Ravi Varadhan wrote:
It is evident that you do not have enough information in the data to
estimate 9 mixture components. This is clearly indicated by a positive
semi-definite information matrix, S, that is less than full rank. You can
monitor the rank of the information matrix, as you
What you say about mixture models is true in general, however this fit was
the best of 100 random EM starts. Unbounded likelihoods I believe are only
a problem for continuous data mixture models and mine was discrete. Anyway
it's nearly midnight now here so I'd better sleep. Before I go, here are
I thought I would have another try at explaining my problem. I think that
last time I may have buried it in irrelevant detail.
This output should explain my dilemma:
dim(S)
[1] 1455 269
summary(as.vector(S))
Min.1st Qu. Median Mean3rd Qu. Max.
-1.160e+04
I tried crossprod(S) but the results were identical. The term
-0.5*log(det(S)) is a complexity penalty meant to make it unattractive to
include too many components in a finite mixture model. This case was for a
9-component mixture. At least up to 6 components the determinant behaved
as expected
Consider the following:
A - 1:10
A
[1] 1 2 3 4 5 6 7 8 9 10
dim(A)
NULL
dim(A) - c(2,5)
A
[,1] [,2] [,3] [,4] [,5]
[1,]13579
[2,]2468 10
dim(A)
[1] 2 5
dim(A) - 10
A
[1] 1 2 3 4 5 6 7 8 9 10
dim(A)
[1] 10
Would it not make
I apologise for not including a reproducible example with this query but I
hope that I can make things clear without one.
I am fitting some finite mixture models to data. Each mixture component
has p parameters (p=29 in my application) and there are q components to
the mixture. The number of data
Thanks to James and Phil and Peter for their helpful suggestions. I think
that I should also point out one way *not* to do the job:
xtabs(Count ~ Education + Age_Group, data=educ)
Age_Group
Education64 25-34 35-44 45-54 55-64
CompletedHS 7558 16431 1855 9435 8795
A small example before I begin my query:
educ - read.table(efile, header=TRUE)
educ
Education Age_Group Count
1 IncompleteHS 25-34 5416
2 IncompleteHS 35-44 5030
3 IncompleteHS 45-54 5777
4 IncompleteHS 55-64 7606
5 IncompleteHS 64 13746
6 CompletedHS
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