Thank you Bert for the references. I have found mix.vmf as a possible
alternative to movMF but am stuck with applying either to my application.
Does anyone know why mix.vmf requires to 2 columns for x?
For example when I use:
gWD <- c(0.1, 1, 0.9, 0.7,0.3)> mix.vmf(gWD, 2)
I get the error:
?Error in matrix(nrow = n, ncol = g) : non-numeric matrix extent?
The following code works but I'm not sure how to adapt this to the
application I require. What I need is to calculate the mixture parameters for a
vector of directional data not a matrix. Could anyone suggest how I can do this?
k <- c(1, 2)
prob <- c(0.3, 0.4, 0.3)
mu <- matrix(rnorm(4), ncol = 2)
mu <- mu / sqrt( rowSums(mu^2) )
x <- rmixvmf(10, prob, mu, k)$x
mix.vmf(x, 3)
With regards to my original post and using movMF I'm stuck with this also. I
found the following from [1], I thought this was the answer however it seems to
need some adjustment as it is not giving me the theta and kappa values I started
with:
kappa2 <- row_norms(y2$theta)
mu2<-y2$theta/row_norms(y2$theta)
[1] On lines 94 and 107 of ?v58i10.R: R example code from the paper ? available
from https://www.jstatsoft.org/article/view/v058i10
Any suggestion would be greatly appreciated as after a further week of
researching this and trying code I still don't seem to have working code.
Here is some code I am using as a testing example:
################################################
## Generate and fit a "small-mix" data set a la Banerjee et al.
mu1 <- rbind(c(-0.251, -0.968),
c(0.399, 0.917))
kappa1 <- c(4, 4)
theta <- kappa1 * mu1
alpha <- c(0.48, 0.52)
## Generate a sample of size n = 50 from the von Mises-Fisher mixture ## with
the above parameters.
set.seed(123)
x <- rmovMF(50, theta, alpha)
## Fit a von Mises-Fisher mixture with the "right" number of
components, ## using 10 EM runs.
y2 <- movMF(x, 2, nruns = 10)
kappa2 <- row_norms(y2$theta)
mu2<-y2$theta/row_norms(y2$theta)
mu3c <- lapply(y2, function(x) y2$theta / row_norms(y2$theta))
################################################
I don't understand why this doesn't give me the values of mu and kappa I
started with. I have tried to adapted this code to have the same number of mu
and kappa values but it doesn't work then as mu is know longer a matrix.
I don't understand why both movMF and mix.vmf give both mu1 and mu2 values
per kappa value. In other words for a Von Mises mixture with 3 mixtures
(clusters) the code gives 6 mu values and 3 kappa values. I was expecting to get
3 preferred directions (mu).
For example:
mix.vmf(x, 3) gives
$param
mu1 mu2 kappa probs
Cluster 1 0.4985047 -0.8668870 12.281773 0.3571429
Cluster 2 0.9490725 0.3150578 34.028465 0.2857143
Cluster 3 0.1017800 0.9948069 4.182367 0.3571429
Many thanks
Peter
-----Original Message-----
From: Bert Gunter [mailto:bgunter.4567 at gmail.com]
Sent: 14 February 2017 17:49
To: Peter Mills
Cc: r-help at r-project.org
Subject: Re: [R] Von Mises mixtures: mu and kappa?
Please search before posting!
Searching "von mises mixture distributions" on rseek.org brought up
what appeared to be several relevant hits. If none of these meet your needs, you
should probably explain why not in a follow up post.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, Feb 14, 2017 at 8:55 AM, Peter Mills <peter.mills at strath.ac.uk>
wrote:> Hello
>
> I am trying to calculate the values of the concentration parameters (kappa)
and preferred direction (mu) for a Von Mises mixture model. I currently have
some R code that gives me optimised values for the product of kappa and mu, but
I'm not sure how to calculate them when both are unknown? How could I
calculate mu and kappa from y2 if I didn't know either in the 1st place? I
what to use movMF to give me values of kappa from some directional data where I
don't know either kappa or mu.
>
>
> ## Generate and fit a "small-mix" data set a la Banerjee et al.
> mu <- rbind(c(-0.251, -0.968),
> c(0.399, 0.917))
> kappa <- c(4, 4)
>
> theta <- kappa * mu
> theta
> alpha <- c(0.48, 0.52)
>
> ## Generate a sample of size n = 50 from the von Mises-Fisher mixture
> ## with the above parameters.
> set.seed(123)
> x <- rmovMF(50, theta, alpha)
> ## Fit a von Mises-Fisher mixture with the "right" number of
> components, ## using 10 EM runs.
> y2 <- movMF(x, 2, nruns = 10)
>
> Y2 gives
>> y2
> theta:
> [,1] [,2]
> 1 2.443225 5.259337
> 2 -1.851384 -4.291278
> alpha:
> [1] 0.4823648 0.5176352
> L:
> [1] 24.98124
>
> How could I calculate kappa and mu if I didn't know either in the 1st
place?
>
> Thanks
> Peter
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.