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The TRANSREG Procedure |

PROC TRANSREG can estimate missing values, with or without category or
monotonicity constraints, so that the regression model fit is optimized.
Several approaches to missing data handling are provided. All
observations with missing values in IDENTITY, CLASS, POINT, EPOINT,
QPOINT, SMOOTH, PSPLINE, and BSPLINE variables are excluded from the
analysis. When METHOD=UNIVARIATE (specified in the PROC TRANSREG or
MODEL statement), observations with missing values in any of the
independent variables are excluded from the analysis. When you specify
the NOMISS *a-option*, observations with missing values in the other
analysis variables are excluded. Otherwise, missing data are estimated,
using variable means as initial estimates.

You can specify the
LINEAR, OPSCORE, MONOTONE, UNTIE, SPLINE, MSPLINE, SSPLINE,
LOG, LOGIT, POWER, ARSIN, RANK, and EXP
transformations in any combination with nonmissing values,
ordinary missing values, and special missing values, as long as
the nonmissing values in each variable have positive variance.
No category or order restrictions are placed
on the estimates of ordinary missing values.
You can force missing value estimates within a variable to be
identical by using special missing values (refer to "DATA
Step Processing" in *SAS Language Reference: Concepts*).
You can specify up to 27 categories of missing values, in which
within-category estimates must be the same, by coding the
missing values using ._ and .A through .Z.

You can also specify an ordering of some missing value estimates.
You can use the MONOTONE= *a-option* in the PROC TRANSREG or
MODEL statement to indicate a range of special missing
values (a subset of the list from .A to .Z) with estimates
that must be weakly ordered within each variable in which they appear.
For example, if MONOTONE=AI, the nine classes, .A, .B,...,
.I, are monotonically scored and optimally
scaled just as MONOTONE transformation values are scored.
In this case, category but not order restrictions are
placed on the missing values ._ and .J through .Z.
You can also use the UNTIE= *a-option* (in the PROC TRANSREG
or MODEL statement) to indicate a range of special
missing values with estimates that must be weakly ordered
within each variable in which they appear but can be untied.

The missing value estimation facilities allow for partitioned or mixed-type variables. For example, a variable can be considered part nominal and part ordinal. Nominal classes of otherwise ordinal variables are coded with special missing values. This feature can be useful with survey research. The class "unfamiliar with the product" in the variable "Rate your preference for 'Brand X' on a 1 to 9 scale, or if you are unfamiliar with the product, check 'unfamiliar with the product'" is an example. You can code "unfamiliar with the product" as a special missing value, such as .A. The 1s to 9s can be monotonically transformed, while no monotonic restrictions are placed on the quantification of the "unfamiliar with the product" class.

A variable specified for a LINEAR transformation, with special missing values and ordered categorical missing values, can be part interval, part ordinal, and part nominal. A variable specified for a MONOTONE transformation can have two independent ordinal parts. A variable specified for an UNTIE transformation can have an ordered categorical part and an ordered part without category restrictions. Many other mixes are possible.

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