stableEM               package:mixPHM               R Documentation

_S_t_a_b_l_e _E_M _s_o_l_u_t_i_o_n

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

     This function performs the clustering for different EM starting
     values in order to find a stable solution.

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

     stableEM(x, K, numEMstart = 5, method = "separate", Sdist = "weibull", 
     EMoption = "classification", EMstop = 0.0001, maxiter = 1000, print.likvec = TRUE)

_A_r_g_u_m_e_n_t_s:

       x: Data frame or matrix of dimension n*p with survival times
          ('NA''s allowed).

       K: Number of mixture components.

numEMstart: Number of different starting solutions

  method: Imposing proportionality restrictions on the hazards:  With
          'separate' no restrictions are imposed, 'main.g' relates to a
          group main effect,  'main.p' to the variables main effects.
          'main.gp' reflects the proportionality assumption over groups
          and variables. 'int.gp' allows for interactions between
          groups and variables.

   Sdist: Various survival distrubtions such as 'weibull',
          'exponential', and 'rayleigh'.

EMoption: 'classification' is based on deterministic cluster
          assignment,  'maximization' on deterministic assignment, and
          'randomization'  provides a posterior-based randomized
          cluster assignement.

  EMstop: Stopping criterion for EM-iteration.

 maxiter: Maximum number of iterations.

print.likvec: If 'TRUE' the likelihood values for different starting
          solutions are printed.

_D_e_t_a_i_l_s:

     After the computation of the models for different starting
     solutions using the function 'phmclust' the best model is chosen,
     i.e., the model with the largest likelihood value.  The output
     values refer to this final model.

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

     Returns an object of class 'mws' with the following values: 

       K: Number of components

    iter: Number of EM iterations

  method: Method with propotionality restrictions used for estimation

   Sdist: Assumed survival distribution

likelihood: Log-likelihood value for each iteration

  pvisit: Matrix of prior probabilities due to 'NA structure'

   shape: Matrix with shape parameters

   scale: Matrix with scale parameters

   group: Final deterministic cluster assignment

posteriors: Final probabilistic cluster assignment

    npar: Number of estimated parameters

     aic: Akaike information criterion

     bic: Bayes information criterion

  clmean: Matrix with cluster means

   clmed: Matrix with cluster medians

_S_e_e _A_l_s_o:

     'phmclust','msBIC'

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

     ## Exponental mixture model with 2 components for 4 different starting solutions
     data(webshop)
     res <- stableEM(webshop, K = 2, numEMstart = 4, Sdist = "exponential")
     res
     summary(res)

