koplsPlotModelDiagnostics       package:kopls       R Documentation

_O_v_e_r_v_i_e_w _o_f _m_o_d_e_l _t_r_a_i_n_i_n_g _r_e_s_u_l_t_s

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

     Produces overview plots of model training results returned from
     the 'koplsModel' function.

_U_s_a_g_e:

     koplsPlotModelDiagnostics(model, plot.values = FALSE)

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

   model: The model training result (see 'koplsModel' ).

plot.values: If TRUE, the exact values will be displayed on the bars as
          text labels. 

_D_e_t_a_i_l_s:

     Produces 2x2 panels of bar plots, containing the total explained
     variation (R2X), the Y-orthogonal explained variation (R2XO), the
     Y-correlated explained variation (R2XC) and (if available) the
     predicted variation from cross-validation (Q2Y).

_A_u_t_h_o_r(_s):

     Max Bylesjo and Mattias Rantalainen

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

     Rantalainen M, Bylesjo M, Cloarec O, Nicholson JK, Holmes E and
     Trygg J. *Kernel-based orthogonal projections to latent structures
     (K-OPLS)*, _J Chemometrics_ 2007; 21:376-385.
     doi:10.1002/cem.1071.

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

     ## Load data set
     data(koplsExample)

     ## Define kernel function parameter
     sigma<-25

     ## Define number of Y-orthogonal components
     nox<-3

     ## Construct kernel
     Ktr<-koplsKernel(Xtr,NULL,'g',sigma)

     ## Model 
     model<-koplsModel(Ktr,Ytr,1,nox,'mc','mc');

     ## Visualize results
     koplsPlotModelDiagnostics(model)
     title("Model diagnostics without cross-validation")

