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]] ______________________________________________ [hidden email] 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. |
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 <[hidden email]> 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]] > > ______________________________________________ > [hidden email] 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. ______________________________________________ [hidden email] 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. |
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:[hidden email]] Sent: 14 February 2017 17:49 To: Peter Mills Cc: [hidden email] 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 <[hidden email]> 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]] > > ______________________________________________ > [hidden email] 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. [hidden email] 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. |
If you believe that a fit to simulated data should give you parameter
estimates identical to those used for the simulation, then I suggest that you stop posting and consult a local statistical expert: you don't know what you're doing. If I have misunderstood your post, then just ignore the above and hopefully someone with greater understanding than I will be able to help you. 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 28, 2017 at 7:39 AM, Peter Mills <[hidden email]> wrote: > 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:[hidden email]] > Sent: 14 February 2017 17:49 > To: Peter Mills > Cc: [hidden email] > 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 <[hidden email]> 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]] >> >> ______________________________________________ >> [hidden email] 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. > ______________________________________________ > [hidden email] 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. ______________________________________________ [hidden email] 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. |
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