feature {feature} | R Documentation |
Package for feature significance for multivariate kernel density estimation.
The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).
There are two main functions in this package.
featureSignifGUI
is the interactive function where
the user can select bandwidths from a pre-defined range. This
mode is useful for initial exploratory
data analysis. featureSignif
is the non-interactive function.
This is useful when the user has a more
definite idea of suitable values for the bandwidths.
For a more detailed example for 1-d and 2-d data, see
vignette("feature")
.
Tarn Duong <tarn.duong@gmail.com> & Matt Wand <wand@uow.edu.au>
Chaudhuri, P. and Marron, J.S. (1999) SiZer for exploration of structures in curves. Journal of the American Statistical Association, 94, 807-823.
Duong, T., Cowling, A., Koch, I., Wand, M.P. (2008) Feature significance for multivariate kernel density estimation. Computational Statistics and Data Analysis, 52, 4225-4242.
Godtliebsen, F., Marron, J.S. and Chaudhuri, P. (2002) Significance in scale space for bivariate density estimation. Journal of Computational and Graphical Statistics, 11, 1-22.
Scott, D.W. (1992) Multivariate Density Estimation: Theory, Practice, and Visualization. John Wiley & Sons. New York.
Wand, M.P. & Jones, M.C. (1995) Kernel Smoothing. Chapman & Hall/CRC. London.