Description Usage Arguments Details Value Author(s) See Also
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.
1 2 
x 
Data in R^d, in the form of an n x d
numeric 
y 
Value of log of maximum likelihood estimator at data points 
w 
Vector of weights w_i such that the computed estimator maximizes w[1] log f(x[1,]) + ... + w[n] log f([x,n]) subject to the restriction that f is logconcave. The default is 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 
Realvalued 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) 
This function is used in mlelcd
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 

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 
Realvalued scalar giving minimum value of the objective function 
b 

beta 

triang 

verts 

vertsoffset 

chull 
Vector containing vertices of faces of the convex hull of the data 
outnorm 

outoffset 

Madeleine Cule
Robert B. Gramacy
Richard Samworth
Yining Chen
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