matplotProbesPDNN          package:affypdnn          R Documentation

_P_l_o_t _t_h_e _P_D_N_N _c_o_m_p_u_t_e_d _p_r_o_b_e _i_n_t_e_n_s_i_t_i_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Plot the probe intensities as computed by 'pmcorrect.pdnn' or
     'pmcorrect.pdnnpredict'

_U_s_a_g_e:

     matplotProbesPDNN(x, type="l", ...)

_A_r_g_u_m_e_n_t_s:

       x: a matrix (and attributes) as returned by 'pmcorrect.pdnn' or
          'pmcorrect.pdnnpredict'. 

    type: type of plot (same as in 'matplot')

     ...: optional arguments to be passed to 'matplot' 

_D_e_t_a_i_l_s:

     The crosses are the probe intensities which are considered `ok' by
     the outlier detection part of the algorithm, while the circles are
     the ones considered `outliers'

_V_a_l_u_e:

     Only used for its side-effect.

_S_e_e _A_l_s_o:

     'pmcorrect.pdnn' and 'pmcorrect.pdnnpredict'

_E_x_a_m_p_l_e_s:

     # see 'pmcorrect.pdnn'

