aneuBiHMM: Bivariate Hidden Markov Model

aneuBiHMMR Documentation

Bivariate Hidden Markov Model

Description

The aneuBiHMM object is output of the function findCNVs.strandseq and is basically a list with various entries. The class() attribute of this list was set to "aneuBiHMM". For a given hmm, the entries can be accessed with the list operators 'hmm[[]]' and 'hmm$'.

Value

ID

An identifier that is used in various AneuFinder functions.

bins

A GRanges-class object containing the genomic bin coordinates, their read count and state classification.

segments

A GRanges-class object containing regions and their state classification.

weights

Weight for each component.

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

startProbs.initial

Initial startProbs at the beginning of the Baum-Welch.

distributions

Estimated parameters of the emission distributions.

distributions.initial

Distribution parameters at the beginning of the Baum-Welch.

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.

See Also

findCNVs.strandseq


ataudt/aneufinder documentation built on April 18, 2023, 4:20 a.m.