ArrayOutliers-methods       package:arrayMvout       R Documentation

_A_r_r_a_y_O_u_t_l_i_e_r_s - _w_r_a_p_p_e_r _f_o_r _p_l_a_t_f_o_r_m-_s_p_e_c_i_f_i_c _m_u_l_t_i_v_a_r_i_a_t_e _o_u_t_l_i_e_r _d_e_t_e_c_t_i_o_n 
_f_o_r _e_x_p_r_e_s_s_i_o_n _a_r_r_a_y_s

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

     wraps functions that perform multivariate outlier detection on
     dimension-reduced QA statistics of expression arrays

_M_e_t_h_o_d_s:



     _d_a_t_a = "_A_N_Y", _a_l_p_h_a = "_m_i_s_s_i_n_g", _a_l_p_h_a_S_e_q = "_m_i_s_s_i_n_g" fails; tells
          user that alpha is obligatory parameter 

     _d_a_t_a = "_A_f_f_y_B_a_t_c_h", _a_l_p_h_a = "_n_u_m_e_r_i_c", _a_l_p_h_a_S_e_q = "_A_N_Y" performs
          calibrated multivariate outlier detection on an AffyBatch
          instance using various affy-specific QA parameters 

     _d_a_t_a = "_L_u_m_i_B_a_t_c_h", _a_l_p_h_a = "_n_u_m_e_r_i_c", _a_l_p_h_a_S_e_q = "_A_N_Y" performs
          calibrated multivariate outlier detection on an LumiBatch
          instance using various illumina-specific QA parameters 

     _d_a_t_a = "_d_a_t_a._f_r_a_m_e", _a_l_p_h_a = "_n_u_m_e_r_i_c", _a_l_p_h_a_S_e_q = "_A_N_Y" performs
          calibrated outlier detection on QA statistics housed in
          data.frame - all columns of the 'data' entity must be numeric
          QA statistics for the arrays.


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

     example(ArrayOutliers)

