指数拟合ggplot R [英] exponential fit in ggplot R

问题描述

我一直试图使用ggplot和geom_smooth将指数曲线拟合到我的数据。我试图复制类似问题的答案（ geom_smooth和指数拟合）
，但不断收到以下错误消息：

 > （x，y，offset = offset，singular.ok = singular.ok，...）：
 NA / NaN / Inf in'x'
  

我不明白错误，因为那里在数据集中不是NA / NaN / Inf值：

 > df 
xy 
 1 1981 3.262897 
 2 1990 2.570096 
 3 2000 7.098903 
 4 2001 5.428424 
 5 2002 6.056302 
 6 2003 5.593942 
 7 2004 10.869635 
 8 2005 12.425793 
 9 2006 5.601889 
 10 2007 6.498187 
 2008 2008 6.967503 
 12 2009 5.358961 
 13 2010 3.519295 
 14 2011 7.137202 
 15 2012 19.121631 
 16 2013 6.479928 
  

解决方案

设置数据： （x = c（1981,1990,2000：2013），
y = c（x，c） 3.262897,2.570096,7.098903,5.428424,6.056302,5.593942，
10.869635,12.425793,5.601889,6.498187,6.967503,5.358961,3.519295，
7.137202,19.121 631,6.479928））

问题是，大于约709的任何数的幂给出的数大于最大值可作为双精度浮点值存储（约。 1e308），并因此导致数字溢出。您可以通过移动您的x变量来轻松地进行补救：

  lm（y〜exp（x），data = dd）##错误
 lm（y〜exp（x-1981），data = dd）## fine 
  

<但是，您可以更容易地绘制该模型的拟合值，如下所示：

  library（ggplot2）; theme_set（theme_bw（））
 ggplot（dd，aes（x，y））+ geom_point（）+ 
 geom_smooth（method =glm，
 method.args = list（family =高斯（link =log）））
  

I've been trying to fit an exponential curve to my data using ggplot and geom_smooth. I'm trying to replicate the answer to a similar problem (geom_smooth and exponential fits) but keep getting following error message:

> exp.model <-lm(y ~ exp(x), df)
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  NA/NaN/Inf in 'x'

I don't understand the error, as there is not NA/NaN/Inf values in the dataset:

>df
      x         y
1  1981  3.262897
2  1990  2.570096
3  2000  7.098903
4  2001  5.428424
5  2002  6.056302
6  2003  5.593942
7  2004 10.869635
8  2005 12.425793
9  2006  5.601889
10 2007  6.498187
11 2008  6.967503
12 2009  5.358961
13 2010  3.519295
14 2011  7.137202
15 2012 19.121631
16 2013  6.479928

解决方案

Set up data:

dd <- data.frame(x=c(1981,1990,2000:2013),
  y = c(3.262897,2.570096,7.098903,5.428424,6.056302,5.593942,
  10.869635,12.425793,5.601889,6.498187,6.967503,5.358961,3.519295,
  7.137202,19.121631,6.479928))

The problem is that exponentiating any number larger than about 709 gives a number greater than the maximum value storeable as a double-precision floating-point value (approx. 1e308), and hence leads to a numeric overflow. You can easily remedy this by shifting your x variable:

lm(y~exp(x),data=dd) ## error
lm(y~exp(x-1981),data=dd) ## fine

However, you can plot the fitted value for this model more easily as follows:

library(ggplot2); theme_set(theme_bw())
ggplot(dd,aes(x,y))+geom_point()+
   geom_smooth(method="glm",
            method.args=list(family=gaussian(link="log")))

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