USEFUL OPTIONS IN PROC HPFOREST . ASSIGNMENT 1 By : Syeda Aleya Section : DLO 1. See the METHOD=GCV option in the MODEL statement of PROC GAM and the SELECT= option in PROC LOESS. On the PROC HPSPLIT statement, there is a PLOTS option that will allow you to open up the subtree where you start and to a set depth. Hello @artyomkosyan and welcome to the SAS Support Communities!. From the output for the ctable option we obtain the classification accuracy metrics for the fitted model. Instead, PROC HPBIN takes the binning results from the BINS_META data set and calculates the weight of evidence and information value. 61. Hello, I am trying to use proc hpsplit to perform some decision tree modeling, I think the procedure successfully generate a tree and output text based results, but for some reason the graphic plots are not displayed. Posted 11-05-2018 10:50 AM (523 views) I have a dataset with 7 observations for each explanatory. The following statements invoke the HPSPLIT procedure to create a classification tree for LobaOreg: . PROC HPSPLIT runs in either single-machine mode or distributed mode. You can specify this pruning method for both classification trees and regression trees (continuous response). You could try to find optimal date ranges with HPSPLIT. My question is that : it is because of the number of observations ?The HPSPLIT Procedure - SAS SAS/STAT User s GuideThe HPSPLIT ProcedureThis document is an individual chapter fromSAS/STAT User s correct bibliographic citation for this manual is as follows: SAS Institute Inc. First, PROC HPSPLIT finds the maximum RSS-based variable importance. 01 seconds - PROC HPSPLIT can also be used to create a regression tree - In this example, we model total 2015 health care expenditures - Created a dataset, modelsetp, limited to privately insured adults present in both years, who remained alive for the full measurement period. The splitting rule above each node determines which. the observation’s assigned leaf number. The relative importance metric is a number between 0 and 1. PROC HPSPLIT is one of the procedures that can be used to identify the “best” split and creation of child nodes based on which we can analyze the dependency of variables. This is performed either by using the validation partition. By default, PROC HPSPLIT treats variable s as categorical variables whose order. 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. 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. Does the last section of Example 67. Documentation Example 5 for PROC HPSPLIT. It is mentioned in SAS documentation that it will eventually replace PROC SPLIT, as it is faster than PROC SPLIT on larger datasets. SAS Customer Recognition Awards. (2018). bank_train is used to develop the decision tree. hmeq maxdepth=7 maxbranch=2; target BAD; input DELINQ DEROG JOB NINQ REASON / level=nom;The PROC HPFOREST statement invokes the procedure. The p-values for the final split determine. Hello , This is the general definition for a seed in SAS. There are two approaches to using PROC HPSPLIT to score a data set. SAS/STAT 15. I added an ID variable to the data set provided by SAS (this will be useful later): data new; set sashelp. data plots= (zoomedtree (depth=2 nodes= (0 3 4)));08-26-2021 01:33 PM. 16. PROC HPSPLIT Features. 2 Cost-Complexity Pruning with Cross Validation. The next step is to write the model equation, which is done in lines 22 to 25 below. - Included data about race and incomeThe PRUNE statement controls pruning. ORDER = ordering. • PROC SGPLOT and PROC PRINT were used to make all graphs and table displays. 11 . I created a reproachable example below. The HPSPLIT procedure in SAS/STAT® software supports a WEIGHT statement. cars; class model; model enginesize = mpg_highway model; run; proc hpsplit data=sashelp. PROC HPSPLIT and ODS were used to create the Decision Tree display images. Percentage success in that branch rises to 89. Credits and Acknowledgments. PROC HPGENSELECT runs in either single-machine mode or distributed mode. Subsections: 15. The HPSPLIT procedure is a high-performance procedure that performs recursive partitioning for classification and regression. 4: Creating a Binary Classification Tree with Validation Data , which is shown in Figure 16. Only automated splitting is available in the HP Tree node / PROC HPSPLIT. 4 Programming Documentation |勾配ブースティング木（Gradient Boosting Tree）. SAS/STAT 15. , to create the sequence of values and the corresponding sequence of nested subtrees, . Documentation Example 2 for PROC HPSPLIT. This document explains the syntax, features, and examples of the HPSPLIT procedure. sas. Introduction to Statistical Modeling with SAS/STAT Software. 01. For more information about these mappings, see the section Levelization of Classification Variables in SAS/STAT 14. I was planning to run a bunch of bootstrap versions of the set through the procedure and record what the value it is splitting on for the single continuous predictor. First, PROC HPSPLIT finds the maximum RSS-based variable importance. id as. Accordingly to SAS Note 50555 the HPSPLIT procedure is first available as a stand-alone procedure in SAS/STAT 14. Then open a text box on the forum with the </> icon and paste the text. 5: Graphs Produced by PROC HPSPLIT. 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. The default is the number of target levels. However, the HPSPLIT procedure provides methods for incorporating missing values in the analysis, as explained in the sections Handling Missing Values and Primary and Surrogate Splitting Rules. The pros and cons of (1) and (2) are not discussed in this paper. First and last five observations from PROC CONTENTS in the order of variables in the dataset. The table below is generated from the lift table macro. I have specified the EVENT= option in the MODEL statement, which. Examples: HPSPLIT Procedure. It uses the mortgage application data set HMEQ in the Sample Library, which is described in the Getting Started example in section Getting Started: HPSPLIT Procedure. Documentation Example 1 for PROC HPSPLIT /**/ proc print. For single-machine mode, the table displays the number of threads used. The p-values for the final split determine. By default, PROC HPSPLIT first tries to find candidates for splits by using the exhaustive method. 61. 2. 1 User's Guide: High-Performance Procedures documentation. Question 6 1 / 1 pts In SAS Studio, the procedure _____ can be used to build a decision tree model. SAS is headed back to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user. But when I try to run it under the SAS University Edition, it doesn't work: Proc hpsplit seems not to be available in the SAS University Edition. SAS/STAT 15. ( Remove variables that have missing. 6 Applying Breiman’s 1-SE Rule with Misclassification Rate. Getting Started; Syntax. This example creates a tree model and saves a node rules representation of the model in a file. 0 Likes. Getting Started; Syntax. maxdepth = 6 /* pythonで. . 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. 2 Cost-Complexity Pruning with Cross Validation. This table shows that that model adequately separated the positive and negative observations. USEFUL OPTIONS IN PROC HPFOREST . PDF EPUB Feedback. This content is presented in an iframe, which your browser does not support. 0038, which corresponds to a subtree with seven leaves. cars; target origin / level=nominal; input msrp cylinders length wheelbase mpg_city mpg_highway invoice weight horsepower / level=interval; input enginesize / level=ordinal; input drivetrain type / level=nominal; output nodestats=nstat; run; proc sql; create view treedata as select a. NOTE: The HPSPLIT procedure is executing in single-machine mode. sas. This table shows that that model adequately separated the positive and negative observations. FedSQL Programming . SAS/STAT 14. If any variables are character or to be treated as categorical, at least one CLASS statement is required. If you specify a validation set by using a PARTITION statement, PROC HPSPLIT uses the validation set for subtree selection. PROC HPSPLIT Features. Here we specify seed to be a certain number seed = [CONSTANT]so that the result will be reproducible. 5: Graphs Produced by PROC HPSPLIT ODS Graph Name PROC HPSPLIT is the procedure in SAS to fit decision tree. 16. Table 61. Is there any alternate proc or code available that can help create decisionAlas, PROC SPLIT does not produce PMML has has no conveniences to help generate it. Output 16. Documentation Example 3 for PROC HPSPLIT. SAS/STAT 15. Similarly, the surrogate count counts the number of times a. documentation. 1 User's Guide documentation. sas. 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. View more in. Solved: the macro for binning of decision tree function included in sas is below: %macro en(); data test_num; set mywork. PROC HPSPLIT in SAS9. 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. PROC HPSPLIT bins continuous predictors to a fixed bin size. comThe DTREE Procedure Overview The DTREE procedure in SAS/OR software is an interactive procedure for decision analysis. 1 User's Guide: High-Performance Procedures. Note: All class levels are padded or truncated to 32 characters. This is performed either by using the validation partition. The PROC HPLOGISTIC statement invokes the procedure. Output 16. In complex trees, you will not be able to reasonably see the entire tree in one plot without losing many details. on a server (SASApp) I get different results. Problem Note 59256: The WEIGHT statement in the HPSPLIT procedure was omitted from the documentation. Example 61. My code is the following: proc hpsplit data = &lib. The data are measurements of 13 chemical attributes for 178 samples of wine. 61. 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. By default, a binary logistic model is fit to a binary response variable, and an ordinal logistic model is fit to a multinomial response variable. PROC ARBOR superseded PROC SPLIT around 2002. With the first approach, you can use the OUTPUT statement to score the training data. I am trying to make a data tree. When creating your Proc HPSPLIT call, every binary, ordinal, nominal variable should be listed in the class statement (HPSPLIT doesn't actually distinquish between nominal and ordinal). PROC DISCRIM (K-nearest-neighbor discriminant analysis) –Dr. The text box is important to preserve text formatting of any diagnostics that SAS places in the log. The HPSPLIT Procedure. At the end of it, the instructor used Proc access to combined multiple model and compared them using the ROC chart above. specifies the sort order for the levels of classification variables. The following SAS program is a basic example of programming with SAS and Jupyter Notebook. Note: For. sas. TARGET [RESPONSE]: here we plug in a single response variable. CIND 119 Assignment1 Student: Lexie Tai ID: 501071793 Q1a proc import out = breastinfo datafile= "V:Lab 1reast_cancer_dataset. 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. SAS® Help Center. You can also use the ODS EXCLUDE statement to suppress some. Re: Drawing a decision tree from HPSPLIT. The pros and cons of (1) and (2) are not discussed in this paper. What's the cardinality of the input variable "mths_since_last_delinq"? In other words, how many distinct levels (distinct values) does it have? You can find out with PROC FREQ or PROC SQL or PROC CARDINALITY (latter procedure only exists in. 4: Creating a Binary Classification Tree with Validation Data , which is shown in Figure 61. Details. Key and uncommon options on PROC HPSPLIT include NODES which prints a table of each node of the tree. 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. implement the CHAID algorithm: SI-CHAID and HPSPLIT. The following two programs are equivalent. The model will run, but the output is not what I expected. The output code file will enable us to apply the model to our unseen bank_test data set. SAS/STAT User’s Guide documentation. sas. options noxwait noxsync xmin; %sysexec start "Preview output" "%sysfunc (pathname (WORK))\temp. We are using the PROC SURVEYSELECT procedure which is used to perform stratified random sampling on the sorted dataset heart. Usually, the purpose of scoring a training data set is to diagnose the model. This works and my codes so far are as following: %macro DTStudy (maxbranch=2, maxdepth=5, minleafsize=20); %let branchTries = %sysfunc(countw(&maxbran. Show LOG from the run you made where it "couldn't split". PROC HPSPLIT is run in the next step: ods graphics on; proc hpsplit data=Wine seed=15531 cvcc; ods select CrossValidationValues CrossValidationASEPlot; ods output CrossValidationValues=p; class Cultivar; model Cultivar = Alcohol Malic Ash Alkan Mg TotPhen Flav NFPhen Cyanins Color Hue ODRatio Proline; grow entropy; prune costcomplexity; run; Doubly confusing because testing the same proc hpsplit on a different machine (SAS server installation using EG 5. 1. execution mode: single mode, number of threads:2. I don't know what you mean by " multiple discriminant analysis in SAS". This is the main function of the pROC package. You can also find links to the syntax and output of the HPSPLIT procedure. 3 Creating a Regression Tree. 1 Building a Classification Tree for a Binary Outcome. Hello everyone, I am trying to use SAS Code node with proc hpsplit to achieve hyperparameter-tuning of decision trees in SAS Enterprise Miner. I've done something similar with CART with Proc HPSPLIT, but I couldn't find a similar way to do it for Random Forests. NOTE: Distributed mode requires SAS High-Performance Statistics. Enter terms to search videos. More specifically, I am looking to build a model that intuitively and logically splits numerical variables instead of randomly computer generated values i. The opposite is: ODS TRACE OFF; Koen. (View the complete code for this example . Dissatisfied. I have already created a partition in my data, which I will use to separate my data into training and testing. 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. The second line uses the proc hpsplit command and sets the random seed for reproducibility. Is there a way that the PROC HPSPLIT can return me with a complete decision tree? proc hpsplit data=data. The sections Splitting Criteria and Splitting Strategy provide details about the splitting methods available in the HPSPLIT procedure. 2018. Getting Started: HPSPLIT Procedure. The ALPHA= option in the PROC HPSPLIT statement (default of 0. Each wine is derived from one of three cultivars that are grown in the same area of Italy, and the goal of the analysis is a model that. Output. 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. DATA Step Programming . PROC HPSPLIT Features. This column shows the probability of a. 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. This is performed either by using the validation partition. The procedure interprets a decision problem represented in SAS data sets, finds the optimal decisions, and plots on a line printer or a graphics device the deci-sion tree showing the optimal decisions. Then it selects the requested number of surrogate-split variables based on the agreement, in order of agreement. SAS/STAT 15. 16. PROC HPSPLIT is the procedure in SAS to fit decision tree. 8 See SAS documentation about PROC HPSPLIT for a decision tree procedure. Overview. The paper reviews the key concepts of each approach and illustrates the syntax and output of each procedure with a basic example. wagesdata seed=15531; class salary city studied_area; model salary = city studied_area; grow entropy; prune costcomplexity; run; I used. 3 Creating a Regression Tree. NOTE: PROCEDURE HPSPLIT used (Total process time): real time 0. Area under the curve (AUC) is defined as the area under the receiver operating characteristic (ROC) curve. 566. 5 Assessing Variable Importance. PROC HPSPLIT uses weakest-link pruning, as described by Breiman et al. If any variables are character or to be treated as categorical, at least one CLASS statement is required. Special SAS Data Sets. Perform search. This is an entirely new procedure for me and it's a little daunting. 4, local server) does not display expected ODS output - it only shows 'PerformanceInfo' and 'DataAccessInfo tables. You can use the PLOTS= option in the PROC HPSPLIT statement to control which nodes are displayed. I have the original data set (which is the above data prior to this bit of code). This is performed either by using the validation partition. . 1, which corresponds to SAS 9. So far I can think only of listing all colors that I'd like to use, via goptions, colors=(). ods graphics on; proc hpsplit data=sashelp. 18 4670 Chapter 62: The HPSPLIT Procedure MAXDEPTH=number specifies the maximum depth of the tree to be grown. Each wine is derived from one of three cultivars that are grown in the same area of Italy, and the goal of the analysis is a model that classifies samples into cultivar. 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): real time 0. The answer here is to fully qualify your path name. The default is the most recently created data set. The SASLOG was shown as follows: NOTE: The HPSPLIT procedure is executing in single-machine mode. cars; target origin / level=nominal; input msrp cylinders length wheelbase mpg_city mpg_highway invoice weight horsepower / level=interval; input enginesize / level=ordinal; input drivetrain type / level=nominal; output nodestats=nstat; run; proc sql; create view treedata as select a. (SAS Institute, 2016) Python is a free, open-source software programming environment commonly used in web and internet development, scientific and numeric computing, and software and game development. Pick the Names you want and put them in your ODS SELECT open-code statement before PROC HPSPLIT. Finding the optimal subtree from this sequence is then a question of determining the optimal value of the complexity parameter . Regression trees model a target. HPSPLIT in SASPy. PROC HPSPLIT tries to create this number of children unless it is impossible (for example, if a split variable does not have enough levels). The first step in the analysis is to run PROC HPSPLIT to identify the best subtree model: ods graphics on; proc hpsplit data=sampsio. In complex trees, you will not. is the sensitivity value at leaf . 0 Likes. 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. seed = an initial value from which a random number function or. The resulting confusion matrix is below. In k-fold cross-validation (used in HPSPLIT) the data have to be split in k distinct sets with (about) equal n° of observations. 2) to run exhaustive CHAID. It is calculated in two steps. The count-based variable importance simply counts the number of times in the tree that a particular variable is used in a split. The code requests the displayed Tree to have a depth of 5 beginning from node "3": proc hpsplit data=x. 16. I have almost zero working knowledge of ODS but got as far as locating the reference below:North American Feebate Analysis Model. . FLAG=p. 4. 2 Cost-Complexity Pruning with Cross Validation. documentation. Hello , You are having enough observations ( # 44249 ). The NAFAM is a static model, and as such, the model results presented in this chapter represent long-run equilibrium solutions 10 to 15 years in the future, when all manufacturers have had the. If any variables are character or to be treated as categorical, at least one CLASS statement is required. ) Maybe not a viable option. I've tried changing various options in the hpsplit procedure itself to no avail. I added an ID variable to the data set provided by SAS (this will be useful later): data new; set sashelp. Variables that appear after the equal sign (=) in the MODEL statement are explanatory variables that model the response variable. 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. ”. System Options. Getting Started; Syntax. cars; target enginesize / level=int; input mpg_highway model; run;SAS provides birthweight data that is useful for illustrating PROC HPSPLIT. This is an entirely new procedure for me and it's a little daunting. The HPSPLIT Procedure. The text box is important to preserve text formatting of any diagnostics that SAS places in the log. Finding the optimal subtree from this sequence is then a question of determining the optimal value of the complexity parameter . I am using HPSPLIT and working with very highly imbalanced database (3% had "event"). Enter terms to search videos. Hello , That's very weird. cars; target enginesize / level=int; input mpg_highway model; run;HPSPLIT and rare events. This option controls the number of bins and thereby also the size of the bins. It displays information about the execution mode. id as. However, information about the WEIGHT statement was omitted from the documentation. The code below specifies how to build a decision tree in SAS. 22603: Producing an actual-by-predicted table (confusion matrix) for a multinomial response. 1 x64), all expected ODS results do appear. Hi there, I ran the proc hpsplit command on my PC for a dataset and only the performance and data access information results were displayed. What’s New in SAS/STAT 15. 16. 187 views. This option controls the number of bins and thereby also the size of the bins. Problem with PROC RANK. Go to the Downloads tab of this note to obtain updated information. SUBSCRIBE TO THE SAS SOFTWARE YOUTUBE CHANNELERROR: Character variable appeared on the MODEL statement without appearing on a CLASS statement. If you specify COMPUTEQUANTILE, PROC HPBIN generates the quantiles and extremes table, which contains the following percentages: 0% (Min), 1%,. If the number of computations exceeds the number that you specify in the LEVTHRESH1= or LEVTHRESH2= option, the procedure switches to the greedy algorithm. 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. ( I don't know about the exact value of k in HPSPLIT. Copy the text for the entire Proc HPSPLIT plus any notes, warnings or other messages. Subsections: 16. Pick the Names you want and put them in your ODS SELECT open-code statement before PROC HPSPLIT. What’s New in SAS/STAT 15. Do you have any additional comments or suggestions regarding SAS documentation in general that will help us better serve you? PDF. The main features of the HPSPLIT procedure are as follows: provides a variety of methods of splitting nodes, including criteria based on impurity (entropy, Gini index, residual sum of squares) and criteria based on statistical tests (chi-square, F test, CHAID, FastCHAID) SAS provides birthweight data that is useful for illustrating PROC HPSPLIT. The kernel makes SAS the analytical engine or “calculator” for data analysis. The KRIGE2D Procedure. To illustrate the process, consider the first two splits for the classification tree in Example 61. 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. /* SAS uses a different method than. Variable importance is based on how the variables are used in the pruned tree. MAXDEPTH= number. The HPSPLIT procedure provides two types of criteria for splitting a parent node : criteria that maximize a decrease in node impurity,. It builds a ROC curve and returns a “roc” object, a list of class “roc”. BASEBALL. Getting Started; Syntax. 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. The variables are the city where he get his degree, the studied area and his actual salary. hmeq maxdepth=7 maxbranch=2; target BAD; input DELINQ DEROG JOB NINQ REASON / level=nom;Very Dissatisfied. RANDOM FOREST – THE HIGH-PERFORMANCE PROCEDURE The SAS® code below calls the High-Performance Random Forest procedure, PROC HPFOREST. Answer: SAS command: proc import out =breast_cancer_dataset datafile = "V:Assignmentreast_cancer_dataset. 2. seed = an initial value from which a random number function or CALL routine calculates a random value. 3: Detailed Tree Diagram. Base SAS Procedures . When creating your Proc HPSPLIT call, every binary, ordinal, nominal variable should be listed in the class statement (HPSPLIT doesn't actually distinquish between nominal and ordinal). 01 seconds cpu time 0. The data are measurements of 13 chemical attributes for 178 samples of wine. It has five different syntaxes: one for C4. If you specify the number of leaves by using the LEAVES= option, the. The RsquareV macro provides the R 2 V statistic proposed by Zhang (2017) for use with any model based on a distribution with a well-defined variance function. PROC HPSPLIT runs in either single-machine mode or distributed mode. None of the very low BW babies are correctly classified, and less than 2% of the low BW babies are. Solved: Hey All I know that proc hpsplit isn't available in SAS Studio. PROC ARBOR superseded PROC SPLIT around 2002. csv" dbms =csv replace; getnames =yes; proc. The data are measurements of 13 chemical attributes for 178 samples of wine. Enter terms to search videos. 4 Creating a Binary Classification Tree with Validation Data. For predict model, most used is. sas. ERROR: Unable to create a usable predictor variable set. 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. 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; And here is the log with error:You can use the code generated to bin your data. 1) proc logistic. 4 (TS1M1) using PROC HPSPLIT. PROC HPSPLIT Statement CLASS Statement CODE Statement GROW Statement ID Statement MODEL Statement OUTPUT Statement PARTITION Statement PERFORMANCE Statement PRUNE Statement RULES Statement.