从简单的拟合中预测x值并在图中标注它 [英] predict x values from simple fitting and annoting it in the plot

问题描述

我有一个非常简单的问题，但到目前为止找不到简单的解决方案。假设我有一些我想要拟合的数据，并显示其x轴值，其中y是特别值。在这种情况下，让我们说y = 0时x值是多少。模型是非常简单的y〜x拟合，但我不知道如何从那里估计x值。无论如何，

样本数据

  library（ggplot2）
库（比例）
 df = data.frame（x = sort（10 ^ runif（8，-6,1），decrease = TRUE），y = seq（-4,4，length.out = 8 ））
 
 ggplot（df，aes（x = x，y = y））+ 
 geom_point（）+ 
 #geom_smooth（method =lm，formula = y 〜x，size = 1，linetype =dashed，col =black，se = FALSE，fullrange = TRUE）+ 
 geom_smooth（se = FALSE）+ 
 labs（title = （break）= c（1e-6,1e-4,1e-2,1），
 labels = trans_format（log10，math_format（10 ^ .x）））+ 
 scale_x_log10 ，限制= c（1e-6,1））+ 
 geom_hline（yintercept = 0，linetype =dashed，color =red，size = 0.6）
  

我想将1e-10输入转换为10 ^ -10格式并在图上加注释。正如我在剧情中指出的那样。

先谢谢你！

解决方案

因为 geom_smooth（）使用R函数来计算平滑线，在 ggplot（）环境之外的预测值。然后一个选择是使用 approx（）得到x值的线性近似值，给定预测的y值 0 $ b

 ＃定义公式
公式<  - 黄土（y〜x，df）
 
＃y近似为0 
 xval < -  approx（x = formula $ fitted，y = formula $ x，xout = 0）$ y 
 
＃添加绘制
 ggplot（...）+ annotate（text，x = xval，y = 0，label = yval）
  

I have a very simple question but so far couldn't find easy solution for that. Let's say I have a some data that I want to fit and show its x axis value where y is in particular value. In this case let's say when y=0 what is the x value. Model is very simple y~x for fitting but I don't know how to estimate x value from there. Anyway,

sample data

library(ggplot2)
library(scales)
df = data.frame(x= sort(10^runif(8,-6,1),decreasing=TRUE), y = seq(-4,4,length.out = 8))

ggplot(df, aes(x = x, y = y)) +
  geom_point() +
  #geom_smooth(method = "lm", formula = y ~ x, size = 1,linetype="dashed",  col="black",se=FALSE, fullrange = TRUE)+
  geom_smooth(se=FALSE)+
  labs(title = "Made-up data") + 
  scale_x_log10(breaks =  c(1e-6,1e-4,1e-2,1),
                labels = trans_format("log10", math_format(10^.x)),limits = c(1e-6,1))+
  geom_hline(yintercept=0,linetype="dashed",colour="red",size=0.6)

I would like to convert 1e-10 input to 10^-10 format and annotate it on the plot. As I indicated in the plot.

thanks in advance!

解决方案

Because geom_smooth() uses R functions to calculate the smooth line, you can attain the predicted values outside the ggplot() environment. One option is then to use approx() to get a linear approximations of the x-value, given the predicted y-value 0.

# Define formula
formula <- loess(y~x, df)

# Approximate when y would be 0
xval <- approx(x = formula$fitted, y = formula$x, xout = 0)$y

# Add to plot
ggplot(...) + annotate("text", x = xval, y = 0 , label = yval)

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