提取stat_smooth线拟合的方法 [英] Method to extract stat_smooth line fit

问题描述

有没有办法提取从stat_smooth返回的拟合行的值？

我使用的代码如下所示：

  p < -  ggplot（df1，aes（x = Days，y = Qty，group = Category，color = Category））
p< -  p + stat_smooth（method = glm，fullrange = TRUE）+ geom_point（））
  

用户将不胜感激任何指导。

.htmlrel =noreferrer> stat_smooth 确实会产生您可以在其他地方使用的输出，并且可以通过一个稍微黑客的方式将其放入一个变量在全球环境中。

将输出变量放在 .. 两边以使用它。因此，如果您在 stat_smooth 调用中添加 aes 并使用全局分配，< < - ，将输出分配给全球环境中的变量，您可以获得拟合值或其他值 - 请参阅下面的内容。

 $ fit 
 [1 ] 1.993594 2.039986 2.087067 2.134889 2.183533 2.232867 2.282897 2.333626 
 [9] 2.385059 2.437200 2.490053 2.543622 2.597911 2.652852 2.708104 2.764156 
 [17] 2.821771 2.888224 2.968745 3.049545 3.115893 3.156368 3.175495 3.181411 
 [25] 3.182252 3.186155 3.201258 3.235698 3.291766 3.353259 3.418409 3.487074 
 [33] 3.559111 3.634377 3.712729 3.813399 3.910849 3.977051 4.037302 4.091635 
 [41] 4.140082 4.182676 4.219447 4.250429 4.275654 4.295154 4.308961 4.317108 
 [49] 4.319626 4.316548 4.308435 4.302276 4.297902 4.292303 4.282505 4.269040 
 [57] 4.253361 4.235474 4.215385 4.193098 4.168621 4.141957 4.113114 4.08209 6 
 [65] 4.048910 4.013560 3.976052 3.936392 3.894586 3.850639 3.804557 3.756345 
 [73] 3.706009 3.653554 3.598987 3.542313 3.483536 3.422664 3.359701 3.294654 
  

您可以获得的输出是：

• y ，预测值
  
• ymin ，
  平均值下方的点位置置信区间
• ymax ，平均值周围的上点点置信区间
  
• se ，标准错误
  
  
  

  请注意，默认情况下它会预测80个数据点，这可能与您的原始数据不一致。

p>


Is there a way to extract the values of the fitted line returned from stat_smooth?

The code I am using looks like this:

p <- ggplot(df1, aes(x=Days, y= Qty,group=Category,color=Category))
p <- p + stat_smooth(method=glm, fullrange=TRUE)+ geom_point())

This new r user would greatly appreciate any guidance.

解决方案

stat_smooth does produce output that you can use elsewhere, and with a slightly hacky way, you can put it into a variable in the global environment.

You enclose the output variable in .. on either side to use it. So if you add an aes in the stat_smooth call and use the global assign, <<-, to assign the output to a varible in the global environment you can get the the fitted values, or others - see below.

qplot(hp,wt,data=mtcars) + stat_smooth(aes(outfit=fit<<-..y..))
fit
 [1] 1.993594 2.039986 2.087067 2.134889 2.183533 2.232867 2.282897 2.333626
 [9] 2.385059 2.437200 2.490053 2.543622 2.597911 2.652852 2.708104 2.764156
[17] 2.821771 2.888224 2.968745 3.049545 3.115893 3.156368 3.175495 3.181411
[25] 3.182252 3.186155 3.201258 3.235698 3.291766 3.353259 3.418409 3.487074
[33] 3.559111 3.634377 3.712729 3.813399 3.910849 3.977051 4.037302 4.091635
[41] 4.140082 4.182676 4.219447 4.250429 4.275654 4.295154 4.308961 4.317108
[49] 4.319626 4.316548 4.308435 4.302276 4.297902 4.292303 4.282505 4.269040
[57] 4.253361 4.235474 4.215385 4.193098 4.168621 4.141957 4.113114 4.082096
[65] 4.048910 4.013560 3.976052 3.936392 3.894586 3.850639 3.804557 3.756345
[73] 3.706009 3.653554 3.598987 3.542313 3.483536 3.422664 3.359701 3.294654

The outputs you can obtain are:

• y, predicted value
• ymin, lower pointwise confidence interval around the mean
• ymax, upper pointwise confidence interval around the mean
• se, standard error

Note that by default it predicts on 80 data points, which may not be aligned with your original data.

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