[R] Fitting a Mixture of Noncentral Student t Distributions to a one-dimensional sample
jzmoser at gmail.com
Wed Apr 30 22:30:34 CEST 2014
Dear R community,
I`d like to extract the parameters of a two-component mixture
distribution of noncentral student t distributions which was fitted to a
There are many packages for R that are capable of handling mixture
distributions in one way or another. Some in the context of a Bayesian
framework requiring kernels. Some in a regression framework. Some in a
nonparametric framework. ...
So far the "mixdist"-package seems to come closest to my wish. This
package fits parametric mixtures to a sample of data. Unfortunately it
doesn`t support the student t distribution.
I have also tried to manually set up a likelihood function as described
But the result is far from perfect.
The "gamlss.mx"-package might be helping, but originally it seems to be
set up for another context, i.e. regression. I tried to regress my data
on a constant and then extract the parameters for the estimated mixture
error distribution. But the estimated parameters seem to be not directly
accessable individually by some command (such as fit1$sigma). And there
seem to be serious convergence problems even in pretty simple and
nonambiguous cases (see example 2). The following syntax is my
gamlss.mx-setup so far:
fit1 <- gamlssMX(waiting~1,data=geyser,family="TF",K=2)
# works fine
N <- 100000
components <- sample(1:2,prob=c(0.6,0.4),size=N,replace=TRUE)
mus <- c(3,-6)
sds <- c(1,9)
nus <- c(25,3)
colnames(mixsim) <- "MCsim"
plot(density(mixsim$MCsim) , xlim=c(-50,50))
fit2 <- gamlssMX(MCsim~1,data=mixsim,family="TF",K=2)
# no convergence
With another dataset and when using the same two component densities for
the mixture as above I ended up with negative estimates for sigma (which
should be positive).
I would be very grateful for any advice. I`ve read through many manuals
and vignettes today but it seems that I am nearly in the same place
where I was this morning.
A small example for a setup that works sort of reliably would be fantastic!
Thanks a lot in advance!!
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