fit-methods             package:Rtreemix             R Documentation

_M_e_t_h_o_d _f_o_r _f_i_t_t_i_n_g _m_u_t_a_g_e_n_e_t_i_c _t_r_e_e_s _m_i_x_t_u_r_e _m_o_d_e_l _t_o _a _g_i_v_e_n _d_a_t_a_s_e_t

_D_e_s_c_r_i_p_t_i_o_n:

     Function for fitting a mutagenetic trees mixture model to a given
     dataset 'data'. The dataset and the number of trees 'K' have to be
     specified. The function estimates K-oncogenetic trees mixture
     model from the specified data by using an EM-like learning
     algorithm. The first tree component of the model has a star
     topology and is referred to as the noise component.

_U_s_a_g_e:

     ## S4 method for signature 'RtreemixData, numeric':
     fit(data, K, ...)

_A_r_g_u_m_e_n_t_s:

    data: An 'RtreemixData' object giving the dataset used for learning
          the trees mixture model.

       K: An 'integer' larger than 0 specifying the number of
          branchings in the mixture model.

     ...: 'no.start.sol' is an 'integer' larger than 0 specifying the
          number of starting solutions for the k-means algorithm. The
          default value is 100. 'eps' is a 'numeric' giving the minimum
          conditional probability to include edge. The default value is
          0.01. 'weighing' is a 'logical' specifying whether to use
          special weights log(Pr(v)) for the edges (root, v). The
          default value is 'FALSE'. 'equal.edgeweights' is a 'logical'
          specifying whether to use equal edge weights in the noise
          component. The default value is 'TRUE'. When you have few
          data samples always use its default value ('TRUE')  to ensure
          nonzero probabilities for all possible patterns (sets of
          events). 'seed' is a positive 'integer' specifying the random
          generator seed. The default value is (-1) and then the time
          is used as a random generator. 'noise' is a 'logical'
          indicating the presence of a noise (star) component in the
          fitted mixture model. It is mostly relevant for models with a
          single tree component, since it is assumed that mixture
          models with at least two components always have the noise as
          a first component. 

_D_e_t_a_i_l_s:

     When K = 1 and noise = FALSE a single mutagenetic tree is fit to
     the data. When K = 1 and noise = TRUE a star mutagenetic tree is
     fit to the data. If K > 1 the first mutagenetic tree is always the
     star, i.e. the case K > 1 and noise = FALSE is not possible.

_V_a_l_u_e:

     The method returns an 'RtreemixModel' object that represents the
     K-trees mixture model learned from the given dataset.

_N_o_t_e:

     When you have too few data samples always use the default value
     'TRUE'  for the 'equal.edgeweights'. Like this you make sure that
     all possible  patterns (sets of events) have non-zero
     probabilities. If they don't the  fitting procedure will not be
     completed and you will get an error!

_A_u_t_h_o_r(_s):

     Jasmina Bogojeska

_R_e_f_e_r_e_n_c_e_s:

     Learning multiple evolutionary pathways from cross-sectional data,
     N. Beerenwinkel et al.

_S_e_e _A_l_s_o:

     'RtreemixData-class', 'RtreemixModel-class', 'generate-methods',
     'bootstrap-methods', 'confIntGPS-methods'

_E_x_a_m_p_l_e_s:

     ## Create an RtreemixData object from a randomly generated RtreemixModel object.
     rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8))
     data <- sim(model = rand.mod, no.draws = 300)
     show(data)

     ## Create an RtreemixModel object by fitting model to the given data.
     mod <- fit(data = data, K = 3, equal.edgeweights = TRUE, noise = TRUE)
     show(mod)
     ## See the number of tree components in the mixture model.
     numTrees(mod)
     ## See the weights of the branchings from the fitted mixture model.
     Weights(mod)
     ## See a specific tree component k.
     getTree(object = mod, k = 2)

