spsi (version 0.1)

sps_eval: Evaluates spline on given points

Description

Function which uses output from sps_prep to evaluate spline on given set of tabulation points

Usage

sps_eval(spline, x, der.x = NULL, y = NULL, der.y = NULL, grid = FALSE)

Arguments

spline

Output of sps_prep function; list containing values of data points, derivatives on the points, degrees of polynomial, etc. see: sps_prep

x

Vector or matrix of x-coordinates of tabulation points.

der.x

vector of requested derivatives of spline with respect to x. By default will return values of spline. If der.x = c(0,1) will return value of spline and its first derivative with respect to x. Only 0 and 1 are supported so far.

y

Vector or matrix of y-coordinates of tabulation points. For bi-variate case only.

der.y

For bi-variate case only. Vector of requested derivatives of spline with respect to y. By default will return values of spline. If der.y = c(0,1) will return value of spline and its first derivative with respect to y. Only 0 and 1 are supported so far. See details.

grid

For bi-variate case only. If TRUE function will return matrix of values of the spline on grid spanned by vectors of tabulation points. If FALSE vector of $f(x[i] , y[i]), i = 1, 2, ..., length(x)$ will be returned. If matrices were given as tabulation points grid is meaningless.

Value

If length of der.x = 1 function will return vector or matrix of values, depending on parameter grid. Otherwise if length of der.x is greater than 1, function will return list of vectors or matrices depending on grid.

Details

Der.x and der.y need some more attention for bi-variate case. If they are not provided they are both assumed to be 0. If user needs more then 1 derivative then length of der.x and der.y must be equal. For example if der.x = c(0,1,0) and der.y = c(0,0,1) than function will return values of spline, values of partial derivative with respect to x and partial derivative with respect to y, respectively, on each tabulation point in a form of list of vectors or matrices.

References

Costantini, P; Fontanella, F; 'Shape Preserving Bi-variate Interpolation' sSIAM J NUMER. ANAL. Vol. 27, No.2, pp. 488-506, April 1990

See Also

sps_fun sps_prep

Examples

## Univariate example
x <- c( 1, 2, 3, 4, 5, 6)
y <- c(16 ,18, 21, 17, 15, 12)
spline <- sps_prep(x, y, shape = 'monotonicity', smoothness = 2)
plot(seq(1, 6, 0.1), sps_eval(spline, seq(1, 6, 0.1)))

## Bivariate example

fun <- function(x,y) pmax(0, sin(pi*x) * sin(pi*y))

X <- seq(-1, 2, 0.5)
Y <- seq(-1, 1, 0.5)
grid <- mesh(X, Y)

Z <- matrix(fun(grid$x, grid$y), ncol = length(Y))

X_ <- seq(-1, 2, 0.05)
Y_ <- seq(-1, 1, 0.05)

# Prepare spline parameters
spline <- sps_prep(X, Y, Z)

# evaluate spline on grid of tabulation points spanned by X_ and Y_
eval <- sps_eval(spline, x = X_, y = Y_, grid = TRUE)

# Plot resulting data
persp3D(X_, Y_, eval)