r.linear.regression(1)	      Grass User's Manual	r.linear.regression(1)



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NNAAMMEE
       rr..lliinneeaarr..rreeggrreessssiioonn - linear regression calculation from data stored in
       ASCII file

       _(_G_R_A_S_S _I_m_a_g_e _P_r_o_c_e_s_s_i_n_g _P_r_o_g_r_a_m_)

SSYYNNOOPPSSIISS
       rr..lliinneeaarr..rreeggrreessssiioonn

       rr..lliinneeaarr..rreeggrreessssiioonn hheellpp

       rr..lliinneeaarr..rreeggrreessssiioonn iinnppuutt==_n_a_m_e oouuttppuutt==_n_a_m_e

DDEESSCCRRIIPPTTIIOONN
       The _r_._l_i_n_e_a_r_._r_e_g_r_e_s_s_i_o_n program calculates the linear regression model.
       If  it  is  used  as  an image processing tool, the multispectral space
       remote sensing data will be the regression variables (ASCII  file)  and
       the  ground vegetation coverage measurements will be the response vari-
       ables (also ASCII file) and this command will be useful	for  obtaining
       linear regression models from the remote-sensing data which have corre-
       sponding ground measurement  and  for  predicting  vegetation  coverage
       using  other  remote-sensing  data  which  have no corresponding ground
       truth records. The input file has the following format


       regression valuables x1, x2, ... response variable  y  channel  1  (x1)
       channel 2 (x2) ... coverage


       For  a three channel remote-sensing data the following is an example of
       input ASCII file
       0.4350	      0.2616	     0.7016	    0.98
       0.4140	      0.2620	     0.6520	    0.99
       0.4940	      0.3500	     0.5580	    0.34
       0.5983	      0.5350	     0.5650	    0.10
       0.4883	      0.3733	     0.5533	    0.88
       0.4150	      0.2916	     0.5116	    0.60
       0.5566	      0.5250	     0.5466	    0.09
       0.4420	      0.2820	     0.6800	    0.86
       0.4220	      0.2620	     0.6260	    0.88
       0.4766	      0.3666	     0.5933	    0.61
       0.5180	      0.4300	     0.5140	    0.60
       0.4416	      0.2700	     0.7383	    0.96
       0.4583	      0.3116	     0.5133	    0.76
       0.4300	      0.2750	     0.7233	    0.98
       0.4320	      0.2760	     0.6460	    1.00
       0.4733	      0.3566	     0.5616	    0.53
       0.4200	      0.2450	     0.7966	    1.00
       0.4850	      0.3533	     0.7216	    0.99
       0.4360	      0.2620	     0.7620	    0.99
       0.4283	      0.2650	     0.6783	    0.91
       0.4633	      0.3200	     0.6750	    0.94


       The resulted regression model (coefficient numbers) and related	infor-
       mation  about  the  confidencial  test, goodness or utility test (e.g.,
       correlation coefficient r between observed and calculated  coverage,  F
       value and t value) are put on the output file (ASCII file also).



	      The formular of linear regression can be:
				       y=a[0]x[0] + a[1],
				       y=a[0]x[0] + a[1]x[1] + a[2]
				       y=a[0]x[0] + a[1]x[1] + a[2]x[2] + a[4]




       _r_._l_i_n_e_a_r_._r_e_g_r_e_s_s_i_o_n will be run non-interactively if the user specifies
       program arguments on the command line, using the form:


       _r_._l_i_n_e_a_r_._r_e_g_r_e_s_s_i_o_n _i_n_p_u_t_=_n_a_m_e _o_u_t_p_u_t_=_n_a_m_e


       But  after  run, the computer will prompt the user to select model num-
       ber.  Alternately, the user can simply type: _r_._l_i_n_e_a_r_._r_e_g_r_e_s_s_i_o_n on the
       command	line without program arguments. In this case, the user will be
       prompted for parameter values using the standard GRASS  user  interface
       described in the manual entry for _p_a_r_s_e_r.

SSEEEE AALLSSOO
	_i_._n_d_v_i_, _r_._r_a_t_i_o_n_a_l_._r_e_g_r_e_s_s_i_o_n

AAUUTTHHOORRSS
       Hong  C. Zhuang, U.S. Army Construction Engineering Research Laboratory
       Department of Electrical Computer Engineering, University  of  Illinois
       at Urbana-Champaign.


       Michael	Shapiro,  U.S.	Army Construction Engineering Research Labora-
       tory.

       _L_a_s_t _c_h_a_n_g_e_d_: _$_D_a_t_e_: _2_0_0_2_/_0_1_/_2_5 _0_5_:_4_5_:_3_4 _$



GRASS 5.0						r.linear.regression(1)
