Utility Macros for Enterprise Miner Daniel Edstrom, Noel-Levitz, Centennial, CO

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1 Utility Macros for Enterprise Miner Daniel Edstrom, Noel-Levitz, Centennial, CO ABSTRACT Creating custom SAS code for use inside an Enterprise Miner diagram often requires managing variable lists stored as macro variables. This paper outlines two innovative utility macros that can manage these variable lists and help accomplish tasks more easily in Enterprise Miner. The basic form of each macro is given along with an example of its use. These utility macros are easily transportable among different Enterprise Miner diagrams and in Base SAS programs. INTRODUCTION The use of SAS code nodes inside SAS Enterprise Miner allows the flexibility to create output files or treat data differently than the default behavior. For example, custom SAS code can be written to perform variable transformations outside the options given in the variable transformation node of Enterprise Miner. In order to modify default procedures while maintaining processing efficiency, it is often beneficial to utilize the different types of variables via macro variables. For example, all nominal input variables are stored as a list in the macro variable _NOMINAL. In practice, however, the macro variable lists are more useful when they can be modified in some way. In order to make this concept more precise, I outline several steps in the modeling process used by Noel-Levitz and discuss two of the SAS macros that I have written which make the necessary modifications to the default macro variable lists in SAS Enterprise Miner. Noel-Levitz builds predictive models using prospect, inquiry, or admit data from college recruitment databases. The purpose of these models is to predict which students have the highest estimated probability of enrollment at a particular institution. In our SAS Enterprise Miner modeling process, we use the regression node to build logistic regression models using stepwise selection criteria. As part of the model selection process, the Noel-Levitz process creates output files for each step in the stepwise model selection. This differs from the default behavior of SAS Enterprise Miner, which produces output for the last step in the stepwise procedure. Additionally, Noel-Levitz produces proprietary output for each model in order to measure model quality. Both of these steps require utility macros that can easily modify the default macro variable lists in order to produce output efficiently. Two of these utility macros are discussed in more detail below. EXAMPLE 1: REMOVE A WORD FROM A LIST The first utility macro used in the Noel-Levitz modeling process removes a particular word from a list of words stored as a macro variable. The basic structure of the macro is given below. %macro dropvar(code,list); %global newlist; %let newlist=; %let count=0; %let word=%quote(%scan(&list,1)); %do %while (&word ne); %if &word ne %quote(&code) %then %do; %let newlist=&newlist %str( ) &word; %let newlist=%cmpres(&newlist); Suppose we have the following macro variable declarations: %let fulllist=major1 ZIPCODE HSCODE SOURCE AVGINC; 1

2 %let dropvar=source; The macro can be called using the following declaration: %dropvar(&dropvar,&fulllist); This will result in the creation of the new macro variable newlist, which now resolves to: MAJOR1 ZIPCODE HSCODE AVGINC Note that newlist is initialized as a global macro variable, since we will want to use this variable after execution of the dropvar macro. EXECUTION OF THE DROPVAR MACRO In order to make the steps in this macro more clear, we proceed with a step-by-step dissection of the process. It is easy to see that the first lines in the dropvar macro initialize several variables. Next, the following values are given to various macro variables: %let word=%quote(%scan(&list,1)); /* word = MAJOR1 */ /* count = 1 */ %if &word ne %quote(&code) %then %do; /* code = SOURCE, so this statement is true */ %let newlist=&newlist %str( ) &word; /* newlist = MAJOR1 */ /* word = ZIPCODE */ The loop continues like this until word is assigned the value SOURCE. In this case, the do-loop is bypassed and variable is thus dropped from the newlist macro variable. 2

3 EXAMPLE OF DROPVAR MACRO USE One of the output files in the Noel-Levitz modeling process is a summary of the way in which each model variable affects the resulting estimated probabilities of enrollment. The resulting graph, shown below, gives the range of estimated probabilities for each variable while holding all other model variables at their mean value. Probability Ranges of Model Variables - Based on Mean Score of Probability XN020-Initial Source Code XN086-High School CEEB Code XN056-First Major as Inquiry XN013-Primary State of Student FN220-LQM Personalizat ion Indicator N461-Avg. Household Income ($) XN720- N230-Distance Dominant from Campus Equifax Cluster MSCORE- Model Score Model Variables In order to create this output, we must score the model validation dataset once for each variable in the model. The basic form of the proc score statement is as follows: proc score data=scorfile score=nl.lregmod out=scor&var type=parms; var &mnmxlist; run; In this example, the variables to be scored are stored as the macro variable mnmxlist. The variable for which a probability range is to be produced is used in its existing form. All other variables are replaced with the mean value of the variable. Suppose we have a model with five variables. Let TRUEVAR_k be model variable k and MEANVAR_j be the mean value for model variable j. Then the mnmxlist macro variable must resolve to the following: Model variable 1: TRUEVAR_1 MEANVAR_2 MEANVAR_3 MEANVAR_4 MEANVAR_5 Model variable 2: MEANVAR_1 TRUEVAR_2 MEANVAR_3 MEANVAR_4 MEANVAR_5 Model variable 3: MEANVAR_1 MEANVAR_2 TRUEVAR_3 MEANVAR_4 MEANVAR_5 Model variable 4: MEANVAR_1 MEANVAR_2 MEANVAR_3 TRUEVAR_4 MEANVAR_5 3

4 Model variable 5: MEANVAR_1 MEANVAR_2 MEANVAR_3 MEANVAR_4 TRUEVAR_5 Then, we call the proc score five times to produce the desired output for each of our model variables. Notice how the dropvar macro helps us in this example. First, mean values for each variable are produced and merged with the validation dataset. The names of the mean value variables are stored as a macro variable list whose default value is given by: MEANVAR_1 MEANVAR_2 MEANVAR_3 MEANVAR_4 MEANVAR_5 We then use the dropvar macro to drop the mean value in question and replace it with the true variable for which output should be produced. EXAMPLE 2: COUNT THE NUMBER OF WORDS THAT MATCH BETWEEN TWO LISTS The second utility macro used in the Noel-Levitz modeling process counts the number of words that match between two lists. The basic structure of the macro is given below. %macro nmatch(list1,list2); %local match; %let match = 0; %let nlist1=%wordcnt(&list1); %let nlist2=%wordcnt(&list2); %do aa=1 %to &nlist1; %do bb=1 %to &nlist2; %if %upcase(%scan(&list1,&aa)) = %upcase(%scan(&list2,&bb)) %then %let match = %eval(&match+1); &match Note the use of the %wordcnt macro. This is an additional utility macro that can be used to count the number of words in a list. The structure of the wordcnt macro is given below. %macro wordcnt(list); %local count; %let count = 0; %if &list ne %then %do; %let word = %scan(&list,1); %do %while (&word ne); &count EXAMPLE OF NMATCH MACRO USE As mentioned previously, Noel-Levitz uses stepwise logistic regression to build models. Unlike the SAS Enterprise Miner approach of generating model validation output only for the best model, Noel-Levitz creates output for every step in the stepwise regression process. Additionally, we create additional output for the single model that SAS Enterprise Miner selects as the best model. In many cases, this model will be the last model created in the stepwise process. There are many cases where the stepwise process removes variables because of their relationship with other variables that have been stepped in. When this happens, we need a way to determine if a model in the stepwise process is the best model created by SAS Enterprise Miner. 4

5 The process of creating model output files begins by parsing out each variable in the stepwise process and counting the number of total steps. For each step in the stepwise process, we then have the following macro variables: varlist /* The list of variables for a particular step in the stepwise process */ bestlist /* The list of variables in the best model */ nbest /* The number of variables in the best model */ Based on these values, we can perform a check at each step in the process, whereby we compare the variables in varlist and bestlist to see if they match. If they do, then we create the additional output we desire. The macro call looks like this: %if %nmatch(&bestvars,&varlist) = &nbest %then %do; /* Additional output for the best model is created here */ CONCLUSION It is easy to see how useful utility macros can be when dealing with lists of words or variables stored as macro variables. Macro processing is an efficient way to manage information like this because the variable lists can be manipulated using only the SAS macro processor. This paper has outlined two utility macros that Noel-Levitz uses in its predictive modeling process. There are countless variations of utility macros that can be written and modified to suit any individual process, whether inside SAS Enterprise Miner or in Base SAS. CONTACT INFORMATION Comments and questions regarding this presentation are highly encouraged. You may contact the author at: Daniel Edstrom Noel-Levitz, Suite South Syracuse Way Centennial, CO Phone: FAX: dan-edstrom@noellevitz.com 5