ruleOutliers,flowClust-method   package:flowClust   R Documentation

_S_h_o_w_i_n_g _o_r _M_o_d_i_f_y_i_n_g _t_h_e _R_u_l_e _u_s_e_d _t_o _I_d_e_n_t_i_f_y _O_u_t_l_i_e_r_s

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

     This method shows or modifies the rule used to identify outliers.

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

     ## S4 method for signature 'flowClust':
     ruleOutliers(object)
     ruleOutliers(object) <- value

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

  object: Object returned from 'flowClust' or 'filter'.

   value: A list object with one or more of the following named
          elements: 'level', 'u.cutoff' and 'z.cutoff'.  Their
          interpretations are the same as those of the corresponding
          arguments in the 'flowClust' function.  Note that when both
          'level' and 'u.cutoff' are missing, the rule set by the
          original value of 'level' or 'u.cutoff' will be unchanged
          rather than removed.  Likewise, when 'z.cutoff' is missing,
          the rule set by the original value of 'z.cutoff' will be
          retained.

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

     The replacement method modifies 'object@ruleOutliers' (or
     'object[[k]]@ruleOutliers' if 'object' is of class 'flowClustList'
     or 'tmixFilterResultList') AND updates the logical vector
     'object@flagOutliers' (or 'object[[k]]@ruleOutliers') according to
     the new rule.

_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'

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

     res <- flowClust(iris[,1:4], K=3)
     summary(res)
     # change the rule to call outliers
     ruleOutliers(res) <- list(z.cutoff=0.9)
     # more outliers now
     summary(res)
     ruleOutliers(res) <- list(level=0.95, z.cutoff=0)
     # augmented cluster boundaries lead to fewer outliers
     summary(res)

