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.
getinfolcd(x, y, w = rep(1/length(y), length(y)), chtol = 10^-6,
MinSigma = NA, NumberOfEvaluations = NA)An object of class "LogConcDEAD", with the following
components:
Data copied from input (may be reordered)
weights copied from input (may be reordered)
vector of
the log of the maximum likelihood estimate, evaluated at the observation points
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 \((<0)\).
Real-valued scalar giving minimum value of the objective function
matrix containing row by row the values of \(b_j\)'s corresponding to each triangulation; see also mlelcd
vector containing the values of \(\beta_j\)'s corresponding to each triangulation; see also mlelcd
matrix containing final triangulation of the convex hull of the data
matrix containing details of triangulation for use in dlcd
matrix containing details of triangulation for use in dlcd
Vector containing vertices of faces of the convex hull of the data
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.
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.
Data in \(R^d\), in the form of an \( n \times d\)
numeric matrix
Value of log of maximum likelihood estimator at data points
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.
Tolerance for computation of convex hull. Altering this is not recommended.
Real-valued scalar giving minimum value of the objective function
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 \((<0)\)
Madeleine Cule
Robert B. Gramacy
Richard Samworth
Yining Chen
This function is used in mlelcd
mlelcd