The HPBIN Procedure

PROC HPBIN Statement

PROC HPBIN <options>;

The PROC HPBIN statement invokes the procedure. Table 4.1 summarizes important options in the PROC HPBIN statement by function.

Table 4.1: PROC HPBIN Statement Options

Option	Description
Basic Options
DATA=	Specifies the input data set
OUTPUT=	Specifies the output data set
NOPRINT	Overwrites the ODS output
Binning Level Options
NUMBIN=	Specifies the global number of bins for all binning variables
Binning Method Options
BUCKET	Specifies the bucket binning method
PSEUDO_QUANTILE	Specifies the pseudo–quantile binning method
QUANTILE	Specifies the quantile binning method
WINSOR	Specifies the Winsorized binning method and the rate that it uses
WINSORRATE=
Statistics Options
COMPUTESTATS	Computes the basic statistics of the binning variables
COMPUTEQUANTILE	Compute the quantiles of the binning variables
Weight-of-Evidence Options
BINS_META=	Specifies the BINS_META input data set, which contains the binning results
WOE	Computes the weight of evidence and information values
WOEADJUST=	Specifies the adjustment factor for the weight-of-evidence calculation

You can specify the following optional arguments:

BINS_META=SAS-data-set

specifies the BINS_META input data set, which contains the binning results. The BINS_META data set contains six variables: variable name, binned variable name, lower bound, upper bound, bin, and range. The mapping table that is generated by PROC HPBIN can be used as the BINS_META data set.

BUCKET | QUANTILE | PSEUDO_QUANTILE | WINSOR WINSORRATE=number

specifies which binning method to use. If you specify BUCKET, then PROC HPBIN uses equal-length binning. If you specify QUANTILE, then PROC HPBIN uses quantile binning. If you specify PSEUDO_QUANTILE, then PROC HPBIN generates a result that approximates the quantile binning. If you specify WINSOR, PROC HPBIN uses Winsorized binning, and you must specify the WINSORRATE option with a value from 0.0 to 0.5 exclusive for number. You can specify only one option. The default is BUCKET.

However, when a BINS_META data set is specified, PROC HPBIN does not do binning and ignores the binning method options, binning level options, and INPUT statement. Instead, PROC HPBIN takes the binning results from the BINS_META data set and calculates the weight of evidence and information value.

COMPUTEQUANTILE

computes the quantile result. If you specify COMPUTEQUANTILE, PROC HPBIN generates the quantiles and extremes table, which contains the following percentages: 0% (Min), 1%, 5%, 10%, 25% (Q1), 50% (Median), 75% (Q3), 90%, 95%, 99%, and 100% (Max).

COMPUTESTATS

computes the statistic result. If you specify COMPUTESTATS, basic statistical information is computed and ODS output can be provided. The output table contains six variables: the mean, median, standard deviation, minimum, maximum, and number of bins for each binning variable.

DATA=SAS-data-set

specifies the input SAS data set or database table to be used by PROC HPBIN.

If the procedure executes in distributed mode, the input data are distributed to memory on the appliance nodes and analyzed in parallel, unless the data are already distributed in the appliance database. In this case, PROC HPBIN reads the data alongside the distributed database.

For single-machine mode, the input must be a SAS data set.

NOPRINT

suppresses the generation of ODS outputs.

NUMBIN=integer

specifies the global number of binning levels for all binning variables. The value of integer can be any integer between 2 and 1,000, inclusive. The default number of binning levels is 16.

The resulting number of binning levels might be less than the specified integer if the sample size is small or if the data are not normalized. In this case, PROC HPBIN provides a warning message.

You can specify a different number of binning levels for each different variable in an INPUT statement. The number of binning levels that you specify in an INPUT statement overwrites the global number of binning levels.

OUTPUT=SAS-data-set

creates an output SAS data set in single-machine mode or a database table that is saved alongside the distributed database in distributed mode. The output data set or table contains binning variables. To avoid data duplication for large data sets, the variables in the input data set are not included in the output data set.

WOE

computes the weight of evidence (WOE) and information value (IV).

WOEADJUST=number

specifies the adjustment factor for the weight-of-evidence calculation. You can specify any value from 0.0 to 1.0, inclusive, for number. The default is 0.5.