postInteraction          package:ArrayTools          R Documentation

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

     Based on the result from the interaction test by looking at the
     result from the regressResult object, this function partitions tne
     orignal data, an  ExpressionSetinto groups, one contains the genes
     without the interaction and others contains the genes with the
     interaction across different level of covariates.

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

     postInteraction(eSet, regressObject, mainVar, compare1, compare2, method = regressionMethod(regressObject), adj = adjustment(regressObject))

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

    eSet: an ExpressionSet

regressObject: a regressResult 

 mainVar: variable of main interest

compare1: the first value of the 'mainVar'. For example, suppose that 
          'mainVar' is "drug", and there are three unique values:
          "drug1",  "drug2", and "placebo". You would like to compare
          "drug1" to "drug2".  Then you would use "drug1" as 'compare1'

compare2: Based on the example for 'compare1', "drug2" will be the
          'compare2'

  method: It is used to run regression within each level of the effect
          modifier. choose the follwoing three options: "limma"
          (LIMMA),  "regression" (ordinary linear regression),
          "permutation" (permutation test)

     adj: adjustment method for multiple comparison test, including
          "holm",  "hochberg", "hommel", "bonferroni", "BH", "BY",
          "fdr", "none". The default  value is "none". Type
          help(p.adjust) for more detail.

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

     an 'interactionResult' class.  The first component contains all
     the result for all the genes.  The second component contains the
     genes without the interaction effect.  The rest of the components
     contains genes with the interactions.

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

     Xiwei Wu, Arthur Li

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

     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")

