multiHMM: Multivariate HMM object

In chromstaR: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data

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:

info	Experiment table for this object.
bincounts	A GRanges-class object containing the genomic bin coordinates and original binned read count values for different offsets.
bins	A GRanges-class 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.
peaks	A list() with GRanges-class containing peak coordinates for each ID in info.
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.
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

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

chromstaR documentation built on Nov. 8, 2020, 8:29 p.m.