Hi Boyi,
To do a forward selection, you can start with a model with no variables.
For example:
>> rng('default')
>> X = rand(20,7);
>> y = [ones(20,1),X(:,1),X(:,3),X(:,5)]*rand(4,1) + 0.1*randn(20,1);
>> stepwiselm(X,y,'Constant')
If there is a set of predictors that must be included in the model, you can use the 'Lower' name/value pair to specify it, and "stepwiselm" will add terms in addition to that, such as starting with the form:
>> lowerMdl = 'y ~ 1 + x1 + x2';
>> stepwiselm(X,y,lowerMdl,'lower',lowerMdl)
As an additional note, if you want to prevent "stepwiselm" from going backwards, you can set the 'PRemove' value to 1 in the Name/ Value pair arguments for "stepwiselm". Generally both forward and backward stepwise regression are both used to determine a final model- it terminates when no single step improves the model according to your criterion.
Hope this helps! -Cindy
