# Getter set of binary rules (object PartitionWithLLR)

### Description

This function returns the binary rule for discrimination between data from class k and data from class l

### Usage

1 | ```
getBinaryRule(object, k, l)
``` |

### Arguments

`object` |
An object of class PartitionWithLLR as returned by learnPartitionWithLLR |

`k` |
an existing label |

`l` |
an existing label |

### Value

A binary classification rule. Can either be an object of class LinearRule or an object of class QuadraticRule

### Author(s)

Robin Girard

### References

Fast rate of convergence in high dimensional linear discriminant analysis. R. Girard To appear in Journal of Nonparametric Statistics.\ Very high dimensional discriminant analysis with thresholding estimation. R. Girard. Submitted.

### See Also

`getLogLikeRatio`

### Examples

1 2 3 4 5 6 7 8 9 10 11 | ```
#try p=1000 , 5000, ...
p=100; n=20 ; mu=array(0,c(p,4)); mu[1:10,1]=2 ;mu[11:20,2]=2;C=array(c(1,20),p)
mu[21:30,3]=2
x=NULL; y=NULL;
for (k in 1:4){
x=rbind(x,t(array(C^(1/2),c(p,n))*(matrix(rnorm(p*n),nrow=p,ncol=n))+array(mu[,k],c(p,n))));
y=c(y,array(k,n))}
#Learning
LearnedLinearPartitionWithLLR=learnPartitionWithLLR(x,y,procedure='FDRThresh')
Rule=getBinaryRule(LearnedLinearPartitionWithLLR,1,2)
show(Rule)
``` |

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