plotClustersPCA {DIRECT} | R Documentation |
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.
plotClustersPCA(item.names, data.summary,
PCA.label.adj = -0.01, ...)
item.names |
A vector of character strings, indicating how each item should be labeled in the PCA plot. |
data.summary |
The list generated from |
PCA.label.adj |
A scalar to be added to the coordinates of |
... |
Additional arguments for |
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.
None.
Audrey Q. Fu
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.
summaryDIRECT
for processing MCMC estimates for clustering and generating the list data.summary
used here.
plotClustersMean
, plotClustersSD
, plotSimulation
.
## See example in DIRECT.