ggplot2 stat_function带有计算的参数，用于facet_grid中的不同数据子集 [英] ggplot2 stat_function with calculated argument for different data subset inside a facet_grid

问题描述

如何将 fitdistr 计算出的参数传递给 stat_function （参见此处）。

我的数据框就是这样的（见下面的完整数据集）：

 > str（small_data）
'data.frame'：1032 obs。 3个变量：
 $实验：具有6个等级的因子1L，2L，3L，..：1 1 1 1 1 1 1 1 1 1 ... 
 $ t ：num 0 0 0 0 0 0 0 0 0 0 ... 
 $ int：num 75.7 86.1 76.3 82.3 98.3 ... 
  

我想绘制一个由 Exp 和 t 分组的facet_grid，密度直方图 int ，并绘制其上拟合的对数正态分布（对数正态线用t表示）。我已经尝试了以下内容：

  library（MASS）
 
 meanlog<  -  function（x） {fitdistr（x，lognormal）$ estimate [[1]]} 
 sdlog < -  function（x）{fitdistr（x，lognormal）$ estimate [[2]]} 
 p_chip <-ggplot（small_data，（aes（x = int）））+ 
 facet_grid（Exp_t）+ 
 stat_function（fun = dlnorm，
 args = with（small_data，
c（meanlog = meanlog（int），
 sdlog = sdlog（int））），
 aes（color = t））+ 
 scale_colour_gradient2（low ='red'，mid =''蓝色'，高='绿色'，中点= 5）+ 
 geom_histogram（aes（x = int，y = ..density ..），binwidth = 150）
  

，但与， meanlog 和 sdlog 使用整个数据集计算meanlog和sdlog，如下所示（曲线在所有方面都相同）。我怎么能让它只在右边 Exp ， t 子集？

编辑：
因为大型数据集以某种方式在某些环境中复制/粘贴时产生了错误，因此这是一个较小的集合，应该更容易复制粘贴。然而，它并不直接对应于上面的图像

  small_data< -data.frame（Exp = c（'1L'，' 1L， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L' '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， 1L， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L' '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， '1L'， 1L， '1L'， '1L'， '1L'， '1L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L' ， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， 2L， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L' ， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'， '2L'） ，T = C（0,0,0,0.33,0.33,0.33,0.67,0.67,0.67,0.67,0.67,0.67,0.67,0.67,0.67,1,1,1,1,1.33,1.33,1.33,1.33 ，1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.67,1.67,1.67,1.67,1.67,2,2,2,2,4,4,4,4,6,6,6,6,8 ，8,10,10,10,10,10,10,10,0,0,0,0,0.33,0.33,0.67,0.67,0.67,0.67,0.67,0.67,1,1,1,1,1.33 ，1.33,1.33,1.33,1.67,1.67,1.67,1.67,1.67,2,2,4,4,4,4,4,6,6,6,8,10,10,10,10,10,10 ），INT = C（123.059145129225,122.520943007553,119.229495472186,163.349124924562,157.235229958189,101.456442831216,111.474216664325,99.982866933181,274.938909090909,147.40293040293,310.134596211366,116.476923076923,182.25272382757,332.75885911841,186.54737080689,479.628657282935,477.898496240602,283.311517925248,567.147534189805,494.208102667338,388.615060940221,624.508012820513， 795.2320925868,549.957142857143,923.04146100691,621.26579261025,717.577954847278,511.907210538479,443.562731447193,391.730061349693,495.384824667473,430.430866037423,157.39336711193,621.531297709924,415.420508401551,440.780570409982,414.551266085513,446.503836734694,255.0596 85999741,355.922701246211,308.996825396825,200.726012503398,297.958043579045,166.873177083333,184.450355103746,558.391405073555,182.63632183908,320.197666318356,151.874083846379,314.008287813147,125.941419000172,151.284729448491,605.400970873786,143.730810479547,240.779288537549,139.011736015851,498.179183673469,498.899700037495,923.604765506808,1302.60915123996,471.794167269222,239.522509225092,534.769484464503， 566.458609271523,337.121275121275,343.216533124878,250.47206095791,585.740563784042,873.775097783572,758.63260265514,561.869607843137,817.746869756034,461.11271165024,406.232050773503,897.39966367713,756.734451942367,605.242334066503,637.310763256886,721.862398822664,898.142725315288,670.916794425087,922.623940368313,1088.8436714166,969.805583375062,986.695448585877,645.589644637402,981.861218195836,541.388875932836， 1309.12344123945,925.446478133674,629.419699499165,1589.24284959626,814.736442884637,904.710338680927,947.911413969336,1481.51339495535,1007.30852694893,563.3552411 71884））
。 
  

解决方案

stat_function（...） - 请参阅此链接 a>，特别是Hadley Wickham的评论。

你必须这么做，也就是说，计算 ggplot外部的函数值。幸运的是，这并不难。

  library（MASS）
 library（ggplot2）
 df <  - 具有（fitdistr（z，对数正态），c（估计[1]，估计[2]））的集合（int_Exp + t，small_data，
函数（z）））
 （small_data，seq（min（int），max（int），len = 100））（其中df <-data.frame（df [，1：2]，df [，3]）） （gg，dlnorm（x，meanlog，sdlog））的数据块（b = ）
 ggplot（small_data，（aes（x = int）））+ 
 geom_histogram（aes（x = int，y = ..density ..），binwidth = 150，
 color = grey50，fill =lightgreen）+ 
 geom_line（data = gg，aes（x，y，color = t））+ 
 facet_grid（Exp〜t）+ 
 scale_colour_gradient2低='红'，mid ='蓝'，高='绿'，中点= 5）
  

所以这段代码创建了一个数据帧 df 包含 meanlog 和 sdlog Exp 和 t 。然后，我们创建一个辅助数据框架， gg ，它有一组x值覆盖您在 int 步骤100，然后复制 Exp 和 t 的每个组合，然后添加一列y-值使用 dlnorm（x，meanlog，sdlog）。然后，我们使用 gg 作为数据集添加一个geom_line图层。

请注意 fitdistr（...）并不总是收敛，因此您应该检查 df NA s c $ c>。

I have a follow up question to how to pass fitdistr calculated args to stat_function (see here for context).

My data frame is like that (see below for full data set):

> str(small_data)
 'data.frame':  1032 obs. of  3 variables:
 $ Exp: Factor w/ 6 levels "1L","2L","3L",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ t  : num  0 0 0 0 0 0 0 0 0 0 ...
 $ int: num  75.7 86.1 76.3 82.3 98.3 ...

I would like to plot a facet_grid grouped by Exp and t showing the density histogram of int as well as plot the fitted log-normal distribution on it (lognormal line colored by t). I have tried the following:

library(MASS)

meanlog <- function(x) { fitdistr(x,"lognormal")$estimate[[1]] }
sdlog <- function(x) { fitdistr(x,"lognormal")$estimate[[2]] }
p_chip<- ggplot(small_data,(aes(x=int)))+
  facet_grid(Exp~t)+
  stat_function(fun=dlnorm,
                args = with(small_data,
                  c(meanlog = meanlog(int),
                    sdlog = sdlog(int))),
                aes(colour=t))+
  scale_colour_gradient2(low='red',mid='blue',high='green',midpoint=5)+
  geom_histogram(aes(x=int,y = ..density..),binwidth =150)

but with, meanlog and sdlog use the whole dataset to compute meanlog and sdlog as shown below (the curve is the same on all facet). How can I have it do the fitting only on the right Exp,t subset?

Edit: Because somehow the large data set created errors in copy/paste on some environment, here is a smaller set which should be easier to copy paste. However it does not directly correspond to the image above

small_data<-data.frame(Exp=c('1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','1L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L','2L'),t=c(0,0,0,0.33,0.33,0.33,0.67,0.67,0.67,0.67,0.67,0.67,0.67,0.67,0.67,1,1,1,1,1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.33,1.67,1.67,1.67,1.67,1.67,2,2,2,2,4,4,4,4,6,6,6,6,8,8,10,10,10,10,10,10,10,0,0,0,0,0.33,0.33,0.67,0.67,0.67,0.67,0.67,0.67,1,1,1,1,1.33,1.33,1.33,1.33,1.67,1.67,1.67,1.67,1.67,2,2,4,4,4,4,4,6,6,6,8,10,10,10,10,10,10),int=c(123.059145129225,122.520943007553,119.229495472186,163.349124924562,157.235229958189,101.456442831216,111.474216664325,99.982866933181,274.938909090909,147.40293040293,310.134596211366,116.476923076923,182.25272382757,332.75885911841,186.54737080689,479.628657282935,477.898496240602,283.311517925248,567.147534189805,494.208102667338,388.615060940221,624.508012820513,795.2320925868,549.957142857143,923.04146100691,621.26579261025,717.577954847278,511.907210538479,443.562731447193,391.730061349693,495.384824667473,430.430866037423,157.39336711193,621.531297709924,415.420508401551,440.780570409982,414.551266085513,446.503836734694,255.059685999741,355.922701246211,308.996825396825,200.726012503398,297.958043579045,166.873177083333,184.450355103746,558.391405073555,182.63632183908,320.197666318356,151.874083846379,314.008287813147,125.941419000172,151.284729448491,605.400970873786,143.730810479547,240.779288537549,139.011736015851,498.179183673469,498.899700037495,923.604765506808,1302.60915123996,471.794167269222,239.522509225092,534.769484464503,566.458609271523,337.121275121275,343.216533124878,250.47206095791,585.740563784042,873.775097783572,758.63260265514,561.869607843137,817.746869756034,461.11271165024,406.232050773503,897.39966367713,756.734451942367,605.242334066503,637.310763256886,721.862398822664,898.142725315288,670.916794425087,922.623940368313,1088.8436714166,969.805583375062,986.695448585877,645.589644637402,981.861218195836,541.388875932836,1309.12344123945,925.446478133674,629.419699499165,1589.24284959626,814.736442884637,904.710338680927,947.911413969336,1481.51339495535,1007.30852694893,563.355241171884))
.

解决方案

This is not possible using stat_function(...) - see this link, especially Hadley Wickham's comments.

You have to do it the hard way, which is to say, calculating the function values external to ggplot. Fortunately, this is not all that difficult.

library(MASS)
library(ggplot2)
df <- aggregate(int~Exp+t,small_data,
                function(z)with(fitdistr(z,"lognormal"),c(estimate[1],estimate[2])))
df <- data.frame(df[,1:2],df[,3])
x  <- with(small_data,seq(min(int),max(int),len=100))
gg <- data.frame(x=rep(x,each=nrow(df)),df)
gg$y <- with(gg,dlnorm(x,meanlog,sdlog))
ggplot(small_data,(aes(x=int)))+
  geom_histogram(aes(x=int,y = ..density..),binwidth =150,
                 color="grey50",fill="lightgreen")+
  geom_line(data=gg, aes(x,y,color=t))+
  facet_grid(Exp~t)+
  scale_colour_gradient2(low='red',mid='blue',high='green',midpoint=5)

So this code creates a data frame df containing meanlog and sdlog for every combination of Exp and t. Then we create an "auxillary data frame", gg, which has a set of x-values covering your range in int with 100 steps, and replicate that for every combination of Exp and t, and we add a column of y-values using dlnorm(x,meanlog,sdlog). Then we add a geom_line layer to the plot using gg as the dataset.

Note that fitdistr(...) does not always converge, so you should check for NAs in df.

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