states.hmm.func {aCGH} | R Documentation

## A function to fit unsupervised Hidden Markov model

### Description

This function is a workhorse of `find.hmm.states`. It operates on the individual chromosomes/samples and is not called directly by users.

### Usage

    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)

### Arguments

`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.

### See Also

`aCGH`

[Package aCGH version 1.70.0 Index]