LogConcDEAD-package {LogConcDEAD} | R Documentation |
This package contains a function to compute the maximum likelihood estimator of a log-concave density in any number of dimensions using Shor's r-algorithm.
Functions to plot (for 1- and 2-d data) and draw samples from the MLE are provided.
lcd.mle
computes the MLE (specified via its value at data points).
Output is a list of class "LogConcDEAD"
which is used for plotting,
function evaluation etc.
lcd.eval
evaluates the MLE at a particular point.
lcd.sample
draws samples from the MLE.
lcd.interp
interpolates the MLE on a grid, for plotting
purposes.
lcd.marg
integrates the MLE in 2-d to allow plotting of
estimates of (currently only axis-aligned) marginals.
plot.LogConcDEAD
produces plots of the MLE, optionally
using the rgl package.
For one dimensional data, the active set algorithm in logcondens is much faster.
Madeleine Cule mlc40@cam.ac.uk
Robert Gramacy bobby@statslab.cam.ac.uk
Richard Samworth rjs57@cam.ac.uk
Maintainer: Madeleine Cule mlc40@cam.ac.uk
Cule, M. L., Samworth, R. J. and Stewart, M. I. (2007) Computing the maximum likelihood estimator of a log-concave density In preparation
Kappel, F. and Kuntsevich, A. V. (2000) An implementation of Shor's r-algorithm Computational Optimization and Applications 15(2) p. 193-205
http://www.uni-graz.at/imawww/kuntsevich/solvopt/
Barber, C.B., Dobkin, D.P., and Huhdanpaa, H.T. (1996) The Quickhull algorithm for convex hulls ACM Trans. on Mathematical Software, 22(4) p. 469-483 http://www.qhull.org
#example with some simple normal data set.seed(101) x <- matrix(rnorm(200),ncol=2) out <- lcd.mle(x) plot(out,itype="ic")