getinfolcd: Construct an object of class LogConcDEAD

In LogConcDEAD: Log-Concave Density Estimation in Arbitrary Dimensions

Construct an object of class LogConcDEAD

Description

A function to construct an object of class LogConcDEAD from a dataset (given as a matrix) and the value of the log maximum likelihood estimator at datapoints.

Usage

getinfolcd(x, y, w = rep(1/length(y), length(y)), chtol = 10^-6, 
  MinSigma = NA, NumberOfEvaluations = NA)

Arguments

x	Data in R^d, in the form of an n \times d numeric matrix
y	Value of log of maximum likelihood estimator at data points
w	Vector of weights w_i such that the computed estimator maximizes \sum_{i=1}^n w_i \log f(x_i) subject to the restriction that f is log-concave. The default is \frac{1}{n} for all i, which corresponds to i.i.d. observations.
chtol	Tolerance for computation of convex hull. Altering this is not recommended.
MinSigma	Real-valued scalar giving minimum value of the objective function
NumberOfEvaluations	Vector containing the number of steps, number of function evaluations, and number of subgradient evaluations. If the SolvOpt algorithm fails, the first component will be an error code (<0)

Details

This function is used in mlelcd

Value

An object of class "LogConcDEAD", with the following components:

x	Data copied from input (may be reordered)
w	weights copied from input (may be reordered)
logMLE	vector of the log of the maximum likelihood estimate, evaluated at the observation points
NumberOfEvaluations	Vector containing the number of steps, number of function evaluations, and number of subgradient evaluations. If the SolvOpt algorithm fails, the first component will be an error code (<0).
MinSigma	Real-valued scalar giving minimum value of the objective function
b	matrix (see Details)
beta	vector (see Details)
triang	matrix containing final triangulation of the convex hull of the data
verts	matrix containing details of triangulation for use in dlcd
vertsoffset	matrix containing details of triangulation for use in dlcd
chull	Vector containing vertices of faces of the convex hull of the data
outnorm	matrix where each row is an outward pointing normal vectors for the faces of the convex hull of the data. The number of vectors depends on the number of faces of the convex hull.
outoffset	matrix where each row is a point on a face of the convex hull of the data. The number of vectors depends on the number of faces of the convex hull.

Author(s)

Madeleine Cule

Robert B. Gramacy

Richard Samworth

Yining Chen

See Also

mlelcd

LogConcDEAD documentation built on April 6, 2023, 1:11 a.m.