Plot Gaussian mixture in R with ggplot2

Mr. Zen

I'm approximating a distribution with a mixture of Gaussians, and was wondering if there was an easy way to automatically plot the estimated kernel density of the entire (1D) dataset as the sum of the component densities, similar to this way using ggplot2 :

Full data density plotted using individual component densities

Given the following sample data, my approach in ggplot2 would be to manually plot the subset densities into the scaled population densities as follows:

#example data
a<-rnorm(1000,0,1) #component 1
b<-rnorm(1000,5,2) #component 2
d<-c(a,b) #overall data 
df<-data.frame(d,id=rep(c(1,2),each=1000)) #add group id

##ggplot2
require(ggplot2)

ggplot(df) +
  geom_density(aes(x=d,y=..scaled..)) +
  geom_density(data=subset(df,id==1), aes(x=d), lty=2) +
  geom_density(data=subset(df,id==2), aes(x=d), lty=4)

ggplot2 figure

Note that this does not work for scale. It also doesn't work when you scale all 3 densities or none at all. Therefore, I cannot reproduce the above plot.

In addition, I am not able to automatically generate this plot without having to subset manually. I tried using position = "stacked" as parameter in geom_density.

I usually have around 5-6 Components per dataset, so manually subsetting would be possible. However, I would like to have different colors or line-types per component density which are displayed in the legend of ggplot, so doing all subsets manually would increase the workload quite a bit.

Any ideas? Thanks!

missuse

Here is a possible solution by specifying each density in the aes call with position = "identity" in one layer and in the second layer using stacked density without the legend.

ggplot(df) +
  stat_density(aes(x = d,  linetype = as.factor(id)), position = "stack", geom = "line", show.legend = F, color = "red") +
  stat_density(aes(x = d,  linetype = as.factor(id)), position = "identity", geom = "line")

enter image description here

Do note that when using more then two groups:

  a <- rnorm(1000, 0, 1) 
  b <- rnorm(1000, 5, 2) 
  c <- rnorm(1000, 3, 2)
  d <- rnorm(1000, -2, 1)
  d <- c(a, b, c, d)
  df <- data.frame(d, id = as.factor(rep(c(1, 2, 3, 4), each = 1000)))

Curves for each stack appear (this is a problem with both sets of examples, but it linetype's masked in the first layer - groupcheck instead):

 gplot(df) +
    stat_density(aes(x = d, group = id), position = "stack", geom = "line", show.legend = F, color = "red") +
    stat_density(aes(x = d, linetype = id), position = "identity", geom = "line")

enter image description here

A relatively simple workaround is to add an alpha map and then manually set the unwanted curves to 0:

  ggplot(df) +
    stat_density(aes(x=d, alpha = id), position = "stack", geom = "line", show.legend = F, color = "red") +
    stat_density(aes(x=d,  linetype = id), position = "identity", geom = "line")+
    scale_alpha_manual(values = c(1,0,0,0))

enter image description here

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