# interactive2D: A GUI for classification in two dimensions using smoothed... In LogConcDEAD: Log-Concave Density Estimation in Arbitrary Dimensions

## Description

Uses `tkrplot` to create a GUI for two-class classification in two dimensions using the smoothed log-concave maximum likelihood estimates

## Usage

 `1` ``` interactive2D(data, cl) ```

## Arguments

 `data` Data in R^2, in the form of an n x 2 numeric `matrix` `cl` factor of true classifications of the data set

## Details

This function uses `tkrplot` to create a GUI for two-class classification in two dimensions using the smoothed log-concave maximum likelihood estimates. The construction of the classifier is standard, and can be found in Chen and Samworth (2011). The slider controls the risk ratio of two classes (equals one by default), which provides a way of demonstrating how the decision boundaries change as the ratio varies. Observations from different classes are plotted in red and green respectively.

## Value

A GUI with a slider

## Author(s)

Yining Chen

Robert B. Gramacy

Richard Samworth

## References

Chen, Y. and Samworth, R. J. (2013) Smoothed log-concave maximum likelihood estimation with applications Statist. Sinica, 23, 1373-1398. http://arxiv.org/abs/1102.1191v4

Cule, M. L., Samworth, R. J., and Stewart, M. I. (2010) Maximum likelihood estimation of a log-concave density, Journal of the Royal Statistical Society, Series B, 72(5) p.545-607.

`dslcd`,`mlelcd`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```## Simple bivariate normal data ## only works interactively, not run as a test example here # set.seed( 1 ) # n = 15 # d = 2 # props=c( 0.6, 0.4 ) # x <- matrix( rnorm( n*d ), ncol = d ) # shiftvec <- ifelse( runif( n ) > props[ 1 ], 0, 1) # x[,1] <- x[,1] + shiftvec # interactive2D( x, shiftvec ) ```