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 pro-
       portional fitting to normalize 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  num-
	      ber  of rows and columns in the matrix; the remain-
	      der 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 fractional 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 com-
       paring the output 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 Classification 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)
