Hello
I am trying to find an automated way of fitting a mixture of normal and
log-normal distributions to data which is clearly bimodal.
Here's a simulated example:
x.1<-rnorm(6000, 2.4, 0.6)x.2<-rlnorm(10000, 1.3,0.1)X<-c(x.1, x.2)
hist(X,100,freq=FALSE, ylim=c(0,1.5))lines(density(x.1), lty=2,
lwd=2)lines(density(x.2), lty=2, lwd=2)lines(density(X), lty=4)
Currently i am using mixtools and just run:
library(mixtools)mixmdl = normalmixEM(X, k=2, epsilon = 1e-08, maxit = 1000,
maxrestarts=20, verb = TRUE, fast=FALSE, ECM = FALSE, arbmean = TRUE, arbvar =
TRUE) plot(mixmdl,which=2)lines(density(X), lty=2, lwd=2)
This is obviously not the best way of doing this. The estimates it gives me
are:mu 3.6595737(x.2 log()=1.29) 2.3113135(x.1)
These are not too far off but I was wondering if someone knows of a better R
package/way of doing this or has any hints that would help me coding it from
scratch (EM+writing down the formulae for ML? but... would this really be
better than using a "more-advanced-already-available-package-made-by-pros"?).
The objective is to estimate threshold values at specific FDRs. (some help with
that would also be most helpful.)
Thanks to all in advance!To'
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