fit.model              package:snapCGH              R Documentation

_F_i_t_t_i_n_g _a _h_e_t_e_r_o_g_n_e_o_u_s _H_M_M _t_o _t_h_e _l_o_g_2 _r_a_t_i_o_s _o_n _a _p_a_r_t_i_c_u_l_a_r _c_h_r_o_m_o_s_o_m_e.

_D_e_s_c_r_i_p_t_i_o_n:

     This function fits five homogeneous HMMs to the log2 ratios on a
     particular chromosome. It then uses either the AIC or BIC to
     determine which of the five models is optimal before using a
     scaled version of the Viterbi algorithm to assign clones to states
     with the same underlying copy number.

_U_s_a_g_e:

     fit.model(sample, chrom, dat, datainfo = clones.info, useCloneDists = TRUE, covariates,
     aic = TRUE, bic = FALSE, delta = 1, var.fixed=FALSE, epsilon = 1e-06,
     numiter = 30000)

_A_r_g_u_m_e_n_t_s:

  sample: If there are multiple samples, the number of the sample to be
          segmented 

   chrom: The chromosome on which the segmentation is to be carried out
          on 

     dat: The log2 ratios obtained from the clones located on that
          chromosome 

datainfo: A dataframe containing information about the clones on that
          chromosome (name, chromosome and location (in Mbs)) 

useCloneDists: Boolean stating whether the distance between clones
          should be incorportated into the HMM.  If false then the HMM
          become homogeneous.

covariates: A matrix contianing the covariate information for the
          clones located on the chromosome to be segmented. It should
          have length one less than the number of clones as covariate
          information is not used when segmenting the first clone on
          the chromosome. 

     aic: Set to true if you want to use the aic. This is the default. 
          Only one of aic and bic should be set to true.

     bic: Set to true if you want to use the bic.

   delta: A parameter to be set if you want to use the BIC 

var.fixed: Logical variable - TRUE if you want to tie the variance to
          be the same across all states. Defaults to FALSE 

 epsilon: .

 numiter: Number of iterations to be used in the optimization alogithm.

_V_a_l_u_e:

     The output is in the same format as that obtained when the nlm
     function is applied.

_A_u_t_h_o_r(_s):

     John Marioni and Mike Smith

