Multivariate HMM object

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Description

The multivariate HMM object is output of the function callPeaksMultivariate and is a list() with various entries. The class() attribute of this list was set to "multiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' or 'hmm$'.

Value

A list() with the following entries:

IDs

IDs of the input univariate HMMs.

bins

A GRanges object containing the genomic bin coordinates, their read count, (optional) posteriors and state classification.

segments

Same as bins, but consecutive bins with the same state are collapsed into segments.

mapping

A named vector giving the mapping from decimal combinatorial states to human readable combinations.

weights

Weight for each component. Same as apply(hmm$posteriors,2,mean).

weights.univariate

Weights of the univariate HMMs.

transitionProbs

Matrix of transition probabilities from each state (row) into each state (column).

transitionProbs.initial

Initial transitionProbs at the beginning of the Baum-Welch.

startProbs

Probabilities for the first bin. Same as hmm$posteriors[1,].

startProbs.initial

Initial startProbs at the beginning of the Baum-Welch.

distributions

Emission distributions used for this model.

post.cutoff

False discovery rate. NULL means that the state with maximum posterior probability was chosen, irrespective of its absolute probability (default=NULL).

convergenceInfo

Contains information about the convergence of the Baum-Welch algorithm.

convergenceInfo$eps

Convergence threshold for the Baum-Welch.

convergenceInfo$loglik

Final loglikelihood after the last iteration.

convergenceInfo$loglik.delta

Change in loglikelihood after the last iteration (should be smaller than eps)

convergenceInfo$num.iterations

Number of iterations that the Baum-Welch needed to converge to the desired eps.

convergenceInfo$time.sec

Time in seconds that the Baum-Welch needed to converge to the desired eps.

correlation.matrix

Correlation matrix of transformed reads.

See Also

callPeaksMultivariate, uniHMM, combinedMultiHMM

Examples

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## Get an example multiHMM
file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData",
                    package="chromstaR")
model <- get(load(file))

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