LSSELECT

lsselect
Select a predictor subset for regression

     	  [Q, I, B, BB] = lsselect(y,x,crit,how,pmax,level)

	  Selects a good subset of regressors in a multiple linear 
	  regression model. 

         Input :  y    dependant variate (column vector)
                  x    regressor variates
                  crit selection criterion (string):
	             'HT'   Hypothesis Test (default level = 0.05)
	             'AIC'  Akaike's Information Criterion 
	             'BIC'  Bayesian Information Criterion
	             'CMV'  Cross Model Validation (inner criterion RSS)
                  how    (string)  choses between :
                    'AS'   All Subsets
	             'FI'   Forward Inclusion
	             'BE'   Backward Elimination
                  pmax limits the number of included parameters (scalar).
	           level  ,optional input argument, p-value reference used
                          for inclusion or deletion.

         Output:  Q criterion as a function of the number of parameters; 
                    might be interpreted as an estimate of the prediction 
                    standard deviation. For the method 'HT', Q is instead 
                    the successive p-values for inclusion or elimination.
                  I index numbers of the included columns.
                  B vector of coefficients, ie the suggested model is 
	             Y = X*B. 
                  Column p of BB is the best B of parameter size p. 

	  The last column of the prediction matrix x must be an intercept 
	  column, ie all elements are ones. This column is never excluded 
	  in the search for a good model. If it is not present it is added.

	  This function is not highly optimized for speed but rather for
	  flexibility. It would be faster if 'all subsets' were in a 
	  separate routine and 'forward' and 'backward' were in another
	  routine, especially for CMV.

         See also LSFIT and LINREG.

See Also