influence_weights: Influence Weights
In cpr: Control Polygon Reduction
Description
Usage
Arguments
Value
Author(s)
Description
Determine the influence weight of each internal knot on each marginal of a
tensor product.
Usage
1
2
influence_weights(x, p = 2, margin = seq_along(x$bspline_list),
n_polycoef = 20L)
Arguments
x
a cpr_cp or cpr_cn object
p
the order of the norm, default p = 2.
margin
The margins to apply CNR to. Ignored for cpr_cp
objects.
n_polycoef
for influence weights in tensor products, this parameter
set the number of polynomial coefficients to use in each of the marginal
calculations. Ignored for cpr_cp objects.
Value
A data_frame with two elements, the internal knots (iknots) and the weights.
Author(s)
Peter DeWitt dewittpe@gmail.com
cpr documentation built on May 1, 2019, 10:46 p.m.
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Description Usage Arguments Value Author(s)
Description
Determine the influence weight of each internal knot on each marginal of a tensor product.
Usage
1 2 | influence_weights(x, p = 2, margin = seq_along(x$bspline_list),
n_polycoef = 20L)
|
Arguments
x |
a |
p |
the order of the norm, default |
margin |
The margins to apply CNR to. Ignored for |
n_polycoef |
for influence weights in tensor products, this parameter
set the number of polynomial coefficients to use in each of the marginal
calculations. Ignored for |
Value
A data_frame with two elements, the internal knots (iknots) and the weights.
Author(s)
Peter DeWitt dewittpe@gmail.com
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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