Thanks for your reply, Charles. spline() doesn't seem to fit a closed
shape; rather, it's producing a parabola. Perhaps I'm missing an
argument I should include?
grid.xspline() seems to get close to what I need, but it returns a grob
object - not sure how to work with those as shapes per se.
My goal is to produce a 2D shape from which I can calculate area,
average widths, and other such things. The context is that we have
measured tree crowns in a manner that has produced 4 points such as
these from two offset axes. We want to use the resulting shapes for our
calculations.
(incidentally, my original points were off - here are the correct ones)
shapepoints = structure(list(x = c(8.9, 0, -7.7, 0, 8.9), y = c(0, 2, 0,
-3.8,
0)), .Names = c("x", "y"), row.names = c(NA, -5L), class = "data.frame")
plot(spline(shapepoints))
Thanks,
Allie
On 3/21/2016 1:10 PM, Charles Determan wrote:
Hi Allie,
What is you goal here? Do you just want to plot a curve to the data?
Do you want a function to approximate the data?
You may find the functions spline() and splinefun() useful.
Quick point though, with so few points you are only going to get a very
rough approximation no matter the method used.
Regards,
Charles
On Mon, Mar 21, 2016 at 7:59 AM, Alexander Shenkin <ashen...@ufl.edu
<mailto:ashen...@ufl.edu>> wrote:
Hello all,
I have sets of 4 x/y points through which I would like to fit
closed, smoothed shapes that go through those 4 points exactly.
smooth.spline doesn't like my data, since there are only 3 unique x
points, and even then, i'm not sure smooth.spline likes making
closed shapes.
Might anyone else have suggestions for fitting algorithms I could
employ?
Thanks,
Allie
shapepoints = structure(c(8.9, 0, -7.7, 0, 0, 2, 0, 3.8), .Dim = c(4L,
2L), .Dimnames = list(NULL, c("x", "y")))
smooth.spline(shapepoints)
# repeat the first point to close the shape
shapepoints = rbind(shapepoints, shapepoints[1,])
smooth.spline(shapepoints)
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