interactive2D: A GUI for classification in two dimensions using smoothed...

In LogConcDEAD: Log-Concave Density Estimation in Arbitrary Dimensions

A GUI for classification in two dimensions using smoothed log-concave

Description

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

Usage

  interactive2D(data, cl)

Arguments

data	Data in R^2, in the form of an n \times 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 (2013). 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

Madeleine Cule

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. https://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.

See Also

dslcd,mlelcd

Examples

## Simple bivariate normal data
## only works interactively, not run as a test example here
if(interactive()){
  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 )
}

LogConcDEAD documentation built on April 6, 2023, 1:11 a.m.