hmm                package:VanillaICE                R Documentation

_W_r_a_p_p_e_r _f_o_r _f_i_t_t_i_n_g _t_h_e _H_M_M

_D_e_s_c_r_i_p_t_i_o_n:

     A wrapper for fitting the HMM.

_U_s_a_g_e:

     hmm(object, states, mu = NULL, probs = NULL, takeLog = FALSE, initialP, returnSegments = TRUE, TAUP = 1e+08, verbose = FALSE, ice = FALSE, envir)

_A_r_g_u_m_e_n_t_s:

  object: SnpCallSet, SnpCopyNumberSet, or oligoSnpSet object

  states: Labels for the hidden states.  See details for order.

      mu: The latent copy number. See details for order. 

   probs: See details.

 takeLog: Whether to take the log of the copy number before computing
          emission probabilities and standard deviations

initialP: Initial state probabilities

returnSegments: Logical: whether to return the segments or the loci x
          sample matrix of predicted states

    TAUP: Scaling parameter for transition probabilities.

 verbose: Logical: Verbose output?

     ice: Whether to use CRLMM confidence scores of the genotype calls.

   envir: Optional.  An environment for storing intermediate files
          created for fitting the HMM.

_D_e_t_a_i_l_s:

     For oligoSnpSet objects, the hidden state labels are assumed to be
     1: hemizygous deletion 2: normal 3: region of homozygosity (ROH)
     4: amplification

     The argument mu should have copy number values corresponding to
     the above states.  For instance on the absolute scale, the copy
     number states should be 1, 2, 2, and 4.

     'probs':  If 'ice' is FALSE, the elements in probs should
     correspond to the probability of a homozygous genotype in each of
     the above states.  If 'ice' is TRUE, the elements in probs should
     correspond to 1.  Pr(homozygous call | truth is heterozyous)   2. 
     Pr(heterozygous call | truth is heterozygous) 3.  Pr(homozygous
     call | truth is ROH) 4.  Pr(homozygous call | truth is normal) . 
     'Normal' meaning copy number 2 and a typical frequency of
     heterozygosity for autosomes.

_V_a_l_u_e:

     If 'returnSegments' is TRUE, a data.frame containing the
     coordinates of the predicted segments is returned.  Otherwise, a
     loci X sample matrix is returned. The elements of the matrix
     correspond to the predict hidden state for a specific locus and
     sample.

_A_u_t_h_o_r(_s):

     R. Scharpf

_R_e_f_e_r_e_n_c_e_s:

     RB Scharpf et al. (2008) Hidden Markov Models for the assessment
     of chromosomal alterations using high-throughput SNP arrays,
     Annals of Applied Statistics

