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TURF.EXAMPLES

This helpfile defines a small dataset and shows what the TURF command does when various turf options are used on the dataset. This section was last updated on Sept 14,2006.

A simple dataset used for some examples

The following dataset has a case identifier (case.id), a case weight (www), and the responses to five variables. The responses are zeros, ones and two twos. This dataset is used to show various TURF options.

The twos are treared differently from ones only when the RESPONSE.WEIGHTS option is in use. 

 -----------------file ddd--------------
 case.id  www    v1    v2    v3    v4    v5

    9001    1     1     1     0     0     0
    9002    1     1     1     0     0     0
    9003    1     1     1     0     0     0
    9004    1     1     1     0     0     0

    9005    1     1     1     0     0     0
    9006    1     1     1     0     0     0
    9007    1     0     1     0     0     0
    9008    1     0     0     1     0     0

    9009    1     0     0     1     0     0
    9010    1     0     0     1     0     0
    9011    1     0     0     1     0     0
    9012    1     0     0     0     1     0

    9013    1     0     0     0     1     0
    9014    1     0     0     0     1     0
    9015    3     0     0     0     0     2
    9016    3     0     0     0     0     2

Example 1

A TURF run using defaults 

   turf ddd [drop case.id  www], size 3, reach.results rrr $
   list rrr $
This command uses PPL (P-STAT Programming Language) to drop the first two variables, leaving five items for the turf analysis. Using [keep v1 to v5] would have done the same thing.

SIZE 3 tells the command to look at the items in groups of 3. REACH.RESULTS RRR creates a file named RRR that identifies the best groups. That file is then listed.

Ten groups will be tested: v1-v2-v3, v1-v2-v4, etc through v3-v4-v5. We are looking for the group that has at least one positive response for the largest number of cases. In other words, which group of three items REACH the most cases ?

The best combination uses variables v2, v3, and v4, which reach 14 of the 16 cases. The FREQ score for that combination is also 14.

V1 is not included because it adds nothing once v2 is selected. V1-v2- v3 have the highest number of of responses, but do not reach as many different cases as the group of v2-v3-v4. The twos in cases 9015 and 9016 on v5 are treated as ones.

Example 2

A TURF run using case weighting 

   turf ddd [drop case.id ],  size 3,  reach.results rrr,
                              case.weights www $
   list rrr $
Here, WWW is not dropped because it is needed in the command. We are seeking the group of three variables that has the largest WEIGHTED number of reached cases. Cases 9015 and 9016 are the only cases with a caseweight of other than one, so they are the ones affected by case- weights in this example.

The best group is v2-v3-v5, which reach 17 weighted cases. V2 and V3 provide 11 cases (all of which had unit weights). Adding v4 would pro- vide 3 more, but adding v5 increases the weighted reach count by 6, since each of those two cases has a caseweight of three on WWW. The FREQ score for that combination is also 17.

Example 3

A TURF run using response weighting and a threshold of more than one 

   turf ddd [drop case.id  www],  size 3,  reach.results rrr,
                                  response.weights,
                                  reach.threshold 2 $
   list rrr $
In examples 1 and 2, the responses to the items were treated in a zero versus nonzero manner. Using RESPONSE.WEIGHTS causes the actual response values to contribute to the reach scores. In addition, using REACH.THRESHOLD 2 causes a case to be reached only when its reach score for a given group is 2 or more.

The best group in this example is v1-v2-v5, which reached 8 cases. Cases 1 through 6 were reached by a response of 1 to both v1 and v2; cases 15 and 16 were reached because of responses of 2 on v5. The FREQ score for that combination is 16.

Example 4

A TURF run using item weighting and a threshold of more than one 

   /* create a file containing a weight value of 2 for item v3 */
   make work1, vars name:c weight;
   v3 2
   $

   turf ddd [drop case.id  www],  size 3,
                                  reach.results   rrr,
                                  item.weights    work1,
                                  reach.threshold 2 $
   list rrr $
Normally each item has a weight of one; each has the same contribution to a reach score. It is possible, however, to make some items worth more than other, possible reflecting, for example, differences in costs of the items. In this example, item v3 is weighted. This is conveyed in file WORK1 whose record defines a weight of 2 for item v3. This causes responses on v3 to be worth twice what they would otherwise be worth. As in exam- ple 3, a threshold of 2 is used. The best group in this example is v1-v2-v3, which reach 10 cases. Cases 1 through 6 achieve a reach score of 2 using items v1 and v2. Cases 8 through 11 have reach scores of 2 because of the item weight given to v3. The FREQ score for that combination is 20.