plotClustersPCA: PCA Plot for Posterior Allocation Probability Matrix

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Function plotClustersPCA generates a Principal Components Analysis (PCA) plot for the posterior mean estimate of allocation probability matrix. The first two principal components are used. See figures in Fu, Russell, Bray and Tavare.

Usage

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plotClustersPCA(item.names, data.summary, 
    PCA.label.adj = -0.01, ...)

Arguments

item.names

A vector of character strings, indicating how each item should be labeled in the PCA plot.

data.summary

The list generated from summaryDIRECT that contains processed posterior estimates.

PCA.label.adj

A scalar to be added to the coordinates of item.names for better display.

...

Additional arguments for plot.

Details

The PCA plot produced here displays the uncertainty in the inferred clustering. Each inferred cluster is shown with a distinct color. The closer two clusters are in the PCA plot, the higher the level of uncertainty in inferring these two clusters.

Value

None.

Author(s)

Audrey Q. Fu

References

Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361.

See Also

summaryDIRECT for processing MCMC estimates for clustering and generating the list data.summary used here.

plotClustersMean, plotClustersSD, plotSimulation.

Examples

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## See example in DIRECT.

DIRECT documentation built on May 1, 2019, 8:08 p.m.