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 vege-
       tation 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 corresponding 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 chan-
       nel 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	information 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  number.	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 Laboratory.

       _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)
