fbinary-methods            package:msbase            R Documentation

_M_e_t_h_o_d_s _f_o_r _F_u_n_c_t_i_o_n "_f_b_i_n_a_r_y" _i_n _P_a_c_k_a_g_e '_m_s_b_a_s_e'

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

     Binary measures are comptued on entries of a contingency table.

     Implements the:
      Relative Mutual information,
      Fowlkes Mallows statistics M_11/sqrt(M_1Y*M_1X),
      the Gower coefficient
     (M_01^XY+M_10^XY)/(M_01^XY+M_10^XY+M_11^XY),
      and Huberts Gamma

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

     obx: see above

     oby: see above

   error: measurement error

     ppm: if 'TRUE' then error in parts per million, in arbitrary units
          otherwise

  weight: should mass accuracy be weighted

    uniq: if 'TRUE' compute optimal aligmnent

  method: type of dissimilarity: c("rmi","fm","hg","gower")

   range: experimental

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

     _o_b_x = "_n_u_m_e_r_i_c", _o_b_y = "_n_u_m_e_r_i_c" expects two numeric vectors as
          parameters

     _o_b_x = "_M_a_s_s_v_e_c_t_o_r", _o_b_y = "_M_a_s_s_v_e_c_t_o_r" expects two massvectors as
          parameters 'Massvector-class'

     _o_b_x = "_M_a_s_s_v_e_c_t_o_r_l_i_s_t", _o_b_y = "_M_a_s_s_v_e_c_t_o_r" returns a vector with
          the all pairwise dissimilaritities between the entries in obx
          and the Massvector oby

     _o_b_x = "_M_a_s_s_v_e_c_t_o_r_l_i_s_t", _o_b_y = "_N_U_L_L" computes a dissimilarity
          matrix

     _o_b_x = "_l_i_s_t", _o_b_y = "_N_U_L_L" computes a dissimilarity matrix

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

     Witold E. Wolski witek96@users.sourceforge.net <URL:
     http://r4proteomics.sourceforge.net>

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

     # resolve multiple matches.
     data(pldata)
     pl1 <- pldata[[1]]
     pl2 <- pldata[[2]]
     fbinary(pl1,pl2,error=400,ppm=TRUE,theta=1,weight=FALSE,method="rmi",uniq=TRUE)
     # dont resolve multiple matches.
     fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=FALSE,method="rmi",uniq=FALSE)
     fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=FALSE,method="hg")
     fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="gower")
     fbinary(pl1,pl2,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
     # seach with one peak-list in a list of peak-lists.
     fbinary(pldata,pl1,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
     # compute distances (dissimilarities) and cluster.
     tmp <- fbinary(pldata,NULL,error=400,ppm=TRUE,theta=0.2,weight=TRUE,method="fm")
     plot(hclust(tmp,method="average"))

