Description Usage Arguments Value
Learn a model and produce a segmentation
1 2 3 |
counts |
Count matrix or list of count matrices matching with the
|
regions |
GRanges object containing the genomic regions of interest. Each of these regions corresponds to a set of bins and each bin to a column of the count matrix. The binsize is automatically derived by comparing the columns of the count matrix with the width of the regions. |
nstates |
Number of states to learn. |
model |
A list with the parameters that describe
the HMM. Missing parameters will be learned, and the provided
parameters will be used as initial parameters for the learning
algorithm. If |
notrain |
If FALSE, the parameters will be learned, otherwise
the provided parameters (with the |
collapseInitP |
In case a model with multiple initial probabilities is provided, should those probabilities be averaged and reduced to one initial probabilities vector? If you are not sure about what this means, don't set this option. |
nthreads |
number of threads used for learning |
split4speed |
add artificial splits in the input regions to improve
the parallelism of the forward-backward algorithm. Usually the results change
very little and the algorithm runs considerably faster, if the number of
input regions is smaller than the number of threads. See |
maxiter |
Maximum number of iterations for learning. |
... |
Advanced options for learning. Type
|
A list with the following arguments:
segments |
The segmentation as a GRanges object.
The slot |
model |
A list containing all the parameters of the model. |
posteriors |
A matrix of size |
states |
An integer vector of length |
viterbi |
Same as |
loglik |
the log-likelihood of the whole dataset. |
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