<< lsfit Regression regres >>

Stixbox >> Stixbox > Regression > lsselect

lsselect

Select a predictor subset for regression

Calling Sequence

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

Parameters

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.

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.

BB

Column p of BB is the best B of parameter size p.

Description

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

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


Report an issue
<< lsfit Regression regres >>