plotErrorsRepeatedOneLayerCV-methods package:Rmagpie R Documentation

_p_l_o_t_E_r_r_o_r_s_R_e_p_e_a_t_e_d_O_n_e_L_a_y_e_r_C_V  _M_e_t_h_o_d _t_o _p_l_o_t _t_h_e _e_s_t_i_m_a_t_e_d _e_r_r_o_r _r_a_t_e_s _i_n _e_a_c_h _r_e_p_e_a_t _o_f _a _o_n_e-_l_a_y_e_r _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 creates a plot that represent the summary estimated
     error rate and the cross-validated error rate in each repeat of
     the one-layer cross-validation of the assessment at stake. The
     plot represents the summary estimate of the error rate (averaged
     over the repeats) and the cross-validated error rate obtained in
     each repeat versus the size of gene subsets (for SVM-RFE) or the
     threshold values (for NSC).

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


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

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

     'plotErrorsFoldTwoLayerCV-methods',
     'plotErrorsSummaryOneLayerCV-methods'

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

     data('vV70genesDataset')

     expeOfInterest <- new("assessment", dataset=vV70genes,
                                        noFolds1stLayer=3,
                                        noFolds2ndLayer=2,
                                        classifierName="svm",
                                        typeFoldCreation="original",
                                        svmKernel="linear",
                                        noOfRepeat=10,
                                        featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,3,4,5,6)))

     expeOfInterest <- runOneLayerExtCV(expeOfInterest)

     plotErrorsRepeatedOneLayerCV(expeOfInterest)

