此有什么可用的。可以 运行 在 ggplot 之外拟合模型并使用 glance() 找出它 return 的列，或者可以在包 [=67= 的帮助下在 ggplot 中执行此操作].我将展示这个替代方案，从上面的代码示例继续。
library(gginnards)
m +
geom_quantile(quantiles = 0.5) +
stat_fit_glance(method = "rq", method.args = list(formula = y ~ x), geom = "debug")
geom_debug() 默认情况下只是将它的输入打印到 R 控制台，它的输入就是统计 returns.
# A tibble: 1 x 11
npcx npcy tau logLik AIC BIC df.residual x y PANEL group
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <fct> <int>
1 NA NA 0.5 816. -1628. -1621. 232 1.87 0.0803 1 -1
我们可以使用 after_stat()（早期版本为 stat() 并包含名称 ...）访问每一列。我们需要使用 ... 的编码符号进行格式化=26=]。如果像本例我们assemble一个需要解析成表达式的字符串，那么还需要parse = TRUE。
m +
geom_quantile(quantiles = 0.5) +
stat_fit_glance(method = "rq", method.args = list(formula = y ~ x),
mapping = aes(label = sprintf('italic(tau)~"="~%.2f~~AIC~"="~%.3g~~BIC~"="~%.3g',
after_stat(tau), after_stat(AIC), after_stat(BIC))),
parse = TRUE)
此示例生成以下图。
对于 stat_fit_tidy()，同样的方法应该有效。但是，在 'ggpmisc' (<= 0.3.7) 中，它使用“lm”而不是“rq”。此错误已在 'ggpmisc' (>= 0.3.8) 中修复，现在在 CRAN 中。
下面的示例仅适用于 'ggpmisc' (>= 0.3.8)
剩下的问题是glance()或tidy()return中的tibble是否包含了想要添加到剧情中的信息，这似乎不是tidy.qr() 的情况，至少在默认情况下是这样。但是，tidy.rq() 有一个参数 se.type，用于确定 return 在 tibble 中编辑的值。修订后的 stat_fit_tidy() 接受要传递给 tidy() 的命名参数，使以下成为可能。
m +
geom_quantile(quantiles = 0.5) +
stat_fit_tidy(method = "rq",
method.args = list(formula = y ~ x),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('y~"="~%.3g~+~%.3g~x*", with "*italic(P)~"="~%.3f',
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE)
此示例生成以下图。
定义一个新的统计数据 stat_rq_eq() 会使这更简单：
stat_rq_eqn <- function(formula = y ~ x, tau = 0.5, ...) {
stat_fit_tidy(method = "rq",
method.args = list(formula = formula, tau = tau),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('y~"="~%.3g~+~%.3g~x*", with "*italic(P)~"="~%.3f',
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE,
...)
}
答案变为：
m +
geom_quantile(quantiles = 0.5) +
stat_rq_eqn(tau = 0.5)
请将此视为 Pedro 出色回答的附录，他在其中完成了大部分繁重的工作 - 这增加了一些演示调整（颜色和线型）和代码以简化多个分位数，生成如下图：
library(tidyverse)
library(ggpmisc) #ensure version 0.3.8 or greater
library(quantreg)
library(generics)
my_formula <- y ~ x
#my_formula <- y ~ poly(x, 3, raw = TRUE)
# base plot
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
# function for labelling
# Doesn't neatly handle P values (e.g return "P<0.001 where appropriate)
stat_rq_eqn <- function(formula = y ~ x, tau = 0.5, colour = "red", label.y = 0.9, ...) {
stat_fit_tidy(method = "rq",
method.args = list(formula = formula, tau = tau),
tidy.args = list(se.type = "nid"),
mapping = aes(label = sprintf('italic(tau)~"="~%.3f~";"~y~"="~%.3g~+~%.3g~x*", with "~italic(P)~"="~%.3g',
after_stat(x_tau),
after_stat(Intercept_estimate),
after_stat(x_estimate),
after_stat(x_p.value))),
parse = TRUE,
colour = colour,
label.y = label.y,
...)
}
# This works, though with double entry of plot specs
# custom colours and linetype
#
#
m +
geom_quantile(quantiles = c(0.1, 0.5, 0.9),
aes(colour = as.factor(..quantile..),
linetype = as.factor(..quantile..))
)+
scale_color_manual(values = c("red","purple","darkgreen"))+
scale_linetype_manual(values = c("dotted", "dashed", "solid"))+
stat_rq_eqn(tau = 0.1, colour = "red", label.y = 0.9)+
stat_rq_eqn(tau = 0.5, colour = "purple", label.y = 0.95)+
stat_rq_eqn(tau = 0.9, colour = "darkgreen", label.y = 1.0)+
theme(legend.position = "none") # suppress legend
# not a good habit to have double entry above
# modified with reference to tibble for plot specs,
# though still a stat_rq_eqn call for each quantile and manual vertical placement
# https://www.r-bloggers.com/2019/06/curly-curly-the-successor-of-bang-bang/
my_tau = c(0.1, 0.5, 0.9)
my_colours = c("red","purple","darkgreen")
my_linetype = c("dotted", "dashed", "solid")
quantile_plot_specs <- tibble(my_tau, my_colours, my_linetype)
m +
geom_quantile(quantiles = {{quantile_plot_specs$my_tau}},
aes(colour = as.factor(..quantile..),
linetype = as.factor(..quantile..))
)+
scale_color_manual(values = {{quantile_plot_specs$my_colours}})+
scale_linetype_manual(values = {{quantile_plot_specs$my_linetype}})+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[1]}}, colour = {{quantile_plot_specs$my_colours[1]}}, label.y = 0.9)+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[2]}}, colour = {{quantile_plot_specs$my_colours[2]}}, label.y = 0.95)+
stat_rq_eqn(tau = {{quantile_plot_specs$my_tau[3]}}, colour = {{quantile_plot_specs$my_colours[3]}}, label.y = 1.0)+
theme(legend.position = "none")
Package 'ggpmisc' (>= 0.4.5) 允许更简单的答案，这更接近@MarkNeal 在他关于中值回归的问题中所希望的解决方案。当使用最新版本的 'ggpmisc' 时，这个答案应该优于早期的答案。未显示：将 se = FALSE 传递给 stat_quant_line() 会禁用置信带。
library(ggplot2)
library(ggpmisc)
#> Loading required package: ggpp
#>
#> Attaching package: 'ggpp'
#> The following object is masked from 'package:ggplot2':
#>
#> annotate
m <- ggplot(mpg, aes(displ, 1 / hwy)) +
geom_point()
m +
stat_quant_line(quantiles = 0.5) +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
quantiles = 0.5,
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03默认绘制中位数和四分位数。
m +
stat_quant_line() +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03我们还可以轻松地将分位数映射到 color 和 linetype 美学。
m +
stat_quant_line(aes(linetype = after_stat(quantile.f),
color = after_stat(quantile.f))) +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = ""),
color = after_stat(quantile.f)),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03我们还可以使用 stat_quant_band() 而不是 stat_quant_line() 将四分位数绘制为带。
m +
stat_quant_band() +
stat_quant_eq(aes(label = paste(after_stat(eq.label), "*\" with \"*",
after_stat(rho.label), "*\", \"*",
after_stat(n.label), "*\".\"",
sep = "")),
size = 3)
#> Warning in rq.fit.br(x, y, tau = tau, ci = TRUE, ...): Solution may be nonunique
由 reprex package (v2.0.1)
创建于 2022-06-03