将双峰/双高斯分布拟合到 R 中的数据集

Question

我有一些数据集，看起来像是由形成双峰图的两个正态分布的叠加组成的。 我想估计这些数据集分布的最佳拟合参数。 通常我会使用 fitdistrplus 包，但我找不到一个分布函数来馈送它的算法。

有人可以指出我的方向或建议我自己如何做吗？

Answer 1

对这个问题的回答看起来像是你的查询。我在这里重复他们的代码：

library(mixdist)  
#Build data vector "x" as a mixture of data from 3 Normal Distributions  
x1 <- rnorm(1000, mean=0, sd=2.0)  
x2 <- rnorm(500, mean=9, sd=1.5)  
x3 <- rnorm(300, mean=13, sd=1.0)  
x <- c(x1, x2, x3)  
#Plot a histogram (you'll play around with the value for "breaks" as    
#you zero-in on the fit).   Then build a data frame that has the  
#bucket midpoints and counts.  
breaks <- 30  
his <- hist(x, breaks=breaks)  
df <- data.frame(mid=his$mids, cou=his$counts)  
head(df)  
#The above Histogram shows 3 peaks that might be represented by 3 Normal  
#Distributions.  Guess at the 3 Means in Ascending Order, with a guess for  
#the associated 3 Sigmas and fit the distribution.  
guemea <- c(3, 11, 14)  
guesig <- c(1, 1, 1)  
guedis <- "norm"  
(fitpro <- mix(as.mixdata(df), mixparam(mu=guemea, sigma=guesig), dist=guedis))  
#Plot the results  
plot(fitpro, main="Fit a Probability Distribution")  
grid()  
legend("topright", lty=1, lwd=c(1, 1, 2), c("Original Distribution to be Fit", "Individual Fitted Distributions", "Fitted Distributions Combined"), col=c("blue", "red", rgb(0.2, 0.7, 0.2)), bg="white")  
===========================  

Parameters:  
      pi     mu  sigma  
1 0.5533 -0.565 1.9671  
2 0.2907  8.570 1.6169  
3 0.1561 12.725 0.9987  
Distribution:  
[1] "norm"  
Constraints:  
   conpi    conmu consigma   
  "NONE"   "NONE"   "NONE"