ridge_pca {ridgetorus} | R Documentation |
This function computes the whole process of toroidal PCA via density ridges on a given sample: parameter estimation of the underlying distribution, estimation of the connected component of the ridge, and determination of its Fourier expansion from which to obtain the first and second scores.
ridge_pca(
x,
type = c("auto", "bvm", "bwc")[1],
N = 500,
K = 15,
scale = TRUE,
lrts = TRUE,
alpha = 0.05,
at2 = TRUE,
...
)
x |
matrix of dimension |
type |
either |
N |
number of discretization points for approximating curve lengths.
Defaults to |
K |
number of terms in the Fourier expansion. Defaults to |
scale |
scale the resulting scores to |
lrts |
run |
alpha |
significance level for the homogeneity test. |
at2 |
do the |
... |
optional parameters passed to |
A list with:
mu_hat |
estimated circular means of the sample. |
coefs_hat |
estimated Fourier coefficients. |
ind_var |
indexing variable. |
scores |
scores for each of the sample points. |
var_exp |
percentage of explained variance. |
fit_mle |
maximum likelihood fit. |
bic_fit |
BIC of the fit. |
data |
original sample. |
scales |
vector of length 2 with the scale limits for the axes. |
type |
type of fit performed. |
p_hom |
|
p_indep |
|
## Bivariate von Mises
n <- 100
x <- r_bvm(n = n, mu = c(1, 2), kappa = c(0.4, 0.4, 0.5))
fit <- ridge_pca(x = x, type = "bvm")
show_ridge_pca(fit = fit, col_data = "red")
x <- r_bvm(n = n, mu = c(2, 1), kappa = c(1, 2, 0))
fit <- ridge_pca(x = x, type = "bvm")
show_ridge_pca(fit = fit, col_data = "red")
x <- r_bvm(n = n, mu = c(2, 1), kappa = c(3, 2, 0))
fit <- ridge_pca(x = x, type = "bvm")
show_ridge_pca(fit = fit, col_data = "red")
## Bivariate wrapped Cauchy
x <- r_bwc(n = n, mu = c(1, 2), xi = c(0.2, 0.2, 0.5))
fit <- ridge_pca(x = x, type = "bwc")
show_ridge_pca(fit = fit, col_data = "red")
x <- r_bwc(n = n, mu = c(1, 2), xi = c(0.2, 0.8, 0))
fit <- ridge_pca(x = x, type = "bwc")
show_ridge_pca(fit = fit, col_data = "red")
x <- r_bwc(n = n, mu = c(1, 2), xi = c(0.5, 0.2, 0))
fit <- ridge_pca(x = x, type = "bwc")
show_ridge_pca(fit = fit, col_data = "red")