对数刻度图中的误差线? [英] Errorbars in a log scale plot?

问题描述

那么，在对数刻度上绘制点时，呈现误差线的正确方法是什么?因为误差线在绝对刻度上是对称的，所以我认为它们在对数刻度上将是不对称的.但是，使用以下代码，它们在对数刻度上显示为对称.我最初的问题是代码是否正确显示错误栏?"经过一番看，我有点不确定.

So, what is the right way to present error bars when plotting points on a log scale? Because error bars are symetric on the absolute scale, I thought they would be asymmetric on the log scale. However, with the below code, they show up as symmetric on the log scale. My initial question was 'Is the code displaying the error bars properly?' After a little bit of looking I am left a little uncertain.

• 我仍然不能完全确定它是否正确显示错误栏.但是，我倾向于理解它正在显示相对"误差线，它认为这是显示已记录数据的正确误差线.
• 我可以用任何简单的方式在对数刻度上显示绝对误差线吗?是否有一个我忽略的参数可以让我翻转此开关?
• 也许更笼统地说，为什么日志空间中的相对误差线被认为是正确"?

library(ggplot2
pde=1.1 #position dodge for error bars
pdp=0.35 #position dodge for points
p<-ggplot(data=mtcars, aes(x=vs, y=mpg, colour=factor(am)))+
  geom_point(position=position_dodge(width=pdp), size=3)+
  stat_summary( fun = "mean", geom="point", size=2,stroke=1.1, position=position_dodge(width=pde))+ 
  stat_summary( fun.data = "mean_se", geom = "errorbar", width=0.15, position=position_dodge(width=pde))+
  scale_y_log10(limits = c(1,150))

推荐答案

的帮助> coord_trans() 解释了比例转换(例如 scale_y_log10())是在计算统计信息之前执行的，而坐标转换(例如 coord_trans(y ="log10"))是在计算统计信息之后执行的.

The help for coord_trans() explains that scale transformations (e.g., scale_y_log10()) are performed before statistics are calculated, while coordinate transformations (e.g., coord_trans(y="log10")) are performed after statistics are calculated.

在您的情况下，这意味着使用 scale_y_log10 的均值和se是在对数转换后的数据上计算的，而不是对原始未转换的数据进行计算的.要计算未转换数据的统计信息，请删除 scale_y_log10()并使用 coord_trans(y ="log10").

In your case, this means that with scale_y_log10 the mean and se are being calculated on the log-transformed data, rather than on the original untransformed data. To calculate the statistics on the untransformed data, remove scale_y_log10() and use coord_trans(y="log10").

下面的示例显示ggplot在内部计算的值，然后通过直接计算来重现这些值:

The example below shows the values that ggplot is calculating internally and then reproduces those values by direct calculation:

library(tidyverse)

pde=1.1 #position dodge for error bars
pdp=0.35 #position dodge for points

p1 = ggplot(data=mtcars, aes(x=vs, y=mpg, colour=factor(am))) +
  geom_point(position=position_dodge(width=pdp), size=3) +
  stat_summary(fun = "mean", geom="point", size=2, stroke=1.1,
               position=position_dodge(width=pde)) +
  stat_summary( fun.data = "mean_se", geom = "errorbar", 
                width=0.15, position=position_dodge(width=pde)) +
  theme_bw() 

p2 = p1 + scale_y_log10() 

# Get data frames for each set of mean/errorbar layers
#  that ggplot calculates internally 
p1dat = ggplot_build(p1)$data[[3]]
p2dat = ggplot_build(p2)$data[[3]]

p1dat %>% select(y, ymin, ymax)
#>          y     ymin     ymax
#> 1 15.05000 14.24910 15.85090
#> 2 20.74286 19.80888 21.67683
#> 3 19.75000 18.11339 21.38661
#> 4 28.37143 26.57319 30.16967

p2dat %>% select(y, ymin, ymax) %>% 
  mutate(y.trans = 10^y,
         ymax.trans = 10^ymax)
#>          y     ymin     ymax  y.trans ymax.trans
#> 1 1.170219 1.145648 1.194790 14.79853   15.65992
#> 2 1.314225 1.294657 1.333793 20.61699   21.56718
#> 3 1.288104 1.252044 1.324165 19.41353   21.09431
#> 4 1.447286 1.418346 1.476226 28.00826   29.93823

现在通过直接计算来重现这些相同的值:

Now reproduce those same values by direct calculation:

mtcars %>% 
  group_by(am, vs) %>% 
  summarise(mean = mean(mpg),
            mean.log = mean(log10(mpg)),
            mean.log.trans = 10^mean.log,
            mean.plus.se = mean + sqrt(var(mpg)/length(mpg)),
            se.log = sqrt(var(log10(mpg))/length(mpg)),
            mean.log.plus.se = mean.log + se.log,
            mean.log.plus.se.trans = 10^mean.log.plus.se)

#>   am vs     mean mean.log mean.log.trans mean.plus.se     se.log
#> 1  0  0 15.05000 1.170219       14.79853     15.85090 0.02457101
#> 2  0  1 20.74286 1.314225       20.61699     21.67683 0.01956814
#> 3  1  0 19.75000 1.288104       19.41353     21.38661 0.03606088
#> 4  1  1 28.37143 1.447286       28.00826     30.16967 0.02893993
#>   mean.log.plus.se mean.log.plus.se.trans
#> 1         1.194790               15.65992
#> 2         1.333793               21.56718
#> 3         1.324165               21.09431
#> 4         1.476226               29.93823

我们还可以看到 coord_trans(y ="log10")在对数转换之前计算平均值和误差线:

And we can also see that coord_trans(y="log10") calculates means and error bars before the log transformation:

p3 = p1 + coord_trans(y="log10")
p3dat = ggplot_build(p3)$data[[3]]

p3dat %>% select(y, ymin, ymax)
#>          y     ymin     ymax
#> 1 15.05000 14.24910 15.85090
#> 2 20.74286 19.80888 21.67683
#> 3 19.75000 18.11339 21.38661
#> 4 28.37143 26.57319 30.16967

这篇关于对数刻度图中的误差线?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！