在“曲线”内使用用户定义的函数功能在R图形 [英] Using user-defined functions within "curve" function in R graphics

问题描述

我需要生成具有不同总面积的正态分布密度图（总计为1）。使用下面的函数，我可以指定lambda - 它给出了相对区域：

$ pre $ s $ cdn sdnorm < - function（x，mean = 0，sd = 1，lambda = 1）{lambda * dnorm（x，mean = mean，sd = sd）}

然后我想用不同的参数来绘制函数。使用ggplot2，这段代码有效：

  require（ggplot2）
 qplot（x，geom =blank）+ stat_function（fun = sdnorm，args = list（mean = 18，sd = 4，lambda = 0.30）+ 
 stat_function（fun = sdnorm，args = list（mean = 8，sd = 2，lambda = 0.7） ）
  

但我真的想在基本的R图形中做到这一点，我认为我需要使用曲线功能。但是，我很努力地让这个工作。

解决方案

如果你看看<$ c $的帮助文件C>？曲线，你会看到第一个参数可以是许多不同的东西：

一个函数，或一个调用或一个表达式，它被写为x的一个函数，它将评估一个与x长度相同的对象。

这意味着你可以指定第一个参数作为函数名或表达式，所以你可以这样做：

  curve（ sdnorm）
  

来获得该函数的默认参数图。否则，重新创建你想要做的ggplot2表示：

  curve（sdnorm（x，mean = 8，sd = 2 
曲线（sdnorm（x，mean = 18，sd = 4，lambda = 0.30），add = TRUE）
  
 
 
 
结果： 
 
 
  
 
I am needing to produce normally distributed density plots with different total areas (summing to 1). Using the following function, I can specify the lambda - which gives the relative area:
sdnorm <-  function(x, mean=0, sd=1, lambda=1){lambda*dnorm(x, mean=mean, sd=sd)}
I then want to plot up the function using different parameters. Using ggplot2, this code works:
require(ggplot2)
qplot(x, geom="blank") + stat_function(fun=sdnorm,args=list(mean=8,sd=2,lambda=0.7)) +
  stat_function(fun=sdnorm,args=list(mean=18,sd=4,lambda=0.30))


but I really want to do this in base R graphics, for which I think I need to use the "curve" function. However, I am struggling to get this to work.
解决方案
If you take a look at the help file for ? curve, you'll see that the first argument can be a number of different things:

  
The name of a function, or a call or an expression written as a function of x which will evaluate to an object of the same length as x.
This means you can specify the first argument as either a function name or an expression, so you could just do:
curve(sdnorm)
to get a plot of the function with its default arguments. Otherwise, to recreate your ggplot2 representation you would want to do:
curve(sdnorm(x, mean=8,sd=2,lambda=0.7), from = 0, to = 30)
curve(sdnorm(x, mean=18,sd=4,lambda=0.30), add = TRUE)
The result:



                        
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