Builds multiple linear regression models using forward selection, backward selection, or stepwise selection.
For a list of all members of this type, see StepwiseRegression Members.
System.Object
Imsl.Stat.StepwiseRegression
Public static (Shared in Visual Basic) members of this type are safe for multithreaded operations. Instance members are not guaranteed to be thread-safe.
Class StepwiseRegression
builds a multiple linear regression model using forward selection, backward selection, or forward stepwise (with a backward glance) selection.
Levels of priority can be assigned to the candidate independent variables using Levels. All variables with a priority level of 1 must enter the model before variables with a priority level of 2. Similarly, variables with a level of 2 must enter before variables with a level of 3, etc. Variables also can be forced into the model using Force. Note that specifying "force" without also specifying levels of priority will result in all variables being forced into the model.
Typically, the intercept is forced into all models and is not a candidate variable. In this case, a sum-of-squares and crossproducts matrix for the independent and dependent variables corrected for the mean is required. Other possibilities are as follows:
The stepwise regression algorithm is due to Efroymson (1960). StepwiseRegression
uses sweeps of the covariance matrix (input in cov, if the covariance matrix is specified, or generated internally) to move variables in and out of the model (Hemmerle 1967, Chapter 3). The SWEEP operator discussed in Goodnight (1979) is used. A description of the stepwise algorithm is also given by Kennedy and Gentle (1980, pp. 335-340). The advantage of stepwise model building over all possible regression (SelectionRegression
) is that it is less demanding computationally when the number of candidate independent variables is very large. However, there is no guarantee that the model selected will be the best model (highest ) for any subset size of independent variables.
Namespace: Imsl.Stat
Assembly: ImslCS (in ImslCS.dll)
StepwiseRegression Members | Imsl.Stat Namespace | Example 1