is.inCH | R Documentation |
is.inCH()
returns TRUE
if and only if p
is contained in
the convex hull of the points given as the rows of M
. If p
is
a matrix, each row is tested individually, and TRUE
is returned if
all rows are in the convex hull.
shrink_into_CH()
returns the coefficient by which rows of p
can be scaled towards or away from point m
in order for all of them to be in the convex hull of M
or on its boundary.
is.inCH(p, M, verbose = FALSE, ...) shrink_into_CH( p, M, m = NULL, verbose = FALSE, ..., solver = c("glpk", "lpsolve") )
p |
A d-dimensional vector or a matrix with d columns |
M |
An n by d matrix. Each row of |
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 |
is.inCH()
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()
.
Logical, telling whether p
is (or all rows of p
are)
in the closed convex hull of the points in M
.
is.inCH()
has been deprecated in favour of
shrink_into_CH()
, which returns the optimal step length instead
of a yes-or-no test. In general, shrink_into_CH(...)>=1
is
equivalent to 'is.inCH(...).
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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