SPC: Spherical principal curves

In spherepc: Spherical Principal Curves

Description

This function fits a spherical principal curve.

Usage

SPC(data, q = 0.1, T = nrow(data), step.size = 1e-3, maxit = 10, 
type = "Intrinsic", thres = 0.1, deletePoints = FALSE, plot.proj = FALSE, 
kernel = "quartic", col1 = "blue", col2 = "green", col3 = "red")

Arguments

data	matrix or data frame consisting of spatial locations with two columns. Each row represents a longitude and latitude (denoted by degrees).
q	numeric value of the smoothing parameter. The parameter plays the same role, as the bandwidth does in kernel regression, in the SPC function. The value should be a numeric value between 0.01 and 0.5. The default is 0.1.
T	the number of points making up the resulting curve. The default is 1000.
step.size	step size of the PrincipalCircle function. The default is 0.001. The resulting principal circle is used by an initial estimate of the SPC.
maxit	maximum number of iterations. The default is 30.
type	type of mean on the sphere. The default is "Intrinsic" and the other choice is "Extrinsic".
thres	threshold of the stopping condition. The default is 0.1
deletePoints	logical value. The argument is an option of whether to delete points or not. If deletePoints is FALSE, this function leaves the points in curves which do not have adjacent data for each expectation step. As a result, the function usually returns a closed curve, i.e. a curve without endpoints. If deletePoints is TRUE, this function deletes the points in curves which do not have adjacent data for each expectation step. As a result, The SPC function usually returns an open curve, i.e. a curve with endpoints. The default is FALSE.
plot.proj	logical value. If the argument is TRUE, the projection line for each data is plotted. The default is FALSE.
kernel	kind of kernel function. The default is quartic kernel and alternatives are indicator or Gaussian.
col1	color of data. The default is blue.
col2	color of points in the principal curves. The default is green.
col3	color of resulting principal curves. The default is red.

Details

This function fits a spherical principal curves, and requires to load the 'rgl', 'sphereplot', and 'geosphere' R packages.

Value

plot and a list consisting of

prin.curves	spatial locations (denoted by degrees) of points in the resulting principal curves.
line	connecting line bewteen points of prin.curves.
converged	whether or not the algorithm converged.
iteration	the number of iterations of the algorithm.
recon.error	sum of squared distances from the data to their projections.
num.dist.pt	the number of distinct projections.

Note

This function requires to load 'rgl', 'sphereplot', and 'geosphere' R packages.

Author(s)

Jongmin Lee

References

Jang-Hyun Kim, Jongmin Lee and Hee-Seok Oh. (2020). Spherical Principal Curves <arXiv:2003.02578>.

Jongmin Lee, Jang-Hyun Kim and Hee-Seok Oh. (2021). Spherical principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2165-2171. <doi.org/10.1109/TPAMI.2020.3025327>.

See Also

SPC.Hauberg.

Examples

library(rgl)
library(sphereplot)
library(geosphere)

#### example 2: waveform data
n <- 200
alpha <- 1/3; freq <- 4                           # amplitude and frequency of wave
sigma1 <- 2; sigma2 <- 10                         # noise levels  
lon <- seq(-180, 180, length.out = n)             # uniformly sampled longitude
lat <- alpha * 180/pi * sin(freq * lon * pi/180) + 10.        # latitude vector
## add Gaussian noises on latitude vector
lat1 <- lat + sigma1 * rnorm(length(lon)); lat2 <- lat + sigma2 * rnorm(length(lon))
wave1 <- cbind(lon, lat1); wave2 <- cbind(lon, lat2)
## implement SPC to the (noisy) waveform data
SPC(wave1, q = 0.05)
SPC(wave2, q = 0.05)

#### example 1: earthquake data
data(Earthquake)
names(Earthquake)
earthquake <- cbind(Earthquake$longitude, Earthquake$latitude)   
SPC(earthquake, q = 0.1)
## options 1: plot the projection lines (use option of plot.proj = TRUE)
SPC(earthquake, q = 0.1, plot.proj = TRUE)
## option 2: open principal curves (use option of deletePoints = TRUE)
SPC(earthquake, q = 0.04, deletePoints = TRUE)

spherepc documentation built on Oct. 7, 2021, 9:14 a.m.