# Relative Likelihood based clustering assuming Poisson distribution.

### Description

The function performs PCA on matrix based on weighted relative likelihood function and provides a plot of first two PCs as well as summary of PCA.

### Usage

1 | ```
pois.rel.pca(x, lambda.min, lambda.max, len = 10, plot = TRUE, seed = 132)
``` |

### Arguments

`x` |
Data can be entered as matrix or list. |

`lambda.min` |
Minimum value of lambda. |

`lambda.max` |
Maximum value of lambda. |

`len` |
Length of values to be evaluated at in between mu.min and mu.max. |

`plot` |
If set TRUE, provides plot of weighted relative likelihood functions colored by their cluster assignment. |

`seed` |
Seed to be set for reproducibility |

### Details

For mathematical details, please contact the authors.

### Value

`PCA.output` |
Summary of Principal Component Analysis |

### Note

None.

### Author(s)

Milan Bimali.

### References

None.

### Examples

1 2 | ```
x <- sim.pois(c(4,10),15,10)
pois.rel.pca(x,1,20,len=20,plot=TRUE,seed=132)
``` |

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