Description Usage Arguments Value Note See Also Examples
The function mba.surf
returns a surface approximated from a
bivariate scatter of data points using multilevel Bsplines.
1 2 
xyz 
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. 
no.X 
resolution of the approximated surface along the x axis. 
no.Y 
resolution of the approximated surface along the y axis. 
n 
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. 
m 
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. 
h 
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. 
extend 
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 
sp 
if TRUE, the resulting surface is returned as a

... 

List with 8 component:
xyz.est 
a list that contains vectors x, y and the no.X x no.Y matrix z of estimated zvalues. 
no.X 

no.Y 

n 

m 

h 

extend 

sp 

b.box 

If no.X != no.Y
then use sp=TRUE
for compatibility with
the image
function.
The function mba.surf
relies on the Multilevel Bspline
Approximation (MBA) algorithm. The underlying code was developed at
SINTEF Applied Mathematics by Dr. <c3><98>yvind Hjelle. Dr. <c3><98>yvind Hjelle
based the algorithm on the paper by the originators of Multilevel Bsplines:
S. Lee, G. Wolberg, and S. Y. Shin. (1997) Scattered data interpolation with multilevel Bsplines. IEEE Transactions on Visualization and Computer Graphics, 3(3):229–244.
For additional documentation and references see:
www.sintef.no/upload/IKT/9011/geometri/MBA/mba_doc/index.html.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  ## 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[[1]]
y < mba.int[[2]]
z < ex*mba.int[[3]]
zlim < range(z)
zlen < zlim[2]  zlim[1] + 1
colorlut < heat.colors(as.integer(zlen))
col < colorlut[ zzlim[1]+1 ]
open3d()
surface3d(x, y, z, color=col, back="lines")
## End(Not run)

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