stability.sim            package:Rtreemix            R Documentation

_S_t_a_b_i_l_i_t_y _a_n_a_l_y_s_i_s _o_f _t_h_e _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

_D_e_s_c_r_i_p_t_i_o_n:

     The function includes stability analysis on different levels of
     the mutagenetic trees mixture model: GPS values, encoded
     probability distribution, tree topologies. Each analysis contains
     the values of different similarity measures with their
     corresponding p-values.

_U_s_a_g_e:

     stability.sim(no.trees = 3, no.events = 9, prob = c(0.2, 0.8),
                   no.draws = 300, no.rands = 100, no.sim = 1)

_A_r_g_u_m_e_n_t_s:

no.trees: An 'integer' larger than 2 giving the number of tree
          components of the mixture models considered in the stability
          analysis. The default value is 3.

no.events: An 'integer' larger than 0 giving the number of genetic
          events of the mixture models considered in the stability
          analysis.

    prob: A 'numeric' vector of length 2 specifying the boundaries for
          the edge weights of the randomly generated trees. The first
          component of the vector (the lower boundary) must be smaller
          than the second component (the upper boundary). The default
          value is (0.2, 0.8).

no.draws: An 'integer' larger than 0 giving the size of the data sample
          drawn from the random models used for learning the mixture
          models. The default value is 300.

no.rands: An 'integer' larger than 0 specifying the number of random
          models used for calculating the p-values. The default value
          is 100.

  no.sim: An 'integer' larger than 0 specifying the number of
          iterations used for the waiting time simulations (a part of
          the GPS calculation). The default value is 1.

_D_e_t_a_i_l_s:

     The stability analysis is performed by first drawing a true
     mixture model uniformly at random from the model space, and
     drawing a data sample from it. Afterwards, a mutagenetic trees
     model is fitted to the drawn sample. The quality of the features
     derived from the model is then assessed by comparing its quality
     with the quality of the corresponding features of a sufficient
     number of random mixture models sampled uniformly from the model
     space. A p-value is obtained as a percentage of cases in which the
     true model is closer to a random model tnah to the fitted model.

_V_a_l_u_e:

  comp1 : Results from the stability analysis of the GPS values derived
          from a fitted mixture model. A 'matrix' with 4 columns and
          'no.sim' rows. The first two columns give the similarity
          values and their corresponding p-values when the Euclidian
          distance is used as a similarity measure for comparing the
          respective GPS vectors. The last two columns depict the same
          results, but with the rank correlation distance used as a
          similarity measure. 

  comp2 : Results from the stability analysis of the probability
          distributions induced by a fitted mixture model. A 'matrix'
          with 6 columns and 'no.sim' rows. Each two columns give the
          values of the comparissons between the true and the fitted
          probability distributions and their corresponding p-values,
          when using the cosine distance, the L1 distance, and the
          Kullback-Leibler divergence as similarity measures.

  comp3 : Results from the stability analysis of the topologies of the
          tree components of a fitted mixture model. A 'matrix' with 2
          columns and 'no.sim' rows that give the value of the
          comparisson of the topologies between the true and the
          corresponding fitted model and their p-values. The similarity
          measure underlying the number of different edges was used.

  comp4 : Similar to 'comp3'. However, the similarity measure for
          comparing the tree topologies besides the number of distinct
          edges includes the L1 distances of the level vectors of
          events. See 'get.tree.levels'.

  comp5 : A 'matrix' where the columns correspond to the true GPS
          vector from each simulation iteration. The matrix has
          'no.sim' columns and 'no.draws' rows.

  comp6 : Same as 'comp5', but the matrix contains the fitted GPS
          values from each simulation iteration.

  comp7 : A 'list' where each component corresponds to the true models
          generated in each simulation iteration. the length of the
          list is 'no.sim'.

  comp8 : Same as 'comp7', but the list contains the fitted models.

_N_o_t_e:

     The stability simulation 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.; Estimating cancer survival and clinical
     outcome based on genetic tumor progression scores, J.
     Rahnenf\"urer et al.

_S_e_e _A_l_s_o:

     'RtreemixData-class', 'RtreemixModel-class', 'RtreemixGPS-class',
     'RtreemixStats-class', 'fit-methods', 'gps-methods',
     'distribution-methods', 'generate-methods', 'sim-methods',
     'L1.dist', 'Pval.dist', 'comp.models', 'comp.trees',
     'get.tree.levels', 'kullback.leibler'

_E_x_a_m_p_l_e_s:

     ## Stability analysis - a toy example
     #stability.sim(no.trees = 3, no.rands = 5, no.sim = 4, no.draws = 300)

