For any variable that is being ranked, PROC RANK stores in memory the value of that variable for every observation.

Ranks are useful for investigating the distribution of values for a variable. The ranks divided by n or n+1 form values in the range 0 to 1, and these values estimate the cumulative distribution function. You can apply inverse cumulative distribution functions to these fractional ranks to obtain probability quantile scores. You can compare these scores to the original values to judge the fit to the distribution. For example, if a set of data has a normal distribution, the normal scores should be a linear function of the original values, and a plot of scores versus original values should be a straight line.

Many nonparametric methods are based on analyzing ranks of a variable:

When PROC RANK ranks values, if two or more values of an analysis variable that are within a BY group are equal, then tied values are present in the data. Because the values are indistinguishable and there is usually no further obvious information about which the ranks can reasonably be based, PROC RANK does not assign different ranks to the values. Tied values could be arbitrarily assigned different ranks. But in statistical applications such as nonparametric statistical tests using ranks, it is conventional to assign the same rank to tied values.

These statistical tests commonly assume that the data is from a continuous distribution, in which the probability of a tie is theoretically zero. In practice, whether because of inaccuracies in measurement, the finite accuracy of representation within a digital computer, or other reasons, tied values often occur. It is also conventional in these statistical tests to assign the average rank to a group of tied values. Assignment of the average rank is preferred because it preserves the sum of the ranks and, therefore, does not distort the estimate of the cumulative distribution function.

For applications within and outside of statistics, the RANK procedure provides the TIES= option to control the treatment of tied values. The default value for this option depends on the specified ranking or scoring method, which you can specify with the options of the PROC RANK statement. For ranking and scoring methods, when TIES=LOW, TIES=HIGH, or TIES=MEAN, tied values are initially treated as if they are distinguishable. These methods all begin by sorting the values of the analysis variable within a BY group, and then assigning to each nonmissing value an ordinal number that indicates its position in the sequence.

Subsequently, for non-scoring methods, PROC RANK resolves tied values by selecting the minimum with TIES=LOW, selecting the maximum with TIES=HIGH, or calculating the average of the ordinals in a group of tied values with TIES=MEAN. PROC RANK then obtains the rank from this value through one or more further transformations such as scaling, translation, and truncation.

Scoring methods include normal and Savage scoring, which are requested by the NORMAL= and SAVAGE options. Non-scoring methods include ordinal ranking, the default, and those methods that are requested by the FRACTION, NPLUS1, GROUPS=, and PERCENT options. For the scoring methods NORMAL= and SAVAGE, PROC RANK obtains the probability quantile scores with the appropriate formulas as if no tied values were present within the data. PROC RANK then resolves tied values by selecting the minimum, selecting the maximum, or calculating the average of all scores within a tied group.

For all ranking and scoring methods, when TIES=DENSE, tied values are treated as indistinguishable, and each value within a tied group is assigned the same ordinal. As with the other TIES= resolution methods, all ranking and scoring methods begin by sorting the values of the analysis variable and then assigning ordinals. However, a group of tied values is treated as a single value. The ordinal assigned to the group differs by only +1 from the ordinal that is assigned to the value just prior to the group, if there is one. The ordinal differs by only -1 from the ordinal assigned to the value just after the group, if there is one. Therefore, the smallest ordinal within a BY group is 1, and the largest ordinal is the number of unique, nonmissing values in the BY group.

After the ordinals are assigned, PROC RANK calculates ranks and scores using the number of unique, nonmissing values instead of the number of nonmissing values for scaling. Because of its tendency to distort the cumulative distribution function estimate, dense ranking is not generally acceptable for use in nonparametric statistical tests.

Note that PROC RANK bases its computations on the internal numeric values of the analysis variables. The procedure does not format or round these values before analysis. When values differ in their internal representation, even slightly, PROC RANK does not treat them as tied values. If this is a concern for your data, then round the analysis variables by an appropriate amount before invoking PROC RANK. For information about the ROUND function, see ROUND Function in SAS Functions and CALL Routines: Reference..

In-database processing has several advantages over processing within SAS. These advantages include increased security, reduced network traffic, and the potential for faster processing. Increased security is possible because sensitive data does not have to be extracted from the DBMS. Faster processing is possible for the following reasons:

In-database processing for PROC RANK supports the following database management systems:

The presence of table statistics might affect the performance of the RANK procedure's in-database processing. If your DBMS is not configured to automatically generate table statistics, then manual generation of table statistics might be necessary to achieve acceptable in-database performance.

If the RANK procedure's input data set is a table or view that resides within a database from which rows would normally be retrieved with the SAS/ACCESS interface to a supported DBMS, then PROC RANK can perform much or all of its work within the DBMS. There are several other factors that determine whether such in-database processing can occur. In-database processing will not occur in the following circumstances:

When PROC RANK can process data within the DBMS, it generates an SQL query. The structure of the SQL query that is generated during an in-database invocation of PROC RANK depends on several factors, including these: The SQL query expresses the required calculations and is submitted to the DBMS. The results of this query will either remain as a new table within the DBMS if the output of the RANK procedure is directed there, or it will be returned to SAS. The settings for the MSGLEVEL option and the SQLGENERATION option determine whether messages will be printed to the SAS log, which indicates whether in-database processing was performed. Generated SQL can be examined by setting the SQL_IP_TRACE option or the SASTRACE= option. Beginning with SAS 9.3, SQL_IP_TRACE shows the SQL that is generated by PROC RANK. For more information, see the SASTRACE= option in SAS/ACCESS for Relational Databases: Reference or the SQL_IP_TRACE option in SAS(R) Analytics Accelerator 1.3 for Teradata: Guide.

For more information about the settings for system options, library options, data set options, and statement options that affect in-database performance for SAS procedures, see the SQLGENERATION= LIBNAME Option and the SQLGENERATION= option in SAS/ACCESS for Relational Databases: Reference.