I have a sample of 1m records obtained from my original data. (For your reference, you may use this dummy data that may generate approximately similar distribution
b <- data.frame(matrix(rnorm(2000000, mean=c(8,17), sd=2)))
c <- b[sample(nrow(b), 1000000), ]
) I believed the histogram to be a mixture of two log-normal distributions and I tried to fit the summed distributions using EM algorithm using the following code:
install.packages("mixtools")
lib(mixtools)
#line below returns EM output of type mixEM[] for mixture of normal distributions
c1 <- normalmixEM(c, lambda=NULL, mu=NULL, sigma=NULL)
plot(c1, density=TRUE)
The first plot is a log-likelihood plot and the second (if you hit return again), gives similar to the following density curves:
As I mentioned c1 is of type mixEM[] and plot() function can accommodate that. I want to fill the density curves with colors. This is easy to do using ggplot2() but ggplot2() does not support data of type mixEM[] and throws this message:
"ggplot doesn't know how to deal with data of class mixEM" Is there any other approach I can take for this problem? Any suggestions are greatly appreciated!!
Thanks!
Look at the structure of the returned object (this should be documented in the help):
> # simple mixture of normals:
> x=c(rnorm(10000,8,2),rnorm(10000,17,4))
> xMix = normalmixEM(x, lambda=NULL, mu=NULL, sigma=NULL)
Now what:
> str(xMix)
List of 9
$ x : num [1:20000] 6.18 9.92 9.07 8.84 9.93 ...
$ lambda : num [1:2] 0.502 0.498
$ mu : num [1:2] 7.99 17.05
$ sigma : num [1:2] 2.03 4.02
$ loglik : num -59877
The lambda, mu, and sigma components define the returned normal densities. You can plot these in ggplot using qplot
and stat_function
. But first make a function that returns scaled normal densities:
sdnorm =
function(x, mean=0, sd=1, lambda=1){lambda*dnorm(x, mean=mean, sd=sd)}
Then:
qplot(x,geom="density") + stat_function(fun=sdnorm,args=list(mean=xMix$mu[1],sd=xMix$sigma[1], lambda=xMix$lambda[1]),fill="blue",geom="polygon") + stat_function(fun=sdnorm,args=list(mean=xMix$mu[2],sd=xMix$sigma[2], lambda=xMix$lambda[2]),fill="#FF0000",geom="polygon")
Or whatever ggplot
skills you have. Transparent colours on the densities might be nice.
ggplot(data.frame(x=x)) +
geom_histogram(aes(x=x,y=..density..),fill="white",color="black") +
stat_function(fun=sdnorm,
args=list(mean=xMix$mu[2],
sd=xMix$sigma[2],
lambda=xMix$lambda[2]),
fill="#FF000080",geom="polygon") +
stat_function(fun=sdnorm,
args=list(mean=xMix$mu[1],
sd=xMix$sigma[1],
lambda=xMix$lambda[1]),
fill="#00FF0080",geom="polygon")
producing:
Here's a slightly different approach which uses geom_ploygon(...)
instead of multiple calls to stat_function(...)
. One problem with stat_function(...)
is that the secondary arguments (mu, sigma, and lambda in this example), which are passed using the args=list(...)
parameter, cannot be included in an aesthetic mapping, so you have to have multiple calls to stat_function(...)
as is @Spacedman`s solution.
This approach builds the PDFs outside of ggplot and uses a single call to geom_polygon(...)
. As a result, it works without modification for an arbitrary number of distributions in the mixture.
# ggplot mixture plot
gg.mixEM <- function(EM) {
require(ggplot2)
x <- with(EM,seq(min(x),max(x),len=1000))
pars <- with(EM,data.frame(comp=colnames(posterior), mu, sigma,lambda))
em.df <- data.frame(x=rep(x,each=nrow(pars)),pars)
em.df$y <- with(em.df,lambda*dnorm(x,mean=mu,sd=sigma))
ggplot(data.frame(x=EM$x),aes(x,y=..density..)) +
geom_histogram(fill=NA,color="black")+
geom_polygon(data=em.df,aes(x,y,fill=comp),color="grey50", alpha=0.5)+
scale_fill_discrete("Component\nMeans",labels=format(em.df$mu,digits=3))+
theme_bw()
}
library(mixtools)
# two components
set.seed(1) # for reproducible example
b <- rnorm(2000000, mean=c(8,17), sd=2)
c <- b[sample(length(b), 1000000) ]
c2 <- normalmixEM(c, lambda=NULL, mu=NULL, sigma=NULL)
gg.mixEM(c2)
# three components
set.seed(1)
b <- rnorm(2000000, mean=c(8,17,30), sd=c(2,3,5))
c <- b[sample(length(b), 1000000) ]
library(mixtools)
c3 <- normalmixEM(c, k=3, lambda=NULL, mu=NULL, sigma=NULL)
gg.mixEM(c3)
来源:https://stackoverflow.com/questions/25313578/any-suggestions-for-how-i-can-plot-mixem-type-data-using-ggplot2