# Flexmix Driver for Mixtures of von Mises-Fisher Distributions This driver for `flexmix` implements estimation of mixtures of von Mises-Fisher distributions where the data can be stored in a dense or a simple triplet matrix (package `slam`) format. ```r FLXMCvMF(formula = . ~ ., kappa = NULL) ``` ## Arguments - `formula`: a formula which is interpreted relative to the formula specified in the call to `flexmix` using `update.formula`. Only the left-hand side (response) of the formula is used. Default is to use the original `flexmix` model formula. - `kappa`: see the `control` argument of `movMF` . ## Returns An object of class `"FLXMCvMF"`. ## Author(s) Bettina Grün ## Examples ```r if (requireNamespace("flexmix", quietly = TRUE)) { ## 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. set.seed(123) y2 <- flexmix::stepFlexmix(x ~ 1, k = 2, model = FLXMCvMF(), verbose = FALSE) ## Inspect the fitted parameters: y2 ## Compare the fitted classes to the true ones: table(True = attr(x, "z"), Fitted = flexmix::clusters(y2)) ## To use a common kappa: y2cv <- flexmix::stepFlexmix(x ~ 1, k = 2, model = FLXMCvMF(kappa = list(common = TRUE)), verbose = FALSE) ## To use a common kappa fixed to the true value of 4: y2cf <- flexmix::stepFlexmix(x ~ 1, k = 2, model = FLXMCvMF(kappa = 4), verbose = FALSE) ## Comparing solutions via BIC: sapply(list(y2, y2cf, y2cv), BIC) ## Use a different kappa solver: set.seed(123) y2a <- flexmix::stepFlexmix(x ~ 1, k = 2, model = FLXMCvMF(kappa = "uniroot"), verbose = FALSE) y2a ## Using a sparse matrix: x <- slam::as.simple_triplet_matrix(x) y2 <- flexmix::stepFlexmix(x ~ 1, k = 2, model = FLXMCvMF(), verbose = FALSE) } ```

FLXMCvMF function