miscellaneous           package:flowClust           R Documentation

_V_a_r_i_o_u_s _F_u_n_c_t_i_o_n_s _f_o_r _R_e_t_r_i_e_v_i_n_g _I_n_f_o_r_m_a_t_i_o_n _f_r_o_m _C_l_u_s_t_e_r_i_n_g _R_e_s_u_l_t_s

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

     Various functions are available to retrieve the information
     criteria ('criterion'), the posterior probabilities of clustering
     memberships z ('posterior'), the weights u ('importance'), the
     uncertainty ('uncertainty'), and the estimates of the cluster
     proportions, means and variances ('getEstimates') resulted from
     the clustering (filtering) operation.

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

     criterion(object, ...)
     criterion(object) <- value
     posterior(object, assign=FALSE)
     importance(object, assign=FALSE)
     uncertainty(object)
     getEstimates(object, data)

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

  object: Object returned from 'flowClust' or 'filter'.  For the
          replacement method of 'criterion', the object must be of
          class 'flowClustList' or 'tmixFilterResultList'.

     ...: Further arguments. Currently this is 'type', a character
          string.  May take '"BIC"', '"ICL"' or '"logLike"', to specify
          the criterion desired.

   value: A character string stating the criterion used to choose the
          best model.  May take either '"BIC"' or '"ICL"'.

  assign: A logical value.  If 'TRUE', only the quantity ('z' for
          'posterior' or 'u' for 'importance') associated with the
          cluster to which an observation is assigned will be returned.
           Default is 'FALSE', meaning that the quantities associated
          with all the clusters will be returned.

    data: A numeric vector, matrix, data frame of observations, or
          object of class 'flowFrame'; an optional argument.  This is
          the object on which 'flowClust' or 'filter' was performed.

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

     These functions are written to retrieve various slots contained in
     the object returned from the clustering operation.  'criterion' is
     to retrieve 'object@BIC', 'object@ICL' or 'object@logLike'.  It
     replacement method modifies 'object@index' and 'object@criterion'
     to select the best model according to the desired criterion. 
     'posterior' and 'importance' provide a means to conveniently
     retrieve information stored in 'object@z' and 'object@u'
     respectively.  'uncertainty' is to retrieve 'object@uncertainty'.
     'getEstimates' is to retrieve information stored in 'object@mu'
     (transformed back to the original scale) and 'object@w'; when the
     data object is provided, an approximate variance estimate (on the
     original scale, obtained by performing one M-step of the EM
     algorithm without taking the Box-Cox transformation) will also be
     computed.

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

     Denote by K the number of clusters, N the number of observations,
     and P the number of variables.  For 'posterior' and 'importance',
     a matrix of size N x K is returned if 'assign=FALSE' (default). 
     Otherwise, a vector of size N is outputted.  'uncertainty' always
     outputs a vector of size N.  'getEstimates' returns a list with
     named elements, 'proportions', 'locations' and, if the data object
     is provided, 'dispersion'.  'proportions' is a vector of size P
     and contains the estimates of the K cluster proportions. 
     'locations' is a matrix of size K x P and contains the estimates
     of the K mean vectors transformed back to the original scale
     (i.e., 'rbox(object@mu, object@lambda)').  'dispersion' is an
     array of dimensions K x P x P, containing the approximate
     estimates of the K covariance matrices on the original scale.

_N_o_t_e:

     When 'object@nu=Inf', the Mahalanobis distances instead of the
     weights are stored in 'object@u'.  Hence, 'importance' will
     retrieve information corresponding to the Mahalanobis distances.

_A_u_t_h_o_r(_s):

     Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>

_R_e_f_e_r_e_n_c_e_s:

     Lo, K., Brinkman, R. R. and Gottardo, R. (2008) Automated Gating
     of Flow Cytometry Data via Robust Model-based Clustering.
     _Cytometry A_ *73*, 321-332.

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

     'flowClust', 'filter', 'Map'

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

     res <- flowClust(iris[,1:4], K=3)
     criterion(res)
     posterior(res)
     posterior(res, assign=TRUE)
     importance(res)
     importance(res, assign=TRUE)
     uncertainty(res)
     getEstimates(res)

