Function summaryDIRECT processes posterior estimates in the output files from DIRECT for clustering and parameter estimation.
summaryDIRECT(data.name, PERM.ADJUST = FALSE)A list with components:
The number of items in the data.
The number of inferred clusters.
A vector of length nitem, each component being the maximum posterior probability of allocating the corresponding item to a cluster.
Vector of cluster sizes.
An integer vector of labels of inferred clusters. The integers are not necessarily consecutive; that is, an inferred mixture component that is associated with items at small posterior allocation probabilities is dropped from the final list of cluster labels.
A data frame containing "first", the most likely allocation; "second", the second most likely allocation; "prob1", the posterior allocation probability associated with "first"; and "prob2", the posterior allocation probability associated with "second".
A nitem-by-nclust matrix of mean posterior allocation probability matrix.
A matrix of nclust rows. Each row, corresponding to an inferred cluster, contains the posterior mean estimates of cluster-specific parameters.
A matrix of nclust rows. Each row, corresponding to an inferred cluster, contains the posterior median estimates of cluster-specific parameters.
A list containing two components:
post.pars.mean: Matrix each row of which contains the posterior mean estimates of parameters for a mixture component.
post.pars.median: Matrix each row of which contains the posterior median estimates of parameters for a mixture component.
A character string indicating the prefix of output files.
If TRUE, add 1 to labels of mixture components such that the labels start from 1 instead of 0.
Audrey Q. Fu
Output files from DIRECT include MCMC samples before relabeling and permuted labels of mixture components after relabeling. Function summaryDIRECT uses permuted labels stored in output file *_mcmc_perms.out to reorganize the MCMC samples stored in other output files *_mcmc_cs.out, *_mcmc_pars.out and *_mcmc_probs.out. It defines each mixture component as a cluster.
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.
DIRECT for what output files are produced.
simuDataREM for simulating data under the mixture random-effects model.