GENSELECT Procedure

reverses the order of the response categories. If you specify both the DESCENDING and ORDER= options, PROC GENSELECT orders the response categories according to the ORDER= option and then reverses that order.

specifies the event category for the binary and multinomial response model. PROC GENSELECT models the probability of the event category. The EVENT= option has no effect when there are more than two response categories.

You can specify one of the following:

By default, EVENT=FIRST.

For example, the following statements specify that observations whose formatted value is 1 represent events in the data. The probability that PROC GENSELECT models is thus the probability that the variable def takes the (formatted) value 1.

proc genselect data=mylib.MyData;
   class A B C;
   model def(event ='1') = A B C x1 x2 x3;
run;

specifies the sort order for the levels of the response variable. You can specify the following values:

By default, ORDER=FORMATTED.

For more information about sort order, see the chapter on the SORT procedure in the Base SAS Procedures Guide and the discussion of BY-group processing in SAS Programmers Guide: Essentials.

specifies the reference category for the generalized logit model and the binary response model. For the generalized logit model, each logit contrasts a nonreference category with the reference category. For the binary response model, specifying one response category as the reference is the same as specifying the other response category as the event. You can specify one of the following:

By default, REF=LAST.

centers and scales continuous main effects for internal computation. (Continuous main effects are centered and scaled to aid in computing maximum likelihood estimates.) Parameter estimates and related statistics are always reported on the original scale. This option has no effect if you specify the LASSO model selection method or the elastic net selection method; instead, you should specify the CENTERLASSO option.

specifies whether all effects, including categorical effects, are centered and scaled internally for the LASSO model selection method or the elastic net selection method. (Effects are centered and scaled to aid in model selection.) Parameter estimates and related statistics are always reported on the original scale. By default, CENTERLASSO=TRUE.

constructs confidence limits for each parameter estimate. The confidence level is 0.95 by default; you can change it by specifying the ALPHA= option. The CLB option is not available when you use either LASSO selection or elastic net selection.

specifies the response distribution for the model. The keywords and the associated distributions are shown in Table 7. For information about default and commonly used link functions for each distribution function, see Table 9.

Table 7: Built-In Distribution Functions

Keyword	Distribution Function
BETA	Beta
BINARY	Binary
BINOMIAL	Binary or binomial
EXPONENTIAL	Exponential
GAMMA	Gamma
GENPOISSON | GPOISSON	Generalized Poisson
GEOMETRIC	Geometric
IGAUSSIAN | IG	Inverse Gaussian
LOGNORMAL | LOGN	Lognormal
MULTINOMIAL	Multinomial. For ordinal responses, it fits
	a model that has a cumulative link function.
	For nominal responses, it fits a model
	that has a generalized logit link function.
NEGATIVEBINOMIAL | NB	Negative binomial
NORMAL | GAUSSIAN	Normal
POISSON	Poisson
T<>	t with degrees of freedom. If you
	do not specify , a value of is used.
TWEEDIE<(Tweedie-options)>	Tweedie
WEIBULL	Two-parameter Weibull

When DISTRIBUTION=TWEEDIE, you can specify the following Tweedie-options:

forces effects to be included in all models. If you specify INCLUDE=n, then the first n effects that are listed in the MODEL statement are included in all models. If you specify INCLUDE=single-effect or if you specify a list of effects within parentheses, then the specified effects are forced into all models. The effects that you specify in the INCLUDE= option must be explanatory effects that are specified in the MODEL statement before the slash (/). This option is not available if you use elastic net selection.

models missing values by using extra model effects. These effects consist of dummy variables that take the value 1 when the value of a continuous model variable involved in the effect is missing, and take the value 0 otherwise. The missing value in the original model effect is replaced by the average value of the effect for the nonmissing values. For continuous-by-class effects, such as A*x, where A is a classification variable and x is a continuous variable, informative missingness creates multiple dummy columns and substitutes the effect mean of x that corresponds to the respective level of A. Missing values for classification variables are treated as valid levels. For more information about informative missingness, see the section Informative Missingness in Chapter 2, Shared Concepts.

specifies the link function for the model. The keywords and their associated link functions are shown in Table 8. Default and commonly used link functions for the available distributions are shown in Table 9.

Table 8: Built-In Link Functions

Keyword	Link Function
CLOGLOG | CLL	Complementary log-log
	or cumulative complementary log-log
GLOGIT	Generalized logit
IDENTITY | ID	Identity
INVERSE | RECIPROCAL | RECIP	Reciprocal
POWERMINUS2 | INV2 | POWMINUS2	Reciprocal square
POWER(p)	Power
LOG	Logarithm
LOGIT	Logit or cumulative logit
LOGLOG	Log-log or cumulative log-log
PROBIT | NORMIT	Probit or cumulative probit

For the probit and cumulative probit links, denotes the quantile function of the standard normal distribution.

If you do not specify the LINK= option, a default link function is used, as shown in Table 9. For binary or multinomial distributions, only the link functions shown in Table 9 are available. For the other distributions, you can use any link function shown in Table 8 by specifying the LINK= option. Other commonly used link functions for each distribution are shown in Table 9.

includes no intercept in the model. An intercept is included by default. The NOINT option is not available for multinomial models.

specifies a variable to be used as an offset to the linear predictor. An offset plays the role of an effect whose coefficient is known to be 1. The offset variable cannot appear in the CLASS statement or elsewhere in the MODEL statement. Observations that have missing values for the offset variable are excluded from the analysis.

specifies a fixed dispersion parameter for those distributions that have a dispersion parameter. The dispersion parameter that is used in all computations is fixed at number and not estimated.

begins the selection process from the designated initial model for the forward selection method. If you specify START=n, then the starting model includes the first n effects that are listed in the MODEL statement. If you specify START=single-effect or START=(effects), then the starting model includes those specified effects. The effects that you specify in the START= option must be explanatory effects that are specified in the MODEL statement before the slash (/). This option is not available when you specify METHOD=BACKWARD or ELASTICNET in the SELECTION statement.

computes Wald statistics for Type 3 contrasts for each effect that you specify in the MODEL statement. This option is not available for models that you fit by using the GEE method. For more information, see the section Joint Tests and Type 3 Tests.