## Transformations for Proportion Variables

Subsections:

Figure 32.14 shows the transformations that are available when you select For proportions from the Family list. These transformations are intended for variables that represent proportions. That is, the variable must take values between 0 and 1. You can also use these transformations for percentages if you first divide the percentages by 100.

Chapter 7 of Atkinson (1985) is devoted to transformations of proportions. Equations for these transformations are given in Table 32.4.

Figure 32.14: Transformations for Proportions

Table 32.4: Description of Transformations for Proportions

 Default Name of Transformation Parameter odds(Y) Odds_Y logit(Y) Logit_Y probit(Y) Probit_Y arcsin(Y) Arcsin_Y arcsin(sqrt(Y)) Angular_Y folded power(Y;a) MLE FPow_Y See text. Guerrero-Johnson(Y;a) MLE GJ_Y See text. Aranda-Ordaz(Y;a) MLE AO_Y See text.

The probit function is the quantile function of the standard normal distribution.

The last three transformations in the list are similar to the Box-Cox transformation described in the section Normalizing Transformations. The parameter for each transformation is in the unit interval: . Typically, you choose a parameter that maximizes (or nearly maximizes) a log-likelihood function.

The log-likelihood function is defined as follows. Let be the number of nonmissing values, and let be the geometric mean function. Each transformation has a corresponding normalized transformation , to be defined later. Define

and define the log-likelihood function as

The following sections define the normalized transformation for the folded power, Guerrero-Johnson, and Aranda-Ordaz transformations. In each section, .