Surface approximation from bivariate scattered data using multilevel B-splines
mba.surf returns a surface approximated from a
bivariate scatter of data points using multilevel B-splines.
mba.surf(xyz, no.X, no.Y, n = 1, m = 1, h = 8, extend=FALSE, sp=FALSE, ...)
- a $n x 3$ matrix or data frame, where $n$ is the number of observed points. The three columns correspond to point x, y, and z coordinates. The z value is the response at the given x, y coordinates.
- resolution of the approximated surface along the x axis.
- resolution of the approximated surface along the y axis.
- initial size of the spline space in the hierarchical construction along the x axis. If the rectangular domain is a square, n = m = 1 is recommended. If the x axis is k times the length of the y axis, n = 1, m = k is recommended. The default is n = 1.
- initial size of the spline space in the hierarchical construction along the y axis. If the y axis is k times the length of the x axis, m = 1, n = k is recommended. The default is m = 1.
- Number of levels in the hierarchical construction. If, e.g., n = m = 1 and h = 8, the resulting spline surface has a coefficient grid of size $2^h$ + 3 = 259 in each direction of the spline surface. See references for additional information.
- if FALSE, a convex hull is computed for the input points
and all matrix elements in z that have centers outside of this
polygon are set to
NA; otherwise, all elements in z are given an estimated z value.
- if TRUE, the resulting surface is returned as a
SpatialPixelsDataFrameobject; otherwise, the surface is in
b.boxis an optional vector to sets the bounding box. The vector's elements are minimum x, maximum x, minimum y, and maximum y, respectively.
List with 8 component:
- a list that contains vectors x, y and the $no.X x no.Y$ matrix z of estimated z-values.
b.boxdefines the bounding box over which z is estimated.
no.X != no.Y then use
sp=TRUE for compatibility with
mba.surf relies on the Multilevel B-spline
Approximation (MBA) algorithm. The underlying code was developed at
SINTEF Applied Mathematics by Dr. Oyvind Hjelle. Dr. Oyvind Hjelle
based the algorithm on the paper by the originators of Multilevel B-splines:
S. Lee, G. Wolberg, and S. Y. Shin. Scattered data interpolation with multilevel B-splines. IEEE Transactions on Visualization and Computer Graphics, 3(3):229--244, 1997.
For additional documentation and references please see:
This minor portion of the MBA codebase was ported by Andrew O. Finley email@example.com.
## Not run: # data(LIDAR) # # mba.int <- mba.surf(LIDAR, 300, 300, extend=TRUE)$xyz.est # # ##Image plot # image(mba.int, xaxs="r", yaxs="r") # # ##Perspective plot # persp(mba.int, theta = 135, phi = 30, col = "green3", scale = FALSE, # ltheta = -120, shade = 0.75, expand = 10, border = NA, box = FALSE) # # ##For a good time I recommend using rgl # library(rgl) # # ex <- 10 # x <- mba.int[] # y <- mba.int[] # z <- ex*mba.int[] # zlim <- range(z) # zlen <- zlim - zlim + 1 # colorlut <- heat.colors(as.integer(zlen)) # col <- colorlut[ z-zlim+1 ] # # open3d() # surface3d(x, y, z, color=col, back="lines") # ## End(Not run)