runTwoLayerExtCV-methods       package:Rmagpie       R Documentation

_r_u_n_T_w_o_L_a_y_e_r_E_x_t_C_V: _M_e_t_h_o_d _t_o _r_u_n _a_n _e_x_t_e_r_n_a_l _t_w_o-_l_a_y_e_r_s _c_r_o_s_s-_v_a_l_i_d_a_t_i_o_n

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

     This method run an external two-layers cross-validation according
     to the options stored in an object of class assessment. The
     concept of two-layers cross-validation has been introduced by J.X.
     Zhu,G.J. McLachlan, L. Ben-Tovim Jonesa, I.A.Wood in 'On selection
     biases with prediction rules formed from gene expression data' and
     by I. A. Wood, P. M. Visscher, and K. L. Mengersen in
     'Classification based upon gene expression data: bias and
     precision of error rates' (cf. section References). This technique
     of cross-validation is used to determine an unbiased estimate of
     the best error rate (using the best size of subset for RFE-SVM, of
     the best threshold for NSC) when feature selection is involved.

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

  object: 'Object of class assessment'. Object assessment of interest

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

     'object of class assessment' in which the one-layer external
     cross-validation has been computed, therfore, the slot
     'resultRepeated2LayerCV' is no more NULL. This methods print out
     the key results of the assessment, to access the full detail of
     the results, the user must call the method 'getResults'.

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


     _o_b_j_e_c_t = "_a_s_s_e_s_s_m_e_n_t" This method is only applicable on objects of
          class assessment.

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

     J.X. Zhu,  G.J. McLachlan, L. Ben-Tovim, I.A. Wood (2008), "On
     selection biases with prediction rules formed from gene expression
     data", Journal of Statistical Planning and Inference, 38:374-386.

     I.A. Wood, P.M. Visscher, and K.L. Mengersen "Classification based
     upon gene expression data: bias and precision of error rates"
     Bioinformatics, June 1, 2007; 23(11): 1363 - 1370.

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

     'assessment', 'getResults', 'runOneLayerExtCV-methods'

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

     data('vV70genesDataset')

     # assessment with RFE and SVM
     myExpe <- new("assessment", dataset=vV70genes,
                        noFolds1stLayer=9,
                        noFolds2ndLayer=10,
                        classifierName="svm",
                        typeFoldCreation="original",
                        svmKernel="linear",
                        noOfRepeat=2,
                        featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6)))

     myExpe <- runTwoLayerExtCV(myExpe)

