shrink_into_CH | R Documentation |
This function uses linear programming to find the value by which
vector p
needs to be scaled towards or away from vector m
in
order for p
to be on the boundary of the convex hull of rows of
M
. If p
is a matrix, a value that scales all rows of p
into
the convex hull of M
is found.
shrink_into_CH(
p,
M,
m = NULL,
verbose = FALSE,
...,
solver = c("glpk", "lpsolve")
)
p |
a |
M |
an |
m |
a |
verbose |
A logical or an integer to control the amount of
progress and diagnostic information to be printed. |
... |
arguments passed directly to linear program solver. |
solver |
a character string selecting which solver to use; by
default, tries |
Logical, telling whether p
is (or all rows of
p
are) in the closed convex hull of the points in
M
.
The scaling factor described above is
returned. shrink_into_CH() >= 1
indicates that all points in
p
are in the convex hull of M
.
This is a successor to the deprecated function is.inCH()
, which
was originally written for the "stepping" algorithm of Hummel et al
(2012). See Krivitsky, Kuvelkar, and Hunter (2022) for detailed
discussion of algorithms used in is.inCH()
and
shrink_into_CH()
.
https://www.cs.mcgill.ca/~fukuda/soft/polyfaq/node22.html
Hummel, R. M., Hunter, D. R., and Handcock, M. S. (2012), Improving Simulation-Based Algorithms for Fitting ERGMs, Journal of Computational and Graphical Statistics, 21: 920-939.
Krivitsky, P. N., Kuvelkar, A. R., and Hunter, D. R. (2022). Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming. arXiv preprint arXiv:2202.03572. https://arxiv.org/abs/2202.03572
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