The function outputs a message and a model table.
Output Message Schema
| Column | Data Type | Description |
|---|---|---|
| message | VARCHAR | Reports that the result is stored in the table specified in the OutputTable syntax element. |
OutputTable Schema
This is the model table to input to LARPredict.
| Column | Data Type | Description |
|---|---|---|
| steps | INTEGER | Sequence number of step. One LAR or LASSO move represents one step. |
| var_id | INTEGER | Sequence number of predictor. Sequence of predictors is specified by TargetColumns syntax element. |
| var_name | VARCHAR | Column name of predictor. |
| max_abs_corr | DOUBLE PRECISION | Modified maximum absolute correlation (common for all active variables) between active variables and current residuals. This value is not necessarily in the range [0,1]. |
| step_length | DOUBLE PRECISION | Distance to move along equiangular direction in step. |
| intercept | DOUBLE PRECISION | Constant item in model. Value evolves along path. |
| predictor_column | DOUBLE PRECISION | [Column appears once for each predictor_column.] Coefficient for predictor. |
Interpreting the Output
At the beginning of stepi, the variable Xk (identified by the values in columns var_id and var_name) either enters into the regression model (indicated by a positive value in the column var_id) or drops from the regression model (indicated by a negative value in the column var_id), and the current common correlation between active variables and current residuals is the value in the column max_abs_corr.
After moving along the equiangular direction for the distance in the column step_length, either an inactive variable qualifies to enter into the model or a currently active variable is dropped from the model, whereby the process reaches stepi+1. The intercept and coefficients correspond to both the end of stepi and the beginning of stepi+1.