plotClustersSD

Function <code>plotClustersSD</code> displays in a single plot the posterior estimates of cluster-specific standard deviations of the three types of variability (random effects) under the DIRECT model. See figures in Fu et al. (2013).

A Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data. It estimates the number of clusters directly from the data using a Dirichlet-process prior. See 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. <doi:10.1214/13-AOAS650>.

Audrey Q Fu

DIRECT

Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior

plotClustersSD function

<dl> <dt>data.summary</dt>
<dd>The list generated from <code>summaryDIRECT</code> that contains processed posterior estimates.</dd> <dt>nTime</dt>
<dd>Number of time points (or experimental conditions).</dd> <dt>...</dt>
<dd>Additional arguments for <code>plot</code>.</dd></dl>

Arguments

Author

Plotting Posterior Estimates of Cluster-Specific Random Effects — plotClustersSD

<dl>

 <dt>data.summary</dt>
<dd>The list generated from <code>summaryDIRECT</code> that contains processed posterior estimates.</dd>

 <dt>nTime</dt>
<dd>Number of time points (or experimental conditions).</dd>

 <dt>...</dt>
<dd>Additional arguments for <code>plot</code>.</dd>

</dl>

plotClustersSD: Plotting Posterior Estimates of Cluster-Specific Random Effects

Description

Usage

Value

Arguments

Author

References

See Also

Examples