stamCV-class              package:stam              R Documentation

_C_r_o_s_s _V_a_l_i_d_a_t_i_o_n _I_n_f_o_r_m_a_t_i_o_n _G_e_n_e_r_a_t_e_d _b_y _S_t_A_M

_D_e_s_c_r_i_p_t_i_o_n:

     Objects if this class are generated by 'stam.cv'. It contains
     results of cross  validated model fits generated in structured
     analysis of microarrays in order to choose graph shrinkage levels.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form  'new("stamCV", exprs,
     classifications, beta, chip, root)', but it is recommended  the
     use the function 'stam.cv'.

_S_l_o_t_s:


     '_s_a_m_p_l_e._l_a_b_e_l_s': Object of class '"character"', names of samples

     '_s_a_m_p_l_e._c_l_a_s_s_e_s': Object of class '"character"', class names for 
          each sample

     '_c_l_a_s_s._l_a_b_e_l_s': Object of class '"character"', one name for each 
          class

     '_p_r_i_o_r': Object of class '"numeric"', prior class probabilities 
          according to prevalence

     '_b_e_t_a': Object of class '"numeric"', class weights, one per class

     '_f_u_l_l._p_a_m_f_i_t': Object of class '"nsc"', PAM fit on all probesets

     '_p_r_o_b_s': Object of class '"array"', matrix of cross validated 
          prediction probabilities [samples x classes x nodes]

     '_f_o_l_d_s': Object of class '"list"', buckets used in cross
          validation

     '_r_e_s_u_l_t_s': Object of class '"data.frame"', cross-validated root 
          error rate, root performance and mean redundancy as well as
          remaining nodes and  the accessible probesets for each delta

     '_n_o_d_e._r_e_s_u_l_t_s': Object of class '"list"', performance, redundancy,
           sensitivity and specificity per node

     '_m_a_x._l_e_a_f_d_e_v': Object of class '"numeric"', performance of worst 
          leaf node

     '_d_e_l_t_a_s': Object of class '"numeric"', shrinkage candidates

     See 'stamNet-class' for slots 'chip', 'root', 'chippkg',  'GOpkg',
     'nodes', 'leafs', 'inodes' and 'probes'.

_E_x_t_e_n_d_s:

     Class '"stamNet"', directly.

_M_e_t_h_o_d_s:


     _p_r_i_n_t 'signature(x = "stamCV")': print information on cross
          validation

     _w_r_i_t_e_H_T_M_L 'signature(x = "stamCV")': generate HTML information on
          cross  validation. However, using 'stam.writeHTML' is
          recommended.

_A_u_t_h_o_r(_s):

     Claudio Lottaz

_S_e_e _A_l_s_o:

     'stam.cv', 'stamNet-class'

