The TRANSREG Procedure

Details

Here are some examples of usage of the new LAMBDA= option:

   model BoxCox(y / lambda=0) = identity(x1-x5);
   model BoxCox(y / lambda=-2 to 2 by 0.1) = identity(x1-x5);
   model BoxCox(y) = identity(x1-x5);

In the first example

   model BoxCox(y / lambda=0) = identity(x1-x5);

LAMBDA=0 specifies a Box-Cox transformation with a power parameter of 0. Since a single value of 0 was specified for LAMBDA=, there is no difference between the following models:

   model BoxCox(y / lambda=0) = identity(x1-x5);
   model log(y) = identity(x1-x5);

In the second example

   model BoxCox(y / lambda=-2 to 2 by 0.1) = identity(x1-x5);

there is a list of power parameters specified. This tells PROC TRANSREG to find a Box-Cox transformation before the usual iterations begin. PROC TRANSREG tries each power parameter in the list and picks the best transformation. A maximum likelihood approach (Draper and Smith 1981, pp. 225-226) is used. Note that this is quite different from TRANSREG's usual approach of iteratively finding optimal transformations. It is analogous to SMOOTH, RANK, and the other nonoptimal transformations that are performed before the iterations begin.

In the third example

   model BoxCox(y) = identity(x1-x5);

the default list of -3 TO 3 BY 0.25 is used.

The procedure prints the optimal power parameter, a confidence interval on the power parameter (using the ALPHA= transformation option), a "convenient" power parameter (selected from the CLL= option list), and the log likelihood for each power parameter tried.

Chapter Contents
Previous
Next
Top