fom                 package:maanova                 R Documentation

_F_i_g_u_r_e _o_f _M_e_r_i_t

_D_e_s_c_r_i_p_t_i_o_n:

     K-means clustering needs a given number of groups, which is
     difficult to guess in most of the cases. This function calculates
     the Figure of Merit values for different number of groups and
     generates the FOM plot (FOM value versus number of groups). Lower
     FOM value means better grouping. User can decide the number of
     groups in kmeans cluster based on that result.

_U_s_a_g_e:

     fom(anovaobj, idx.gene, term, ngroups)

_A_r_g_u_m_e_n_t_s:

anovaobj: An object of class 'maanova'.

idx.gene: The index of genes to be clustered.

    term: The factor (in formula) used in clustering. The expression
          level for this term will be used in clustering. This term has
          to  correspond to the gene list, e.g, idx.gene in this
          function. The gene list should be the significant hits in
          testing this term.

 ngroups: The number of groups for K-means cluster. This could be a
          vector or an integer.

_V_a_l_u_e:

     A vector of FOM values for the given number of groups

_A_u_t_h_o_r(_s):

     Hao Wu

_R_e_f_e_r_e_n_c_e_s:

     Yeung, K.Y., D.R. Haynor, and W.L.Ruzzo (2001). Validating
     clustering for gene expression data. _Bioinformatics_,
     *17:309-318*.

_S_e_e _A_l_s_o:

     'macluster', 'consensus', 'kmeans'

_E_x_a_m_p_l_e_s:

     # load in data
     data(abf1)
     # fit the anova model
     ## Not run: 
     fit.fix = fitmaanova(abf1,formula = ~Strain)
     # test Strain effect 
     test.fix = matest(abf1, fit.fix, term="Strain",n.perm= 1000)
     # pick significant genes - pick the genes selected by Fs test
     idx <- volcano(test.fix)$idx.Fs
     # generate FOM
     m <- fom(fit.fix, idx, "Strain", 10)
     ## End(Not run)

