feature {feature}R Documentation

feature

Description

Package for feature significance for multivariate kernel density estimation.

Details

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").

Author(s)

Tarn Duong <tarn.duong@gmail.com> & Matt Wand <wand@uow.edu.au>

References

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.

See Also

sm, KernSmooth


[Package feature version 1.2.2 Index]