spherical: Spherical Spline Basis and Penalty

In npreg: Nonparametric Regression via Smoothing Splines

Spherical Spline Basis and Penalty

Description

Generate the smoothing spline basis and penalty matrix for a spherical spline. This basis is designed for predictors where the values are points on a sphere.

Usage

basis.sph(x, knots, m = 2, intercept = FALSE, ridge = FALSE)

penalty.sph(x, m = 2)

Arguments

x	Predictor variables (basis) or spline knots (penalty). Matrix of dimension n by 2. Column 1 is latitude (-90 to 90 deg) and column 2 is longitude (-180 to 180 deg).
knots	Spline knots. Matrix of dimension r by 2. Column 1 is latitude (-90 to 90 deg) and column 2 is longitude (-180 to 180 deg).
m	Penalty order. "m=2" for 2nd order spherical spline, "m=3" for 3rd order, and "m=4" for 4th order.
intercept	If TRUE, the first column of the basis will be a column of ones.
ridge	If TRUE, the basis matrix is post-multiplied by the inverse square root of the penalty matrix. See Note and Examples.

Details

Generates a basis function or penalty matrix used to fit spherical splines of order 2, 3, or 4.

With an intercept included, the basis function matrix has the form

X = [X_0, X_1]

where matrix X_0 is an n by 1 matrix of ones, and X_1 is a matrix of dimension n by r.

The X_0 matrix contains the "parametric part" of the basis (i.e., the intercept).

The matrix X_1 contains the "nonparametric part" of the basis, which consists of the reproducing kernel function

ρ(x, y) = [q_{2m-2}(x.y) - α] / β

evaluated at all combinations of x and knots. Note that α = 1/(2m - 1) and β = 2π(2m-2)! are constants, q_{2m-2}(.) is the spherical spline semi-kernel function, and x.y denotes the cosine of the angle between x and y (see References).

The penalty matrix consists of the reproducing kernel function

ρ(x, y) = [q_{2m-2}(x.y) - α] / β

evaluated at all combinations of x.

Value

Basis: Matrix of dimension c(length(x), df) where df = nrow(knots) + intercept.

Penalty: Matrix of dimension c(r, r) where r = nrow(x) is the number of knots.

Note

The inputs x and knots must have the same dimension.

If ridge = TRUE, the penalty matrix is the identity matrix.

Author(s)

Nathaniel E. Helwig <[e-mail]>

References

Gu, C. (2013). Smoothing Spline ANOVA Models. 2nd Ed. New York, NY: Springer-Verlag. doi: 10.1007/978-1-4614-5369-7

Wahba, G (1981). Spline interpolation and smoothing on the sphere. SIAM Journal on Scientific Computing, 2(1), 5-16. doi: 10.1137/0902002

See Also

See thinplate for a thin plate spline basis and penalty.

Examples

######***######   standard parameterization   ######***######

# function with spherical predictors
set.seed(0)
n <- 1000
myfun <- function(x){
  sin(pi*x[,1]) + cos(2*pi*x[,2]) + cos(pi*x[,3])
  }
x3d <- cbind(runif(n), runif(n), runif(n)) - 0.5
x3d <- t(apply(x3d, 1, function(x) x / sqrt(sum(x^2))))
eta <- myfun(x3d)
y <- eta + rnorm(n, sd = 0.5)

# convert x latitude and longitude
x <- cbind(latitude = acos(x3d[,3]) - pi/2,
           longitude = atan2(x3d[,2], x3d[,1])) * (180 / pi)

# select first 100 points as knots
knots <- x[1:100,]

# cubic spherical spline basis
X <- basis.sph(x, knots, intercept = TRUE)

# cubic spherical spline penalty
Q <- penalty.sph(knots)

# pad Q with zeros (for intercept)
Q <- rbind(0, cbind(0, Q))

# define smoothing parameter
lambda <- 1e-5

# estimate coefficients
coefs <- psolve(crossprod(X) + n * lambda * Q) %*% crossprod(X, y)

# estimate eta
yhat <- X %*% coefs

# check rmse
sqrt(mean((eta - yhat)^2))



######***######   ridge parameterization   ######***######

# function with spherical predictors
set.seed(0)
n <- 1000
myfun <- function(x){
  sin(pi*x[,1]) + cos(2*pi*x[,2]) + cos(pi*x[,3])
  }
x3d <- cbind(runif(n), runif(n), runif(n)) - 0.5
x3d <- t(apply(x3d, 1, function(x) x / sqrt(sum(x^2))))
eta <- myfun(x3d)
y <- eta + rnorm(n, sd = 0.5)

# convert x latitude and longitude
x <- cbind(latitude = acos(x3d[,3]) - pi/2,
           longitude = atan2(x3d[,2], x3d[,1])) * (180 / pi)

# select first 100 points as knots
knots <- x[1:100,]

# cubic spherical spline basis
X <- basis.sph(x, knots, intercept = TRUE, ridge = TRUE)

# cubic spherical spline penalty (ridge)
Q <- diag(rep(c(0, 1), times = c(1, ncol(X) - 1)))

# define smoothing parameter
lambda <- 1e-5

# estimate coefficients
coefs <- psolve(crossprod(X) + n * lambda * Q) %*% crossprod(X, y)

# estimate eta
yhat <- X %*% coefs

# check rmse
sqrt(mean((eta - yhat)^2))

npreg documentation built on July 21, 2022, 1:06 a.m.