fdist-methods             package:msbase             R Documentation

_E_u_c_l_i_d_e_a_n _o_r _M_a_n_h_a_t_t_a_n _d_i_s_t_a_n_c_e _f_o_r _p_e_a_k-_l_i_s_t _c_o_m_p_a_r_i_s_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Computes Euclidean or Manhattan distance for two aligned vectors.

_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

    full: if 'TRUE' then compute for matching and not matching peaks

  weight: if 'TRUE' weight differences between matching peaks.

    uniq: if 'TRUE' compute non-crossing matching

  method: type of dissimilarity:

             *  euclidean - euclidean distance  ('dist')

             *  manhattan - manhattan distance. ('dist')

             *  canberra - canberra distance. ('dist')

             *  simindex - similarity index. 

    norm: how to scale the itensities:

             *  vnorm - l=2 norm, vector length normalization.

             *  tic - l=1 norm, total ion count normalization.

             *  student - root mean square normalization (see. 'scale') 

             *  zscore - z-score normalization. (see. 'scale') 

   theta: "numeric" how to weight the non-matching peaks.

       N: default 0 - total length of alinged peak-lists.

   range: experimental

_M_e_t_h_o_d_s:

     _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" :

     _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" :

     _o_b_x = "_M_a_s_s_v_e_c_t_o_r_l_i_s_t", _o_b_y = "_N_U_L_L" :

_A_u_t_h_o_r(_s):

     Witold E. Wolski witek96@users.sourceforge.net

_S_e_e _A_l_s_o:

     'fcor-methods','fbinary-methods'

_E_x_a_m_p_l_e_s:

     data(pldata)
     fdist(pldata[[1]],pldata[[2]],error=400,ppm=TRUE,theta=1,weight=FALSE,scale="zscore",method="euclidean")
     fdist(pldata[[1]],pldata[[2]],error=400,ppm=TRUE,theta=1,weight=FALSE,scale="no",method="manhattan")
     tmp <- fdist(pldata,NULL,error=400,ppm=TRUE,theta=1,weight=FALSE,scale="student",method="euclidean")
     plot(hclust(tmp,method="average"))

