evaluateClustering           package:GOSim           R Documentation

_E_v_a_l_u_a_t_e _a _g_i_v_e_n _g_r_o_u_p_i_n_g _o_f _g_e_n_e_s _o_r _G_O _t_e_r_m_s.

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

     Evaluate a given grouping of genes or terms with respect to their
     GO similarity.

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

     evaluateClustering(clust, Sim)

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

   clust: grouping = vector of integer or character 

     Sim: similarity matrix 

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

     If necessary, more details than the description above

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

     List with two items: 

clusterstats : matrix (ncluster x 2) of median within cluster
          similarities and median absolute deviations

clustersil: cluster silhouette values

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

     Holger Froehlich

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

     Rousseeuw, P., Silhouettes: a graphical aid to the interpretation
     and validation of cluster analysis, J. Comp. and Applied
     Mathematics, 1987, 20, 53-6

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

     'getGeneSimPrototypes', 'getGeneSim', 'getTermSim'

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

             ## Not run: 
             setOntology("BP")
             genes = c("207","208","596","901","780","3169","9518","2852","26353","8614","7494")
             sim = getGeneSim(genes,verbose=FALSE)
             ev = evaluateClustering(c(2,3,2,3,1,2,1,1,3,1,2), sim)
             print(ev$clusterstats)
             plot(ev$clustersil,main="")
             
     ## End(Not run)

