bootstrap-methods          package:Rtreemix          R Documentation

_M_e_t_h_o_d _f_o_r _f_i_t_t_i_n_g _a _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 _a_n_d _a_n_a_l_y_z_i_n_g _i_t_s _v_a_r_i_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     This method fits an 'RtreemixModel' to a given dataset and then
     analyzes its variance with the bootstrap method. The 'data' and
     the number of trees 'K' have to be specified.

_U_s_a_g_e:

     ## S4 method for signature 'RtreemixData, numeric':
     bootstrap(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. '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'. '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. 'B' is an 'integer' larger than 0 specifying the
          number of bootstrap samples. Its default value is 1000.
          'conf.interval' is a 'numeric' specifying the Confidence
          level for the intervals. Its default value is 0.05. 

_V_a_l_u_e:

     The function returns an object from the class 'RtreemixModel'.
     This is the mixture model learned on the given 'data'. Besides the
     edge weights it also contains their confidence intervals resulting
     from the bootstrap analysis. Confidence intervals for the mixture
     parameters are also comupted and available.

_N_o_t_e:

     The bootstrap examples are time consuming. They are commented out
     because of the time restrictions of the check of the package. For
     trying out the code please copy it and uncomment it.

_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', 'fit-methods'

_E_x_a_m_p_l_e_s:

     ## Create an RtreemixData object from a randomly generated RtreemixModel object.
     #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8))
     #data <- sim(model = rand.mod, no.draws = 300)

     ## Create a RtreemixModel and analyze its variance with the bootstrap method.
     #mod.boot <- bootstrap(data = data, K = 2, equal.edgeweights = TRUE, B = 10) ## time consuming computation

     ## See the confidence intervals for the mixture parameters (the weights).
     #WeightsCI(mod.boot)
     ## See the confidence intervals of the conditional probabilities assigned to the edges.
     #edgeData(getTree(mod.boot, 2), attr = "ci")

