hmm                package:VanillaICE                R Documentation

_F_i_t_s _t_h_e _h_i_d_d_e_n _M_a_r_k_o_v _m_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Fits the hidden Markov model

_U_s_a_g_e:

     hmm(object, params)

_A_r_g_u_m_e_n_t_s:

  object: An object of class 'HmmOptions'

  params: An object of class 'HmmParameter'

_D_e_t_a_i_l_s:

     None yet.

_V_a_l_u_e:

     An object of class 'HmmPredict'

_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

_S_e_e _A_l_s_o:

     'HmmParameter-class' 'HmmPredict-class' 'SnpLevelSet-class'

_E_x_a_m_p_l_e_s:

     data(chromosome1)
     chromosome1 <- chromosome1[1:500, ]
     options <- new("HmmOptions",
                    states=c("D", "N", "L", "A"),
                    snpset=chromosome1,
                    copyNumber.location=c(1, 2, 2, 3),
                    probHomCall=c(0.99, 0.7, 0.99, 0.7))
     validObject(options)
     params <- new("HmmParameter", 
                   states=states(options),
                   initialStateProbability=0.99)
     cn.emission <- copyNumber.emission(options)
     gt.emission <- calls.emission(options)
     emission(params) <- cn.emission + gt.emission ##log scale
     genomicDistance(params) <- exp(-2 *calculateDistance(options)/(100*1e6))
     ##no scaling
     transitionScale(params) <- matrix(1, length(states(options)), length(states(options)))
     if(validObject(params)) fit <- hmm(options, params)

