m.ipf(1)		      Grass User's Manual		      m.ipf(1)



NNAAMMEE
       mm..iippff - Iterative proportional fitting for error matrices.

	     _(_G_R_A_S_S _D_a_t_a _I_m_p_o_r_t_/_P_r_o_c_e_s_s_i_n_g _P_r_o_g_r_a_m_)

SSYYNNOOPPSSIISS
       mm..iippff
       mm..iippff hheellpp
       mm..iippff [--eemmzz] [iinnppuutt==_n_a_m_e] [ffoorrmmaatt==_s_t_r_i_n_g] [ssttoopp==_v_a_l_u_e]

DDEESSCCRRIIPPTTIIOONN
       _m_._i_p_f  uses an error or confusion matrix produced by _r_._c_o_i_n or _r_._k_a_p_p_a,
       smooths zero counts, and does iterative proportional fitting to normal-
       ize the matrix.

OOPPTTIIOONNSS
   FFllaaggss::
       --ee     Indicate when the iterative algorithm finished.

       --mm     Print the marginals (row and column totals) with each matrix.

       --zz     Print the intermediate (smoothed) matrix.


   PPaarraammeetteerrss::
       iinnppuutt==_n_a_m_e
	      The  input  file must have the following format:	the first line
	      contains an integer K which is the number of rows and columns in
	      the  matrix;  the  remainder  of the file is the matrix, i.e., K
	      lines, each containing K integers.  If the input is  not	speci-
	      fied on the command line, it may come from standard input.

       ffoorrmmaatt==_s_t_r_i_n_g
	      Specifies  the  format  conversion  string  used	to  print  the
	      results.	Default is _%_7_._3_f. For details, see the UNIX  man  page
	      for _p_r_i_n_t_f_.

       ssttoopp==_v_a_l_u_e
	      The  stopping criteria is a floating point number which actually
	      specifies an integer maximum number of iterations  and  a  frac-
	      tional  change  in marginal. The default, 100.01, specifies that
	      the interative proportional fitting will stop at 100  iterations
	      or  when marginals do not change by 0.01, whichever comes first.


EEXXAAMMPPLLEE
       For the following input,
       3
       712    0   12
	 0  584    2
	18    0  434
	zero counts in the matrix will be smoothed:
       711.249	 0.438	12.314
	 0.443 583.289	 2.268
	18.309	 0.273 433.418
	and the matrix will be normalized to yield:
       0.969 0.001 0.022
       0.001 0.999 0.004
       0.031 0.001 0.973


NNOOTTEESS
       Iterative proportional curve fitting is useful when comparing the  out-
       put  of	image  classification  algorithms  (for  example, _i_._m_a_x_l_i_k and
       _i_._s_m_a_p), especially  when  training  fields  (signatures)  and/or  test
       fields  are  different.	The  diagonals of the normalized matrix can be
       used in a Tukey multiple comparison test.

SSEEEE AALLSSOO
       _A_s_s_e_s_s_i_n_g _M_u_l_t_i_p_l_e _C_l_a_s_s_i_f_i_c_a_t_i_o_n_s - GRASS Tutorial on _m_._i_p_f


       _r_._c_o_i_n

       _r_._k_a_p_p_a



       Zhuang, X., B.A. Engel, X. Xiong, and C. Johanssen. 1994.  Analysis  of
       Classification  Results of Remotely Sensed Data and Evaluation of Clas-
       sification Algorithms,
	_P_h_o_t_o_g_r_a_m_m_e_t_r_i_c _E_n_g_i_n_e_e_r_i_n_g _a_n_d _R_e_m_o_t_e _S_e_n_s_i_n_g (in press)

AAUUTTHHOORR
       James Darrell McCauley, Agricultural Engineering, Purdue University

       _L_a_s_t _c_h_a_n_g_e_d_: _$_D_a_t_e_: _2_0_0_2_/_0_1_/_2_5 _0_5_:_4_5_:_3_3 _$



GRASS 5.0							      m.ipf(1)
