the observation’s assigned node number. cars; target enginesize / level=int; input mpg_highway model; run;HPSPLIT and rare events. NOTE: PROCEDURE HPSPLIT used (Total process time): real time 0. Thank you. . The HPSPLIT procedure is a high-performance utility procedure that creates a decision tree model and saves results in output data sets and files for use in SAS Enterprise Miner. 1. Subsections: 61. PDF EPUB Feedback. 3: Detailed Tree Diagram By default, this view provides detailed splitting information about the first three levels of the tree, including the splitting variable and splitting values. The HPSPLIT Procedure. For this reason, the HPSPLIT procedure implements a strategy that combines three different methods of generating candidate splits. The next step is to write the model equation, which is done in lines 22 to 25 below. This column shows the probability of a. 4. James Goodnight, SAS founder and CEO, 1979 Neural Networks and Statistical Models,. The KDE Procedure. bds_vars maxdepth = 4 maxbranch = 4 nodestats=DT_1. 3) It is available in 9. 5 Assessing Variable Importance. 5 Assessing Variable Importance. By default, ORDER=FORMATTED except for numeric CLASS variables that have no specified. 16. Mark as New;specifies how PROC HPSPLIT creates a default splitting rule to handle missing values, unknown levels, and levels that have fewer observations than you specify in the MINCATSIZE= option. 4: Creating a Binary Classification Tree with Validation Data , which is shown in Figure 16. Usage Note. It builds a ROC curve and returns a “roc” object, a list of class “roc”. Answer: SAS command: proc import out =breast_cancer_dataset datafile = "V:Assignmentreast_cancer_dataset. These are reported as “VSSE” and “VIMPORT. I added an ID variable to the data set provided by SAS (this will be useful later): data new; set sashelp. After twisting SAS code, I can run a different version of HPSPLIT in SAS EG without syntax errors. Question 6 1 / 1 pts In SAS Studio, the procedure _____ can be used to build a decision tree model. Perform search. The OUTPUT statement allows several SAS data sets to be created. I have problem whereby a proc hpsplit program running on my local machine (SAS 9. The following variables were selected and applied to the HPSPLIT method using SAS Version 9. So far I can think only of listing all colors that I'd like to use, via goptions, colors=(). The data are measurements of 13 chemical attributes for 178 samples of wine. At the end of it, the instructor used Proc access to combined multiple model and compared them using the ROC chart above. bweight; count + 1; run; Then running the basic HPSPLIT is fairly straightforward: proc hpsplit data=new seed=123; class black boy married momedlevel momsmoke ;SAS/STAT User's Guide: High-Performance Procedures Example Programs. Hello, Which version of SAS are you using? Find out by submitting: %PUT &=sysvlong; I suppose you will get always the same result if you specify a seed: SEED= Specifies the random number seed to use for cross validation like proc hpsplit data=train leafsize=2213 seed=1014; Kind regards, K. However, information about the WEIGHT statement was omitted from the documentation. I created a reproachable example below. Similarly, the surrogate count counts the number of times a. Re: HPSPLIT Grow Statement for Imbalanced Data. sas. cars; class model; model enginesize = mpg_highway model; run; proc hpsplit data=sashelp. Alas, PROC SPLIT does not produce PMML has has no conveniences to help generate it. By default, PROC HPSPLIT treats variable s as categorical variables whose order. 4: ODS Tables Produced by PROC HPSPLIT. The default depends on the value of the MAXBRANCH= option. Getting Started: HPSPLIT Procedure. 1. We would like to show you a description here but the site won’t allow us. The HPSPLIT procedure measures model fit based on a number of metrics for classification trees and regression trees. PROC HPSPLIT tries to create this number of children unless it is impossible (for example, if a split variable does not have enough levels). System Options. Subsections: 61. Multiple CLASS statements are supported. 05; roc; run; Eight variables were removed from the model. CIND 119 Assignment1 Student: Lexie Tai ID: 501071793 Q1a proc import out = breastinfo datafile= "V:Lab 1reast_cancer_dataset. The procedure produces classification trees, which model a categorical response, and regression trees, which model a continuous response. 45539 PROC DTREE 78028 PROC HPSPLIT 10557 PROC SPLIT 57397 PROC DECISION That is correct. My code is the following: proc hpsplit data = &lib. Read the file in SAS and display the contents using the import and print procedures. The p-values for the final split determine. The count-based variable importance. The output of the decision tree algorithm is a new column labeled “P_TARGET1”. Table 5. csv" dbms =csv replace; getnames =yes; proc. PROC HPSPLIT Features. treeaddhealth;PROC SORT; BY AID; ods graphics on;proc hpsplit seed=15531;c. (SAS also has PROC HPSPLIT and PROC DMSPLIT. More info on the algorithm can be found in section 3. You can also find links to the syntax and output of the HPSPLIT procedure. By default, all variables that appear in the. 2 REPLIES 2. csv a. This list can be used, for example, in the model statement of a subsequent procedure. The table below is generated from the lift table macro. free, open-source programming media. e. 3. 16. PROC HPSPLIT runs in either single-machine mode or distributed mode. The following SAS program is a basic example of programming with SAS and Jupyter Notebook. maxdepth=8 plots=zoomedtree; target default_flag / level=interval; input bureau_Score cc_util annual_income emp_length. Predictor variables were chosen during the exploratory data analysis due to their possible importance to the model as described in the table above (see code at end). Just the nature of this particular graphics output. Special SAS Data Sets. PROC HPSPLIT Features F 5007 PROC HPSPLIT Features The main features of the HPSPLIT procedure are as follows: provides a variety of methods of splitting nodes, including criteria based on impurity (entropy, Giniproc template; source HPStat. Next, you will specify the categorical variables of the data with the class statement. For interval inputs, CHAID chooses the best. Area under the curve (AUC) is defined as the area under the receiver operating characteristic (ROC) curve. Details. Instead, PROC HPBIN takes the binning results from the BINS_META data set and calculates the weight of evidence and information value. RESOURCES /. SAS is headed back to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user. Variables that appear after the equal sign (=) in the MODEL statement are explanatory variables that model the response variable. (View the complete code for this example . 4TS1M3) or later. 61. The OUTPUT statement creates a data set that contains one observation for each observation in the input data set. 1 User's Guide: High-Performance Procedures documentation. bds_vars maxdepth = 4 maxbranch =. The default is set using the following equation, where b is the value. Documentation Example 4 for PROC HPSPLIT. FLAG=p. This works and my codes so far are as following: %macro DTStudy (maxbranch=2, maxdepth=5, minleafsize=20); %let branchTries = %sysfunc(countw(&maxbran. Accordingly to SAS Note 50555 the HPSPLIT procedure is first available as a stand-alone procedure in SAS/STAT 14. The following statements create a regression tree model: ods graphics on; proc hpsplit data=sashelp. The INBREED Procedure. 3® User’s Guide The HPSPLIT Procedure SAS® Documentation January 31, 2023I use the proc hpsplit to discretize the interval variables and collapsing the levels of the ordinal and nominal variables. By default, PROC HPSPLIT selects the parameter that minimizes the ASE, as indicated by the vertical reference line and the dot in Output 16. I confirm that I've turned on ODS GRAPHICS. NOTE: Cross-validating using 10 folds. hmeq seed=123 maxdepth=10 plots= (zoomedtree (nodes= ("3") depth=5)); Doubly confusing because testing the same proc hpsplit on a different machine (SAS server installation using EG 5. Read Less. Error! Reference source not found. Data sets that have a large number of predictor variables and a large number of response levels can cause PROC HPSPLIT to run out of memory. HPSPLIT in SASPy. This works and my codes so far are as following: %macro DTStudy (maxbranch=2, maxdepth=5, minleafsize=20); %let branchTries = %sysfunc(countw(&maxbran. If you're running this on a server, make sure that path is a path you can write to from the server (not "c:something" probably). The PROC HPLOGISTIC statement invokes the procedure. On the other hand, in order to find out the most desired output given the combination of variables, a decision tree with PROC The relative importance metric is a number between 0 and 1. The HPSPLIT Procedure This document is an individual chapter from SAS/STAT ® 15. 4. 5-style pruning, one for no pruning, one for cost-complexity pruning, one for pruning by using a specified metric and choosing the subtree based on the change in a specified metric, and one for pruning by using a specified metric and choosing the subtree based on. SAS/STAT User's Guide: High-Performance Procedures Example Programs. Thank you in advance and have a good day. Introduction. PGBy default, PROC HPSPLIT creates a decision tree (nominal target). The greedy method, which is based on the CHAID algorithm, finds split candidates by recursively halving the data. Impute the missing values with a procedure (PROC STDIZE, PROC MI, PROC FASTCLUS, and so on), or by some value (s) that make sense based on your subject knowledge. Enter terms to search videos. I do not have a code for my condition table where i have variables "DECISION" and "ID" - it comes as an output from hpsplit procedure. PROC HPSPLIT Statement CODE Statement CRITERION Statement ID Statement INPUT Statement OUTPUT Statement PARTITION Statement PERFORMANCE Statement PRUNE Statement RULES Statement SCORE Statement TARGET Statement. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNELCharacter variable appeared on the MODEL statement without appearing on a CLASS statement. HMEQ data set which is available as a sample data set in SAS Enterprise Miner and is also attached here. )For this reason, the HPSPLIT procedure implements a strategy that combines three different methods of generating candidate splits. The data are measurements of 13 chemical attributes for 178 samples of wine. documentation of the PROC > Details > ODS Table Names, or put : ODS TRACE ON; (ODS Table Names are then published in the LOG) --> then run your PROC. Customer Support SAS Documentation. It is my experience that it is hard to fit the output from PROC HPSPLIT into a window and still be able to read the text. The HPSPLIT procedure is a high-performance utility procedure that creates a decision or regression tree model and saves results in output data sets and files for use in SAS Enterprise Miner. I also ran proc product_status and the have same SAS packages both local (EG) and on server for both SAS/STAT and High Performance Suite. 2. Any help is greatly appreciated!! My outcome is a binary group, and I have a few binary predictors. It is calculated in two steps. Then open a text box on the forum with the </> icon and paste the text. If you specify both the DESCENDING and ORDER= options, PROC HPSPLIT orders the categories according to the ORDER= option and then reverses that order. seed = an initial value from which a random number function or. The actual context is more the following: The next step is to separat. The OUT= data set contains the following: the response variable. 08058. Then it selects the requested number of surrogate-split variables based on the agreement, in order of agreement. In this case, events are considered extremely costly so we are willing to trade off specificity (false positives) for sensitivity (false negatives). Read the file in SAS and display the contents using the import and print procedures. The SASLOG was shown as follows: NOTE: The HPSPLIT procedure is executing in single-machine mode. The HPSPLIT procedure provides two types of criteria for splitting a parent node : criteria that maximize a decrease in node impurity, as defined by an impurity function, and criteria that are defined by a statistical test. uses values of a chi-square test (decision tree) or an F test (regression tree) to merge similar levels of nominal inputs until the number of children in the proposed split reaches the value of the MAXBRANCH= option. 379. The data are measurements of 13 chemical attributes for 178 samples of wine. Barring missing target values, which are not handled by the tree, the per-leaf and per-observation methods for calculating the subtree. Neither dissatisfied or satisfied (OR neutral) Satisfied. 0038, which corresponds to a subtree with seven leaves. (View the complete code for this example . The plot in Figure 62. comBy default, PROC HPSPLIT creates a plot of the estimated misclassification rate at each complexity parameter value in the sequence, as displayed in Output 15. The count-based variable importance simply counts the number of times in the tree that a particular variable is used in a split. 6 Applying Breiman’s 1-SE Rule with Misclassification Rate. execution mode: single mode, number of threads:2. In SAS, the HPSPLIT procedure is a high-performance procedure to create a decision. filename x temp; proc hpsplit data=sashelp. The procedure produces classification trees, which model a categorical response, and regression trees, which model a continuous response. By default, this view provides detailed splitting information about the first three levels of the tree, including the splitting variable and splitting values. 5-style pruning, one for no pruning, one for cost-complexity pruning, one for pruning by using a specified metric and choosing the subtree based on the change in a specified metric, and one for pruning by using a specified metric and choosing the subtree based on. PROC HPGENSELECT runs in either single-machine mode or distributed mode. This includes the class of generalized linear models and generalized additive models based on distributions such as the binomial for logistic models, Poisson, gamma, and others. documentation. That is, instead of scanning through the entire data set, the proportions of observations are examined at the leaves. 9 Two approaches of how to use binned X in a model are: (1) As a classification variable (via a CLASS statement), or (2) As a weight of evidence coded variable. Output. SAS® Help Center. That is, instead of scanning through the entire data set, PROC HPSPLIT examines the proportions of observations at the leaves. The code requests the displayed Tree to have a depth of 5 beginning from node "3": proc hpsplit data=x. Then, for each variable, it calculates the relative variable importance as the RSS-based importance of this variable divided by the maximum RSS-based importance among all the variables. train(drop = survived); run;This is a very basic outline of the procedure but a necessary step in the process, simply due to the lack of online documentation. csv" dbms=csv replace; getname=yes; proc print data = breastinfo; title "Breast Cancer"; run; Q1b The resulting decision tree has 286 examples at the root node. Re: PROC HPSPLIT Decision Tree. , to create the sequence of values and the corresponding sequence of nested subtrees, . I am using PROC RANK and group them into 5 before creating portfolios. Getting Started: HPSPLIT Procedure. HPSplit Procedure proc hpsplit data=sashelp. The PROC HPSPLIT statement and the MODEL statement are required. The HPSPLIT procedure provides various methods of handling missing values of predictor variables. The skeleton code would look like . You can use scoring to improve or deploy your model. SAS/STAT 14. Do you have any additional comments or suggestions regarding SAS documentation in general that will help us better serve you? PDF. proc hpsplit data=hpsplit. 8563 represents 'Success', based on variable i_22801, parameter being >= -2. Each wine is derived from one of three cultivars that are grown in the same area of Italy. i have tried on HPSplit procedure and managed to score them successfully as below using sampsio. The plot in Figure 15. However, when someone else ran the same command on his PC, the complete results displayed. The following statements use the HPSPLIT procedure to create a classification tree: ods graphics on; proc hpsplit data=Wine seed=15533; class Cultivar; model Cultivar =. NOTE: PROCEDURE HPSPLIT used (Total process time): documentation. baseball seed=123; class league division; model logSalary = nAtBat nHits nHome nRuns nRBI nBB yrMajor crAtBat crHits crHome crRuns crRbi crBB league division nOuts nAssts nError; output out=hpsplout; run; By default, the tree is grown using the. The HPSPLIT procedure is designed for high-performance computing. Output 61. specifies how PROC HPSPLIT creates a default splitting rule to handle missing values, unknown levels, and levels that have fewer observations than you specify in the MINCATSIZE= option. sas. To be able to force particular splits, you would have to use the Interactive Decision Tree Application in the Decision Tree node in EM. The LOGISTIC procedure, never one for a dull moment, has extended unequal slopes models to all polytomous responses as well as providing the adjacent-category logit response function. bweight; count + 1; run; Then running the basic HPSPLIT is fairly straightforward: proc hpsplit data=new seed=123; class black boy married momedlevel momsmoke ; the differences between PROC HPSPLIT and PROC DTREE. You can override the default number of bins by using the NUMBIN= option on any INPUT statement. comIf you specify a validation set by using a PARTITION statement, PROC HPSPLIT uses the validation set for subtree selection. 61. MAXDEPTH= number. I have already created a partition in my data, which I will use to separate my data into training and testing. The output of the decision tree algorithm is a new column labeled “P_TARGET1”. Finding the optimal subtree from this sequence is then a question of determining the optimal value of the complexity parameter . 3 Creating a. 16. In SAS you can use PROC LOGISTIC for the analysis. Node 1 split should read variable1 < 200 and. Posted 11-02-2015 04:38 PM (6260 views) | In reply to PGStats. Hello , That's very weird. bank_train is used to develop the decision tree. Specifies the input data set. PROC HPSPLIT uses sensitivity as the Y axis and 1 – specificity as the X axis to draw the ROC curve. PROC HPSPLIT uses sensitivity as the Y axis and 1 – specificity as the X axis to draw the ROC curve. You can specify one of the following values for ordering:The reason I mentioned HPSPLIT is that it is yet another nonparametric regression procedure in SAS. specifies the sort order for the levels of classification variables. PROC HPSPLIT in SAS9. The SAS procedure ‘HPFOREST’ is used when implementing the Random Forest algorithm. I've obtained a graph with proc tree where I put all information in the leaves but I would prefer the layout provided by proc netdraw or proc dtree. 4656 F Chapter 62: The HPSPLIT Procedure Overview: HPSPLIT Procedure The HPSPLIT procedure is a high-performance procedure that builds tree-based statistical models for classification and regression. The text box is important to preserve text formatting of any diagnostics that SAS places in the log. Getting Started: HPSPLIT Procedure. And new software implements generalized additive models byThe variable Cultivar is a nominal categorical variable with levels 1, 2, and 3, and the 13 attribute variables are continuous. 6 Applying Breiman’s 1-SE Rule with Misclassification. There are two approaches to using PROC HPSPLIT to score a data set. As a result, it does not create utility files but rather stores all the data in memory. Getting Started: HPSPLIT Procedure. HPSplit. A primary splitting rule is always calculated by default, and it provides for the assignment of observations. Dissatisfied. This example uses the wine data from the Getting Started section in the PROC HPSPLIT chapter of the SAS/STAT User's Guide. 4. Here we specify seed to be a certain number seed = [CONSTANT]so that the result will be reproducible. Misclassification rate on proc hpsplit Posted 11-30-2021 04:27 PM (398 views) I am using a proc hpsplit to create a decision tree. baseball seed=123; class league division; model logSalary = nAtBat nHits nHome nRuns nRBI nBB yrMajor crAtBat crHits crHome crRuns crRbi crBB league division nOuts nAssts nError; output out=hpsplout; run; And here is the log with error:You can use the code generated to bin your data. The data are measurements of 13 chemical attributes for 178 samples of wine. 1) proc logistic. Finding the optimal subtree from this sequence is then a question of determining the optimal value of the complexity parameter . documentation. The default is the number of target levels. Example 61. 1. comThe DTREE Procedure Overview The DTREE procedure in SAS/OR software is an interactive procedure for decision analysis. 1. The SAS kernel for Juypter is designed to enable users to write programs for SAS with Jupyter Notebooks. Important to know about the HP-routines is that they are we're created with concurrent programming in mind (multiple cpus and/or threads executing in parallel). Posted 03-02-2018 03:53 PM (1448 views) | In reply to pamelisa. Getting Started: HPSPLIT Procedure. Perform search. Hi, if specific output nodestates= option in Proc HPSPLIT, it will give you a table that I think is the key to generate the tree rule. 11 . I am trying to generate a decision tree by using PROC HPSPLIT on E guide at work. The following statements create a regression tree model: ods graphics on; proc hpsplit data=sashelp. This is an entirely new procedure for me and it's a little daunting. 0 Likes. If you're running this on a server, make sure that path is a path you can write to from the server (not "c:\something" probably). . Other procedure can produce nice plots, such as REG, GLM and so on. Both types of splitting rules use the value of a single predictor variable to assign an observation to a branch. Overview. 1 Building a Classification Tree for a Binary Outcome. I've tried changing various options in the hpsplit procedure itself to no avail. INTRODUCTION When we want to explore the relationship of variables and outcome, that is the effect of variables on the outcome, PROC HPSPLIT is a useful tool. the observation’s assigned leaf number. Upgrades are free with a valid SAS license. The HPSPLIT procedure is a high-performance procedure that builds tree-based statistical models for classification and regression. NOTE: Distributed mode requires SAS High-Performance Statistics. An unknown level is a level of a categorical predictor that does not exist in the training data but is encountered during scoring. The default is the most recently created data set. It can handle large data sets efficiently and provides various options for splitting criteria, pruning methods, and output statistics. Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data. Getting Started; Syntax. If the sum of the elements is equal to zero, then the sign depends on how the number is rounded off. The. specifies the maximum depth of the tree to be grown. Alexandre Dumas,. This table shows that that model adequately separated the positive and negative observations. 4. Solved: Hey All I know that proc hpsplit isn't available in SAS Studio. The following statements create the tree model:PROC HPSPLIT generates SAS DATA step code when you specify the CODE statement. 16. 1 x64), all expected ODS results do appear. proc hpsplit data=sashelp. 2) to run exhaustive CHAID. The classification and regression trees are no longer just the purview of data miners, but are now available to SAS/STAT customers with the HPSPLIT procedure. txt" ; PROC HPSPLIT uses weakest-link pruning, as described by Breiman et al. I can work with proc hpsplit in SAS/STAT module. AUC is calculated by trapezoidal rule integration, This example explains basic features of the HPSPLIT procedure for building a classification tree. Some of the variables that are involved in the manufacturing process are as follows: gTemp is the growth temperature of substrate, aTemp is the anneal. But I couldn't find anything concrete in. . I am using this data set to create portfolios for each date (newdatadate in my case). The HPSPLIT procedure provides two types of criteria for splitting a parent node : criteria that maximize a decrease in node impurity,. I am looking for a way to create a couple/few step code to do following: I have two variables, ID and DECISION (screenshot attached), and I have another variable in a different dataset (variable called Var1) that can be empty or any number from 0 to infinite (with decimals), for example first row. Following suggestions from yesterday's question, we have converted a single long column of text to four text strings across -- a text string in each of four columns, 1000 rows of such. The model will run, but the output is not what I expected. Overview. Problem Note 59256: The WEIGHT statement in the HPSPLIT procedure was omitted from the documentation. 7877 proc hpsplit data=train leafsize=2213 assignmissing=none seed=1111; 7878 model loan_status =mths_since_last_delinq; 7879 output nodestats=work. sas. TARGET [RESPONSE]: here we plug in a single response variable. Details. I am using the SASPy equivalent to PROC HPSPLIT to build a decision tree. The procedure produces classification trees, which model a categorical response, and regression trees, which model a continuous response. )The following two programs are equivalent. USEFUL OPTIONS IN PROC HPFOREST . SAS/STAT® 15. Examples: HPSPLIT Procedure. If you specify a validation set by using a PARTITION statement, PROC HPSPLIT uses the validation set for subtree selection. Finding the optimal subtree from this sequence is then a question of determining the optimal value of the complexity parameter . The code below refers to the SAMPSIO. 4, if you can upgrade. The splitting rule above each node determines which. 18 4670 Chapter 62: The HPSPLIT Procedure MAXDEPTH=number specifies the maximum depth of the tree to be grown. HPSPLIT procedure. . Hello , This is the general definition for a seed in SAS. For more information about interval. PROC FACTOR chooses the solution that makes the sum of the elements of each eigenvector nonnegative. This is the main function of the pROC package. PROC HPSPLIT Features. 2 of "Targeted Learning" by van Der Laan and Rose (1ed); specifically, this macro implements the algorithm shown in figure 3. I have almost zero working knowledge of ODS but got as far as locating the reference below: proc hpsplit data=default_flag leafsize=50. SAS/STAT 15. 4: Creating a Binary Classification Tree with Validation Data . The score script that was generated from the CODE FILE statement in the PROC HPSPLIT procedure is applied to the holdout bank_test data set through the use of the %INCLUDE statement. Each table that the HPSPLIT procedure creates has a name associated with it, and you must use this name to refer to the table when you use ODS statements. summarizes the available options in the PROC HPLOGISTIC statement by function. In other fields, the phrase refers to classification or regression trees. For distributed mode, the table displays the grid mode (symmetric or asymmetric), the number of compute nodes, and the number of threads per node. This is performed either by using the validation partition. For more information about these mappings, see the section Levelization of Classification Variables in SAS/STAT 14. If any variables are character or to be treated as categorical, at least one CLASS statement is required. 5 Assessing Variable Importance. Hello! I am trying to create a decision tree in SAS v9. Option. Below is the code and attached are the outputs from HPSPLIT from both runs:The following statements use the HPSPLIT procedure to create a decision tree and an output file that contains SAS DATA step code for predicting the probability of default: proc hpsplit data=sashelp. (SAS also has PROC HPSPLIT and PROC DMSPLIT. - Included data about race and income The PRUNE statement controls pruning. 187 views. Output 16. Output 61. If you're a student or researcher you can also use SAS UE which would have support for HPSPLIT. Finally, the next block calls the SGPLOT procedure to plot the partial dependence function, which is shown as a series plot in Figure 1: proc sgplot data=partialDependence; series x = horsepower y = AvgYHat; run; quit; You can create PD plots for model inputs of both interval and classification variables. categories. However, the output is not what I expected. The opposite is: ODS TRACE OFF; Koen. What’s New in SAS/STAT 15. I have specified the EVENT= option in the MODEL statement, which. In addition, the BONFERRONI keyword in the PROC HPSPLIT statement causes the p -value of the split (which was determined by Kolmogorov-Smirnov distance) to be adjusted using the.