interactionResult-class      package:ArrayTools      R Documentation

_C_l_a_s_s _t_o _C_o_n_t_a_i_n _t_h_e _R_e_g_r_e_s_s_i_o_n _R_e_s_u_l_t _B_a_s_e_d _o_n _A_n _I_n_t_e_r_a_c_t_i_o_n _M_o_d_e_l

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

     Class to Contain the Regression Result Based on An Interaction
     Model. Interaction is a statistical term refering to a situation
     when the relationship between the outcome and the variable of the
     main interest differs at different levels of the extraneous
     variable

_C_r_e_a_t_i_n_g _O_b_j_e_c_t_s:

     'interactionResult' object is generally created from the
     'postInteraction' function See 'postInteraction'

_O_b_j_e_c_t _C_o_m_p_o_n_e_n_t_s:

     A list of four or more components.  Each component is a
     reggressResult class. The first component contains all the genes.
     The second component contains genes with the interaction effect
     The rest components contains genes with the interaction effect
     across different levels.  Each component contains the result for
     each level.

_E_x_t_e_n_d_s:

     Class '"list"', from data part. Class '"vector"', by class "list",
     distance 2.

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


     _a_d_j_u_s_t_m_e_n_t 'signature(object = "regressResult")'{:  access the
          'adjustment' slot }

     _g_e_t_A_d_j_P 'signature(object = "regressResult")'{:   access the
          'adjPVal' slot }

     _g_e_t_A_n_n_o_t_a_t_i_o_n 'signature(object = "regressResult")'{:   access the
          'annotation' slot }

     _g_e_t_C_o_n_t_r_a_s_t 'signature(object = "regressResult")'{:   access the
          'contrast' slot }

     _g_e_t_F 'signature(object = "regressResult")'{:   access the 'FValue'
          slot}

     _g_e_t_F_C 'signature(object = "regressResult")'{:   access the
          'foldChange' slot }

     _g_e_t_F_C_C_u_t_o_f_f 'signature(object = "regressResult")'{:   access the
          'significantFCCutoff' slot}

     _g_e_t_F_i_l_e_N_a_m_e 'signature(object = "regressResult")'{:   access the
          'fileName' slot }

     _g_e_t_F_i_l_t_e_r_M_e_t_h_o_d 'signature(object = "regressResult")'{:   access
          the 'filterMethod' slot }

     _g_e_t_I_D 'signature(object = "regressResult")'{:   access the 'ID'
          slot  }

     _g_e_t_I_n_d_e_x 'signature(object = "regressResult")'{:   access the
          'significantIndex' slot}

     _g_e_t_N_o_r_m_a_l_i_z_a_t_i_o_n_M_e_t_h_o_d 'signature(object = "regressResult")'{:  
          access the 'normalizationMethod' slot}

     _g_e_t_P 'signature(object = "regressResult")'{:   access the 'pValue'
          slot }

     _g_e_t_P_C_u_t_o_f_f 'signature(object = "regressResult")'{:   access the
          'significantPvalueCutoff' slot }

     _O_u_t_p_u_t_2_H_T_M_L 'signature(object = "regressResult")'{:   create HTML
          file for sigificant genes in regressionResult}

     _r_e_g_r_e_s_s_i_o_n_M_e_t_h_o_d 'signature(object = "regressResult")'{:   access
          the 'regressionMethod' slot}

     _s_e_l_e_c_t_S_i_g_G_e_n_e 'signature(object = "regressResult")'{:   select
          significant genes for 'regressionResult' class}

     _s_h_o_w 'signature(object = "regressResult")'{:   print
          'regressResult'}

     _S_o_r_t 'signature(x = "regressResult")'{:   sort 'regressResult'}

     _s_u_m_m_a_r_y 'signature(object = "regressResult")'{:   print the
          summary for 'regressResult'}

     _g_e_t_L_e_n_g_t_h 'signature(object = "interactionResult")'{:   calculate
          the length of the interactionResult class}

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

     Xiwei Wu, Arthur Li

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

     'regressResult'

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

     ## Creating the interactionREsult takes a few steps:
     data(eSetExample)
     design.int<- new("designMatrix", target=pData(eSetExample), covariates = c("Treatment", "Group"),
         intIndex = c(1, 2))
     contrast.int<- new("contrastMatrix", design.matrix = design.int, interaction=TRUE)
     result.int<- regress(eSetExample, contrast.int)
     sigResult.int <- selectSigGene(result.int)
     intResult <- postInteraction(eSetExample, sigResult.int, mainVar ="Treatment",
        compare1 = "Treated", compare2 = "Control")

