Relative Likelihood based clustering assuming Poisson distribution.

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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

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