plotClustersMean: Plotting Clustered Mean Vectors

In DIRECT: Bayesian Clustering of Multivariate Data Under the Dirichlet-Process Prior

Plotting Clustered Mean Vectors

Description

Function plotClustersMean produces a plot of multiple panels. Each panel displays for a inferred cluster the mean vectors of items allocated to this cluster, as well as the inferred cluster mean vector. See figures in Fu, Russell, Bray and Tavare.

Usage

plotClustersMean(data, data.summary, 
    SKIP, nTime = length(times), times = 1:nTime, ...)

Arguments

data	An N \times JR matrix of continuous values, or a data frame containing such a matrix. N is the number if items, J the number of time points (or experimental conditions) and R the number of replicates. Each row contains values for Replicates 1 through R under Condition 1, values for Replicates 1 through R under Condition 2, and so on.
data.summary	The list generated from summaryDIRECT that contains processed posterior estimates.
SKIP	Number of columns in data to be skipped when processing the data.
nTime	Number of time points (or experimental conditions).
times	An integer vector of length nTime, indicating times (or experimental conditions).
...	Additional arguments for plot.

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.

plotClustersPCA, plotClustersSD, plotSimulation.

Examples

## See example in DIRECT.

DIRECT documentation built on Sept. 8, 2023, 5:45 p.m.