A function to fit unsupervised Hidden Markov model
This function is a workhorse of
operates on the individual chromosomes/samples and is not called
directly by users.
states.hmm.func(sample, chrom, dat, datainfo = clones.info, vr = 0.01, maxiter = 100, aic = FALSE, bic = TRUE, delta = 1, nlists = 1, eps = .01, print.info = FALSE, diag.prob = .99)
- sample identifier
- chromosome identifier
- dataframe with clones in the rows and samples in the columns
- dataframe containing the clones information that is used to map each clone of the array to a position on the genome. Has to contain columns with names Clone/Chrom/kb containing clone names, chromosomal assignment and kb positions respectively
- Initial experimental variance
- Maximum number of iterations
- TRUE or FALSE variable indicating whether or nor AIC criterion should be used for model selection (see DETAILS)
- TRUE or FALSE variable indicating whether or nor BIC criterion should be used for model selection (see DETAILS)
- numeric vector of penalty factors to use with BIC criterion. If BIC is true, delta=1 is always calculated (see DETAILS)
- defaults to 1 when aic=TRUE, otherwise > 1
- parameter controlling the convergence of the EM algorithm.
- print.info = T allows diagnostic information to be printed on the screen.
- parameter controlling the construction of the initial transition probability matrix.
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