states.hmm.func: A function to fit unsupervised Hidden Markov model
In aCGH: Classes and functions for Array Comparative Genomic Hybridization data

This function is a workhorse of find.hmm.states. It 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`	sample identifier
`chrom`	chromosome identifier
`dat`	dataframe with clones in the rows and samples in the columns
`datainfo`	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
`vr`	Initial experimental variance
`maxiter`	Maximum number of iterations
`aic`	TRUE or FALSE variable indicating whether or nor AIC criterion should be used for model selection (see DETAILS)
`bic`	TRUE or FALSE variable indicating whether or nor BIC criterion should be used for model selection (see DETAILS)
`delta`	numeric vector of penalty factors to use with BIC criterion. If BIC is true, delta=1 is always calculated (see DETAILS)
`nlists`	defaults to 1 when aic=TRUE, otherwise > 1
`eps`	parameter controlling the convergence of the EM algorithm.
`print.info`	print.info = T allows diagnostic information to be printed on the screen.
`diag.prob`	parameter controlling the construction of the initial transition probability matrix.