Package 'DirStats' reference manual

Title:	Nonparametric Methods for Directional Data
Description:	Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in García-Portugués (2013) <doi:10.1214/13-ejs821>.
Authors:	Eduardo García-Portugués [aut, cre]
Maintainer:	Eduardo García-Portugués <[email protected]>
License:	GPL-3
Version:	0.1.10
Built:	2024-11-11 07:13:55 UTC
Source:	CRAN

`DirStats` – Nonparametric Methods for Directional Data

Description

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in García-Portugués (2013) <doi:10.1214/13-ejs821>.

Author(s)

Eduardo García-Portugués.

Fitting mixtures of von Mises–Fisher distributions

Description

Fitting mixtures of von Mises–Fisher distributions by the Expectation-Maximization algorithm, with determination of the optimal number of mixture components.

Usage

bic_vmf_mix(data, M_bound = ceiling(log(nrow(data))), M_neig = 3,
  crit = "BIC", iterative = TRUE, plot_it = FALSE, verbose = FALSE,
  kappa_max = 250)
bic_vmf_mix(data, M_bound = ceiling(log(nrow(data))), M_neig = 3,
  crit = "BIC", iterative = TRUE, plot_it = FALSE, verbose = FALSE,
  kappa_max = 250)

Arguments

`data`	directional data, a matrix of size `c(n, q + 1)`.
`M_bound`	bound for the number of components in the mixtures. If it is not enough, the search for the mixture with minimum `crit` will continue from `M_bound + 1` if `iterative = TRUE`. Defaults to `ceiling(log(nrow(data)))`.
`M_neig`	number of neighbors explored around the optimal number of mixture components. Defaults to `3`.
`crit`	information criterion employed, either `"BIC"` (default), `"AICc"` or `"AIC"`.
`iterative`	keep exploring higher number of components if the optimum is attained at `M_bound`? Defaults to `TRUE`.
`plot_it`	display an informative plot on the optimization's grid search? Defaults to `FALSE`.
`verbose`	display fitting progress? Defaults to `FALSE`.
`kappa_max`	maximum value of allowed concentrations, to avoid numerical instabilities. Defaults to `250`.

Details

See Algorithm 3 in García-Portugués (2013). The Expectation-Maximization fit is performed with movMF.

Value

A list with entries:

best_fit: a list with estimated mixture parameters mu_hat, kappa_hat, and p_hat of the best-fitting mixture according to crit.
fit_mixs: a list with of the fitted mixtures.
BICs: a vector with the BICs (or other information criterion) of the fitted mixtures.

References

García-Portugués, E. (2013). Exact risk improvement of bandwidth selectors for kernel density estimation with directional data. Electronic Journal of Statistics, 7:1655–1685. doi:10.1214/13-ejs821

Hornik, K. and Grün, B. (2014). movMF: An R Package for Fitting Mixtures of von Mises–Fisher Distributions. Journal of Statistical Software, 58(10):1–31. doi:10.18637/jss.v058.i10

Examples

# Sample
q <- 2
n <- 300
set.seed(42)
samp <- rbind(rotasym::r_vMF(n = n / 3, mu = c(rep(0, q), 1), kappa = 5),
              rotasym::r_vMF(n = n / 3, mu = c(rep(0, q), -1), kappa = 5),
              rotasym::r_vMF(n = n / 3, mu = c(1, rep(0, q)), kappa = 5))

# Mixture fit
bic_vmf_mix(data = samp, plot_it = TRUE, verbose = TRUE)
# Sample
q <- 2
n <- 300
set.seed(42)
samp <- rbind(rotasym::r_vMF(n = n / 3, mu = c(rep(0, q), 1), kappa = 5),
              rotasym::r_vMF(n = n / 3, mu = c(rep(0, q), -1), kappa = 5),
              rotasym::r_vMF(n = n / 3, mu = c(1, rep(0, q)), kappa = 5))

# Mixture fit
bic_vmf_mix(data = samp, plot_it = TRUE, verbose = TRUE)

Cross-validation bandwidth selectors for directional data

Description

Likelihood and least squares cross-validation bandwidth selectors for kernel density estimation with directional data.

Usage

bw_dir_lcv(data, h_grid = exp(seq(log(0.05), log(1.5), l = 100)), L = NULL,
  plot_it = FALSE, optim = TRUE, optim_par = 0.25, optim_lower = 0.06,
  optim_upper = 10)

bw_dir_lscv(data, h_grid = exp(seq(log(0.05), log(1.5), l = 100)),
  L = NULL, plot_it = FALSE, optim = TRUE, R_code = FALSE,
  optim_par = 0.25, optim_lower = 0.06, optim_upper = 10)
bw_dir_lcv(data, h_grid = exp(seq(log(0.05), log(1.5), l = 100)), L = NULL,
  plot_it = FALSE, optim = TRUE, optim_par = 0.25, optim_lower = 0.06,
  optim_upper = 10)

bw_dir_lscv(data, h_grid = exp(seq(log(0.05), log(1.5), l = 100)),
  L = NULL, plot_it = FALSE, optim = TRUE, R_code = FALSE,
  optim_par = 0.25, optim_lower = 0.06, optim_upper = 10)

Arguments

`data`	directional data, a matrix of size `c(n, q + 1)`.
`h_grid`	vector of bandwidths for performing a grid search. Defaults to `exp(seq(log(0.05), log(1.5), l = 100))`.
`L`	kernel function. Set internally to `function(x) exp(-x)` (von Mises–Fisher kernel) if `NULL` (default).
`plot_it`	display an informative plot on the optimization's grid search? Defaults to `FALSE`.
`optim`	run an optimization? Defaults to `TRUE`. Otherwise, a grid search on `h` is done. Only effective if `L = NULL`.
`optim_par`, `optim_lower`, `optim_upper`	parameters passed to `par`, `lower`, and `upper` in `optim` when using the `"L-BFGS-B"` method. Default to `0.25`, `0.06` (to avoid numerical instabilities), and `10`.
`R_code`	use slower R code when `L = NULL`? Defaults to `FALSE`.

Details

data is not checked to have unit norm, so the user must be careful. When L = NULL, faster FORTRAN code is employed.

bw_dir_lscv employs Monte Carlo integration for $q > 2$ , which results in a random output. Use set.seed before to avoid it.

Value

A list with entries:

h_opt: selected bandwidth.
h_grid: h_grid, if used (otherwise NULL).
CV_opt: minimum of the CV loss.
CV_grid: value of the CV function at h_grid, if used (otherwise NULL).

Source

The function bw_dir_lscv employs Netlib's subroutine ribesl for evaluating the modified Bessel function of the first kind. The subroutine is based on a program by Sookne (1973) and was modified by W. J. Cody and L. Stoltz. An earlier version was published in Cody (1983).

References

Cody, W. J. (1983). Algorithm 597: Sequence of modified Bessel functions of the first kind. ACM Transactions on Mathematical Software, 9(2):242–245. doi:10.1145/357456.357462

Hall, P., Watson, G. S., and Cabrera, J. (1987). Kernel density estimation with spherical data. Biometrika, 74(4):751–762. doi:10.1093/biomet/74.4.751

Sookne, D. J. (1973). Bessel functions of real argument and integer order. Journal of Research of the National Bureau of Standards, 77B:125–132.

Examples

# Sample
n <- 25
q <- 2
set.seed(42)
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# bw_dir_lcv
bw_dir_lcv(data = samp, optim = TRUE)$h_opt
bw_dir_lcv(data = samp, optim = FALSE, plot_it = TRUE)$h_opt
bw_dir_lcv(data = samp, L = function(x) exp(-x))$h_opt

# bw_dir_lscv
set.seed(42)
bw_dir_lscv(data = samp, optim = TRUE)$h_opt
bw_dir_lscv(data = samp, optim = FALSE, plot_it = TRUE)$h_opt
bw_dir_lscv(data = samp, optim = FALSE, R_code = TRUE)$h_opt
bw_dir_lscv(data = samp, L = function(x) exp(-x))$h_opt
# Sample
n <- 25
q <- 2
set.seed(42)
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# bw_dir_lcv
bw_dir_lcv(data = samp, optim = TRUE)$h_opt
bw_dir_lcv(data = samp, optim = FALSE, plot_it = TRUE)$h_opt
bw_dir_lcv(data = samp, L = function(x) exp(-x))$h_opt

# bw_dir_lscv
set.seed(42)
bw_dir_lscv(data = samp, optim = TRUE)$h_opt
bw_dir_lscv(data = samp, optim = FALSE, plot_it = TRUE)$h_opt
bw_dir_lscv(data = samp, optim = FALSE, R_code = TRUE)$h_opt
bw_dir_lscv(data = samp, L = function(x) exp(-x))$h_opt

Plug-in bandwidth selectors for directional data

Description

Plug-in bandwidth selectors for kernel density estimation with directional data, including Rule-Of-Thumb (ROT), Asymptotic MIxtures (AMI), and Exact MIxtures (EMI).

Usage

bw_dir_rot(data)

bw_dir_ami(data, fit_mix = NULL, L = NULL)

R_Psi_mixvmf(q, mu, kappa, p)

bw_dir_emi(data, fit_mix = NULL, optim = TRUE,
  h_grid = exp(seq(log(0.05), log(1.5), l = 100)), plot_it = TRUE,
  optim_par = 0.25, optim_lower = 0.06, optim_upper = 10)
bw_dir_rot(data)

bw_dir_ami(data, fit_mix = NULL, L = NULL)

R_Psi_mixvmf(q, mu, kappa, p)

bw_dir_emi(data, fit_mix = NULL, optim = TRUE,
  h_grid = exp(seq(log(0.05), log(1.5), l = 100)), plot_it = TRUE,
  optim_par = 0.25, optim_lower = 0.06, optim_upper = 10)

Arguments

`data`	directional data, a matrix of size `c(n, q + 1)`.
`fit_mix`	output from `bic_vmf_mix`. Computed internally if `NULL` (default).
`L`	kernel function. Set internally to `function(x) exp(-x)` (von Mises–Fisher kernel) if `NULL` (default).
`q`	dimension of $S^q$ , $q\ge 1$ .
`mu`, `kappa`, `p`	mixture parameters. `mu` is the mean matrix of size `c(length(p), q + 1)`, `kappa` is vector of `length(p)` concentration parameters, and `p` is the vector of mixture proportions.
`optim`	run an optimization? Defaults to `TRUE`. Otherwise, a grid search on `h` is done. Only effective if `L = NULL`.
`h_grid`	vector of bandwidths for performing a grid search. Defaults to `exp(seq(log(0.05), log(1.5), l = 100))`.
`plot_it`	display an informative plot on the optimization's grid search? Defaults to `FALSE`.
`optim_par`, `optim_lower`, `optim_upper`	parameters passed to `par`, `lower`, and `upper` in `optim` when using the `"L-BFGS-B"` method. Default to `0.25`, `0.06` (to avoid numerical instabilities), and `10`.

Details

See Algorithms 1 (AMI) and 2 (EMI) in García-Portugués (2013). The ROT selector is implemented according to Proposition 2, but without the paper's typo in equation (6), case $q = 2$ , where an incorrect extra $\hat\kappa$ appears premultiplying $(1 + 4 \hat\kappa^2) \sinh(2 \hat\kappa)$ in the denominator.

bw_dir_ami uses R_Psi_mixvmf for computing the curvature term of a mixture of von Mises–Fisher densities.

bw_dir_emi employs Monte Carlo integration for $q > 2$ , which results in a random output. Use set.seed before to avoid it.

Value

Selected bandwidth for bw_dir_rot and bw_dir_ami. bw_dir_emi returns a list with entries:

h_opt: selected bandwidth.
h_grid: h_grid, if used (otherwise NULL).
MISE_opt: minimum of the MISE loss.
MISE_grid: value of the MISE function at h_grid, if used (otherwise NULL).

References

Examples

# Sample
n <- 25
q <- 2
set.seed(42)
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Mixture fit
fit_mix <- bic_vmf_mix(data = samp, plot_it = TRUE)

# ROT
bw_dir_rot(samp)

# AMI
bw_dir_ami(samp)
bw_dir_ami(samp, fit_mix = fit_mix)
bw_dir_ami(samp, fit_mix = fit_mix, L = function(x) exp(-x))

# EMI
bw_dir_emi(samp)
bw_dir_emi(samp, fit_mix = fit_mix, optim = FALSE, plot_it = TRUE)
# Sample
n <- 25
q <- 2
set.seed(42)
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Mixture fit
fit_mix <- bic_vmf_mix(data = samp, plot_it = TRUE)

# ROT
bw_dir_rot(samp)

# AMI
bw_dir_ami(samp)
bw_dir_ami(samp, fit_mix = fit_mix)
bw_dir_ami(samp, fit_mix = fit_mix, L = function(x) exp(-x))

# EMI
bw_dir_emi(samp)
bw_dir_emi(samp, fit_mix = fit_mix, optim = FALSE, plot_it = TRUE)

Convenience functions

Description

Normalization of data in $R^{q + 1}$ to $S^q$ . Transformations between $S^1$ and $[0, 2\pi)$ , and between $S^2$ and $[0, 2\pi) \times [0, \pi]$ .

Usage

norm2(x)

normalize(x)

to_cir(th)

to_rad(x)

to_sph(th, ph)
norm2(x)

normalize(x)

to_cir(th)

to_rad(x)

to_sph(th, ph)

Arguments

`x`	matrix or vector, in $S^1$ for `to_cir`.
`th`	vector of angles in $[0, 2\pi)$ .
`ph`	vector of angles in $[0, \pi]$ .

Value

Euclidean norm (norm) and normalized data (normalize). Position in $S^1$ (to_cir) or in $[0, 2\pi)$ (to_rad). Position in $S^2$ (to_sph) or in $[0, 2\pi) \times [0, \pi]$ (to_rad).

Examples

# Normalization
x <- 1:3
norm2(x)
normalize(x)
x <- rbind(1:3, 3:1)
norm2(x)
normalize(x)

# Circular transformations
th <- 1
x <- c(0, 1)
to_rad(to_cir(th))
to_rad(to_cir(c(th, th + 1)))
to_cir(to_rad(x))
to_cir(to_rad(rbind(x, -x)))

# Spherical transformations
th <- 2
ph <- 1
x <- c(0, 1, 0)
to_rad(to_sph(th, ph))
to_rad(to_sph(c(th, th + 1),
              c(ph, ph + 1)))
to_sph(to_rad(x)[, 1], to_rad(x)[, 2])
to_sph(to_rad(rbind(x, -x))[, 1], to_rad(rbind(x, -x))[, 2])
# Normalization
x <- 1:3
norm2(x)
normalize(x)
x <- rbind(1:3, 3:1)
norm2(x)
normalize(x)

# Circular transformations
th <- 1
x <- c(0, 1)
to_rad(to_cir(th))
to_rad(to_cir(c(th, th + 1)))
to_cir(to_rad(x))
to_cir(to_rad(rbind(x, -x)))

# Spherical transformations
th <- 2
ph <- 1
x <- c(0, 1, 0)
to_rad(to_sph(th, ph))
to_rad(to_sph(c(th, th + 1),
              c(ph, ph + 1)))
to_sph(to_rad(x)[, 1], to_rad(x)[, 2])
to_sph(to_rad(rbind(x, -x))[, 1], to_rad(rbind(x, -x))[, 2])

Integration routines

Description

Several quadrature rules for integration of functions on $S^1$ , $S^2$ , and $S^q$ , $q \ge 3$ .

Usage

int_cir(f, N = 500, na.rm = TRUE, f_vect = TRUE, ...)

int_sph(f, na.rm = TRUE, f_vect = TRUE, ...)

int_hypsph(f, q, M = 1e+05, na.rm = TRUE, f_vect = TRUE, ...)
int_cir(f, N = 500, na.rm = TRUE, f_vect = TRUE, ...)

int_sph(f, na.rm = TRUE, f_vect = TRUE, ...)

int_hypsph(f, q, M = 1e+05, na.rm = TRUE, f_vect = TRUE, ...)

Arguments

`f`	function to be integrated on $S^q$ . Must be vectorized and accept matrix inputs of size `c(nx, q + 1)`.
`N`	Defaults to `5e2`.
`na.rm`	ignore possible `NA`s arising from the evaluation of `f`? Defaults to `TRUE`.
`f_vect`	can `f` be called in a vectorized form, with matrix input? Defaults to `TRUE`.
`...`	further arguments passed to `f`.
`q`	dimension of $S^q$ , $q\ge 1$ .
`M`	number of Monte Carlo replicates. Defaults to `1e5`.

Details

int_cir is an extension of equation (4.1.11) in Press et al. (1997), a periodic trapezoidal rule. int_sph employs the Lebedev quadrature on $S^2$ . int_hypsph implements a Monte Carlo integration on $S^q$ .

Value

A scalar approximating the integral.

References

Lebedev, V. I. and Laikov, D. N. (1999). A quadrature formula for the sphere of the 131st algebraic order of accuracy. Doklady Mathematics, 59(3):477–481.

Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery B. P. (1997). Numerical Recipes in Fortran 77: The Art of Scientific Computing. Volume 1. Cambridge University Press, Cambridge. Second edition.

Examples

# S^1, trapezoidal rule
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 1), kappa = 2)
int_cir(f = f)

# S^2, Lebedev rule
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 0, 1), kappa = 2)
int_sph(f = f)

# S^2, Monte Carlo
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 0, 1), kappa = 2)
int_hypsph(f = f, q = 2)
# S^1, trapezoidal rule
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 1), kappa = 2)
int_cir(f = f)

# S^2, Lebedev rule
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 0, 1), kappa = 2)
int_sph(f = f)

# S^2, Monte Carlo
f <- function(x) rotasym::d_vMF(x = x, mu = c(0, 0, 1), kappa = 2)
int_hypsph(f = f, q = 2)

Directional kernel density estimator

Description

Kernel density estimation with directional data as in the estimator of Bai et al. (1988).

Usage

kde_dir(x, data, h, L = NULL)

c_h(h, q, L = NULL)

lambda_L(L = NULL, q)

b_L(L = NULL, q)

d_L(L = NULL, q)
kde_dir(x, data, h, L = NULL)

c_h(h, q, L = NULL)

lambda_L(L = NULL, q)

b_L(L = NULL, q)

d_L(L = NULL, q)

Arguments

`x`	evaluation points, a matrix of size `c(nx, q + 1)`.
`data`	directional data, a matrix of size `c(n, q + 1)`.
`h`	bandwidth, a scalar for `kde_dir`. Can be a vector for `c_h`.
`L`	kernel function. Set internally to `function(x) exp(-x)` (von Mises–Fisher kernel) if `NULL` (default).
`q`	dimension of $S^q$ , $q\ge 1$ .

Details

data is not checked to have unit norm, so the user must be careful. When L = NULL, faster FORTRAN code is employed.

Value

kde_dir returns a vector of size nx with the evaluated kernel density estimator. c_h returns the normalizing constant for the kernel, a vector of length length(h). lambda_L, b_L, and d_L return moments of L.

References

Bai, Z. D., Rao, C. R., and Zhao, L. C. (1988). Kernel estimators of density function of directional data. Journal of Multivariate Analysis, 27(1):24–39. doi:10.1016/0047-259X(88)90113-3

Examples

# Sample
n <- 50
q <- 3
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Evaluation points
x <- rbind(diag(1, nrow = q + 1), diag(-1, nrow = q + 1))

# kde_dir
kde_dir(x = x, data = samp, h = 0.5, L = NULL)
kde_dir(x = x, data = samp, h = 0.5, L = function(x) exp(-x))

# c_h
c_h(h = 0.5, q = q, L = NULL)
c_h(h = 0.5, q = q, L = function(x) exp(-x))

# b_L
b_L(L = NULL, q = q)
b_L(L = function(x) exp(-x), q = q)

# d_L
d_L(L = NULL, q = q)
d_L(L = function(x) exp(-x), q = q)

# lambda_L
lambda_L(L = NULL, q = q)
lambda_L(L = function(x) exp(-x), q = q)
# Sample
n <- 50
q <- 3
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Evaluation points
x <- rbind(diag(1, nrow = q + 1), diag(-1, nrow = q + 1))

# kde_dir
kde_dir(x = x, data = samp, h = 0.5, L = NULL)
kde_dir(x = x, data = samp, h = 0.5, L = function(x) exp(-x))

# c_h
c_h(h = 0.5, q = q, L = NULL)
c_h(h = 0.5, q = q, L = function(x) exp(-x))

# b_L
b_L(L = NULL, q = q)
b_L(L = function(x) exp(-x), q = q)

# d_L
d_L(L = NULL, q = q)
d_L(L = function(x) exp(-x), q = q)

# lambda_L
lambda_L(L = NULL, q = q)
lambda_L(L = function(x) exp(-x), q = q)

Lebedev quadrature on the sphere

Description

Nodes and weights for Lebedev quadrature on the sphere $S^2$ . The rule has 5810 points and is exact up to polynomials of order 131.

Usage

lebedev
lebedev

Format

A data frame with 5810 rows and two variables:

xyz: nodes for quadrature, a matrix with three columns.
w: weights for quadrature, a vector.

Details

The approximation to the integral of $f$ has the form

$\int_{S^2} f(x, y, z) \,\mathrm{d}x \,\mathrm{d}y \,\mathrm{d}z = 4 \pi \sum_{i = 1}^N w_i f(x_i, y_i, z_i)$

where $N = 5810$ . The nodes (in spherical coordinates) and weights are processed from lebedev_131.txt.

Source

https://people.sc.fsu.edu/~jburkardt/datasets/sphere_lebedev_rule/sphere_lebedev_rule.html

References

Lebedev, V. I. and Laikov, D. N. (1999). A quadrature formula for the sphere of the 131st algebraic order of accuracy. Doklady Mathematics, 59(3):477–481.

Examples

# Load data
data("lebedev")

# Integrate x_1 * x_2^2 (zero integral)
f_1 <- function(x) x[, 1] * x[, 2]^2
4 * pi * sum(lebedev$w * f_1(lebedev$xyz))
# Load data
data("lebedev")

# Integrate x_1 * x_2^2 (zero integral)
f_1 <- function(x) x[, 1] * x[, 2]^2
4 * pi * sum(lebedev$w * f_1(lebedev$xyz))

Von Mises–Fisher distribution utilities

Description

Maximum likelihood estimation for the von Mises–Fisher distribution and evaluation of density mixtures.

Usage

kappa_ml(data, min_kappa = 1e-04, max_kappa = 100, ...)

mu_ml(data)

d_mixvmf(x, mu, kappa, p, norm = FALSE)
kappa_ml(data, min_kappa = 1e-04, max_kappa = 100, ...)

mu_ml(data)

d_mixvmf(x, mu, kappa, p, norm = FALSE)

Arguments

`data`	directional data, a matrix of size `c(n, q + 1)`.
`min_kappa`, `max_kappa`	minimum and maximum kappas to look for the maximum likelihood estimate.
`...`	further parameters passed to `uniroot`.
`x`	evaluation points, a matrix of size `c(nx, q + 1)`.
`mu`, `kappa`, `p`	mixture parameters. `mu` is the mean matrix of size `c(length(p), q + 1)`, `kappa` is vector of `length(p)` concentration parameters, and `p` is the vector of mixture proportions.
`norm`	enforce normalization of `x` internally? Defaults to `FALSE`.

Value

Estimated vector mean (mu_ml) or concentration parameter (kappa_ml). A vector of length nx for d_mixvmf.

Examples

# Sample
n <- 50
q <- 2
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Estimates
mu_ml(samp)
kappa_ml(samp)

# Mixture
x <- to_cir(seq(0, 2 * pi, l = 200))
dens <- d_mixvmf(x = x, mu = rbind(c(-1, 0), c(0, 1), c(1, 0)),
                 kappa = 1:3, p = c(0.5, 0.2, 0.3))
plot(to_rad(x), dens, type = "l")
# Sample
n <- 50
q <- 2
samp <- rotasym::r_vMF(n = n, mu = c(1, rep(0, q)), kappa = 2)

# Estimates
mu_ml(samp)
kappa_ml(samp)

# Mixture
x <- to_cir(seq(0, 2 * pi, l = 200))
dens <- d_mixvmf(x = x, mu = rbind(c(-1, 0), c(0, 1), c(1, 0)),
                 kappa = 1:3, p = c(0.5, 0.2, 0.3))
plot(to_rad(x), dens, type = "l")

Package 'DirStats'

Help Index

DirStats – Nonparametric Methods for Directional Data

Description

Author(s)

Fitting mixtures of von Mises–Fisher distributions

Description

Usage

Arguments

Details

Value

References

Examples

Cross-validation bandwidth selectors for directional data

Description

Usage

Arguments

Details

Value

Source

References

Examples

Plug-in bandwidth selectors for directional data

Description

Usage

Arguments

Details

Value

References

Examples

Convenience functions

Description

Usage

Arguments

Value

Examples

Integration routines

Description

Usage

Arguments

Details

Value

References

Examples

Directional kernel density estimator

Description

Usage

Arguments

Details

Value

References

Examples

Lebedev quadrature on the sphere

Description

Usage

Format

Details

Source

References

Examples

Von Mises–Fisher distribution utilities

Description

Usage

Arguments

Value

Examples

`DirStats` – Nonparametric Methods for Directional Data