hmm.setup {tileHMM}R Documentation

Create HMM from Initial Parameter Estimates Obtained from Data

Description

Convenient way to obtain initial parameter estimates from data.

Usage

hmm.setup(data, state = c("enriched", "non-enriched"), 
    probe.region = 35, frag.size = 1000, pos.state = 1, 
    em.type = "tDist", max.prob = 1, df = 9)

Arguments

data Observation sequence. This can be either a single sequence or a list of sequences.
state Vector of state names for HMM.
probe.region Length of genomic region represented by one probe (on average).
frag.size Expected size of ChIP fragments.
pos.state Index of state which is considered to represent ‘positive’ result.
em.type Character string identifying type of emission distribution to be used. Currently only "tDist" is supported.
max.prob Maximum probability allowed in transition matrix. Setting this to less than 1 ensures that there are no null transitions.
df Degrees of freedom for emission distributions.

Details

The parameter estimates are obtained by first clustering the observations, then the mean and variance of the resulting groups are used together with cluster size, expected fragment size and probe density to generate initial values for model parameters.

The parameter values generated by this procedure are only a rough guess and have to be optimised before the model is used for data analysis.

Value

Object of class contHMM.

Note

This method currently only supports two state HMMs with t distributions.

Author(s)

Peter Humburg

See Also

contHMM, getHMM, tDist, viterbiEM

Examples

## create two state HMM with t distributions
state.names <- c("one","two")
transition <- c(0.035, 0.01)
location <- c(-1, 2)
scale <- c(1, 1)
df <- c(4, 6)
hmm <- getHMM(list(a=transition, mu=location, sigma=scale, nu=df), 
    state.names)

## obtain observation sequence from model
obs <- sampleSeq(hmm, 500)

## build model from data
model <- hmm.setup(obs, state = c("one", "two"),df=5)

[Package tileHMM version 1.0-2 Index]