runHomHMM

states.hmm.func

an object of class <code><a rd-options="limma:malist" href="/link/MAList?package=snapCGH&version=1.42.0&to=limma%3Amalist" data-mini-rdoc="limma:malist::MAList">MAList</a></code> or
    <code><a rd-options="snapCGH:SegList" href="/link/SegList?package=snapCGH&version=1.42.0&to=snapCGH%3ASegList" data-mini-rdoc="snapCGH:SegList::SegList">SegList</a></code>

input

Gets passed to the function <code>repeated::hidden</code> as the
    <code>pshape</code> argument.

Gets passed to the function <code>repeated::hidden</code> as
    the <code>iterlim</code> argument. 

maxiter

Choice of which selection criteria should be used in
    the algorithm.  The choices are either AIC or BIC

criteria

Delta value used of the BIC is selected.  If no value is
    entered it defaults to 1.

delta

if true the SegList output includes a probability
    that a clone is in its assigned state and a smoothed value for the
    clone.

full.output

parameter controlling the convergence of the EM
    algorithm. 


  This function fits an unsupervised Hidden Markov model to a given
  <code><a rd-options="limma:malist" href="/link/MAList?package=snapCGH&version=1.42.0&to=limma%3Amalist" data-mini-rdoc="limma:malist::MAList">MAList</a></code> or <code><a rd-options="snapCGH:SegList" href="/link/SegList?package=snapCGH&version=1.42.0&to=snapCGH%3ASegList" data-mini-rdoc="snapCGH:SegList::SegList">SegList</a></code>


models

Methods for segmenting, normalising and processing aCGH
data; including plotting functions for visualising raw and
segmented data for individual and multiple arrays.

John Marioni

snapCGH

Segmentation, normalisation and processing of aCGH data.

runHomHMM function

an object of class <code><a href="MAList" rd-options="limma:malist">MAList</a></code> or
    <code><a href="SegList" rd-options="snapCGH:SegList">SegList</a></code>

Gets passed to the function <code>repeated::hidden</code> as the
    <code>pshape</code> argument.

Gets passed to the function <code>repeated::hidden</code> as
    the <code>iterlim</code> argument.

Choice of which selection criteria should be used in
    the algorithm.  The choices are either AIC or BIC

Delta value used of the BIC is selected.  If no value is
    entered it defaults to 1.

if true the SegList output includes a probability
    that a clone is in its assigned state and a smoothed value for the
    clone.

parameter controlling the convergence of the EM
    algorithm.

This function fits an unsupervised Hidden Markov model to a given
  <code><a href="MAList" rd-options="limma:malist">MAList</a></code> or <code><a href="SegList" rd-options="snapCGH:SegList">SegList</a></code>

runHomHMM: A function to fit unsupervised Hidden Markov model

Description

Usage

Arguments

See Also