This variable is useful for matching BY groups with macro variables that PROC GLMSELECT creates. GENMOD fits the "generalized linear model" which allows for any response distribution in a family of distributions and it models a function (the "link" function) of the response mean. For more information, see Chapter 56, “The GLMSELECT Procedure. IMPORT; class gender(ref='female') pepper discipline; model quality = gender numYears pepper discipline easiness raterInterest / selection=none; run; Note that you can also do this with prox mixed. For this specific purpose, the. You can name the fractions of the data that you want to reserve as test data and validation data. Hi there, I would like to persist the model (formula) produced by proc glmselect like so: PROC GLMSELECT DATA = WORK. My thought is to use PROC GLMSELECT to use k fold. 49. . The EFFECTPLOT statement is a hidden gem in SAS/STAT software that deserves more recognition. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. GENMOD fits the. GLMSELECT focuses on the standard independently and identically distributed general linear model for univariate responses and offers great flexibility for and insight into the model selection algorithm. Leutrain plots=coefficients;proc glmselect data = analysisData testdata = testData seed = 1 plots (stepAxis = number) = all; partition fraction. The procedure offers options for customizing the selection with a wide variety of selection and stopping criteria. The procedure also provides graphical summaries of the selected search. Examples of megamodels arising in genomic data analysis and nonparametric modeling are discussed. If you specify a VALDATA= data set in the PROC GLMSELECT statement, then you cannot also specify the VALIDATE= suboption in the PARTITION statement. ORDINAL LOGISTIC REGRESSION THE MODEL As noted, ordinal logistic regression refers to the case where the DV has an order; the multinomial case is covered. For example, if the number of observations in the data set is 100, then the following two PROC GLMSELECT steps are. The GLMSELECT procedure offers extensive capabilities for customizing the. The first call writes the design matrix that PROC GLM uses (internally) for the default reference levels. Sorted by: 3. Currently loaded videos are 1 through 15 of 15 total videos. This process results in valid statistical inferences that properly reflect the uncertainty due to missing values; for example, valid confidenceAs stated in the documentation, "PROC GLMSELECT provides results (displayed tables, output data sets, and macro variables) that make it easy to take the selected model and explore it in more detail in a subsequent procedure such as REG or GLM. Compared with the LASSO method, the elastic net method can select more variables, and the number of selected. The MODELAVERAGE statement in PROC GLMSELECT is intended for when you use variable-selection methods to choose effects in a linear regression model. 1 Answer. 0001 Bla Bla 1 -4. The %Marginal macro takes as input an output SAS data set. . For example, the following call to PROC GLMSELECT specifies several model effects by using the "stars and bars" syntax: The syntax Group | x includes the classification effect (Group), a linear effect (x), and an interaction effect (Group*x). EFFECT MyPoly=POLYNOMIAL (x1 x2/degree=4 MDEGREE=2); generates the terms , , , , ,, and . In this example, model selection that uses other information criteria and out-of-sample prediction. . It also demonstrates several features of the OUTDESIGN= option in the PROC GLMSELECT statement. 129965 -38. (View the complete code for this example . (Others include PROC CATMOD and PROC GLMSELECT. ; run; Let’s look at the data. In addressing these examples, built-in facilities of the procedure to handle validation and test data are highlighted in addition to techniquesThe PROC GLMSELECT statement invokes the procedure. e. 877694553 0. The use of the WHERE clause in the. In the first step of the selection process, either A or B can enter the model. 4 Multimember Effects and the Design Matrix. 1: Modeling Baseball Salaries Using Performance Statistics. But I also need to use the fitted model to make prediction on testing dataset. Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. documentation. A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. Since the variation of salaries is much greater for the higher. In that example, the default. class; if mod(_n_, 3) > 0 then role = "training"; else role = "test"; run; proc glmselect data=splitclass; class sex; model weight = sex height / selection=none; partition rolevar=role(test="test" train="training"); output out=outClass. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. In the examples, both entry model (&SLENTRY) and depart model (&SLSTAY) significant level are 0. Since the variation of salaries is much greater for the higher salaries, it is appropriate to apply a log transformation to the. specifies that, at most, the first n characters of a CLASS variable label be used in creating labels for the corresponding design variables. . In the following statements, the OUTDESIGN option of the GLMSELECT procedure generates the design matrix. data salary; input salary age educ pol$ @@; datalines; 38 25 4 D 45 27 4 R 28 26 4 O 55 39 4 D 74 42 4 R 43 41 4 OWith the same VALDATA= data set named in the PROC GLMSELECT statement as in the LASSO example, the minimum of the validation ASE occurs at step 105, and hence the model at this step is selected, resulting in 54 selected effects. You either need to take out the interaction term (s) with missing data cell, or maybe combine your data categories to get rid of missing data cells. For example, see the GLMSELECT documentation example, which is similar to the following: ods graphics on; proc glmselect data=sashelp. First, I ran: proc glmselect data=sashelp. This option applies only when. "One"of"these" models,"f(x),is"the"“true”"or"“generating”"model. . You can use a SAS autocall macro, %Marginal, to display marginal model plots. This example shows how you can use both test set and cross validation to monitor and control variable selection. Example 49. The following statements are available in the GLMSELECT procedure: All statements other than the MODEL statement are optional and multiple SCORE statements can be used. 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 analysis, with numerous examples in addition. selection=stepwise (select=SL SLE=0. See the section Macro Variables Containing Selected Models for details. The example also uses k -fold external cross validation as a criterion in the CHOOSE= option to choose the best model based on the penalized regression fit. Documentation Example 1 for PROC CLUSTER. . This example illustrates how you can use PROC HPGENSELECT to perform Poisson regression for count data. Choose PROC GLMSELECT for “large p” problems and choose PROC REG for smaller numbers of predictors, e. It is common in this graph for several coefficients to have similar values in the final model. Elastic Net # Observations (Training sample) 38: 38 # Variables: 7129. Proc Logistic, and %StepSvyreg vs. If I use: /selection=none stb showpvalues; as option for proc glmselect I get: Effect Parameter DF Estimate StandardizedEst StdErr tValue Probt Intercept Intercept 1 9. BY Statement. In addressing these examples, built-in facilities of the procedure to handle validation and test data are highlighted in addition to techniques The PROC GLMSELECT statement invokes the procedure. Dep Mean, the sample mean of the dependent variable . Can you please provide some code example? This is a code example, which does not work: proc GLMSELECT data=sashelp. Many of these options and syntax are shared with other procedures, such as proc glmselect and proc reg. With two outliers (example 5), the parameter estimate was reduced to 0. We also have basline data on their demographics. It illustrates how you can use the experimental EFFECT statement to generate a large collection of B-spline basis functions from which a subset is selected to fit scatter plot data. . baseball; proc contents varnum data=baseball;But PROC GLMMOD is not the only way to generate design matrices in SAS. 7. Proc Logistic, and %StepSvyreg vs. It also demonstrates several features of the OUTDESIGN= option in the PROC GLMSELECT statement. I recommend that you switch to PROC GLMSELECT, which has many more variable selection techniques and also provides many more diagnostic tables and graphs. But, there are quite big difference in how the two procedure works. 1 Modeling Baseball Salaries Using Performance Statistics. There are 1,000,000 observations in the data set, and the response yPoisson is a Poisson variable with a mean that depends on 20 of the 100 regressors. The HPGENSELECT Procedure. You can also specify criteria based on validation; this. 941651 -0. With the same VALDATA= data set named in the PROC GLMSELECT statement as in the LASSO example, the minimum of the validation ASE occurs at step 105, and hence the model at this step is selected, resulting in 54 selected effects. PROC GLMSELECT fits an ordinary regression model. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. Proc genmod use numerical methods to maximize the likelihood functions. PROC GLMSELECT supports several criteria that you can use for this purpose. . . keyword <=name> specifies the statistics to include in the output data set and optionally names the new variables that contain the statistics. [1] PROC GLMSELECT provides the most modern and flexible options for model selection. 15); run; • GLMSELECT procedure • REG procedure ①CLASSステートメントが 利用可能 ②交互作用項を含む 変数選択. PROC GLMSELECT assigns a name to each graph it creates using ODS. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. It illustrates how you can use the experimental EFFECT statement to generate a large collection of B-spline basis functions from which a subset is selected to fit scatter plot data. In the first step of the selection process, either A or B can enter the model. Example 42. Then the OUTDESIGN= option on the PROC GLMSELECT statement writes the spline effects to the Splines data set. This list can be used, for example, in the model statement of a subsequent procedure. 1 Modeling Baseball Salaries Using Performance Statistics. proc glm data = "c: emphsb2"; class female prog; model. If you were to sample from the distribution of Y but discard values less than (greater than) C, the distribution of the remaining observations would be. You can find further discussion and formula for these criteria in the PROC GLMSELECT documentation. The following sections describe the ODS graphical displays produced by PROC GLMSELECT. 8 Effect Selection Options in the documentation. 0001 Bla Bla 1 -4. Using binary responses in PROC GLMSELECT is not truly a logistic regression. Read Less. This. . Examples. Model_Fit "Parameter Estimates" =. However, beginning with SAS 9. The original data came from a weekly diary study of about 400 people. For. proc logistic has a few different variable selection methods that can be specified in the model statement. The HPFMM Procedure. For example, consider the data shown inFigure 2, where the variance of Y increases with X. GLM does not have a selection procedure. Ideally, a priori knowledge should be used to decide. Q&A for work. This list can be used, for example, in the model statement. I used the example in the SAS/STAT 13. sas. For the reference level, all three dummy variables have a value of . Output 44. . Example 1. Example 1. . 3789 Example 47. 1, to incorporate a categorical covariate into the model, the user must first create indicator variables. – JJFord3. For more information,. The focus of this example is to show how you use the LASSO method and how you can switch the modes of execution of PROC HPGENSELECT. . Another example is the MCMC procedure, whose documentation includes an example that creates a design matrix for a Bayesian regression model . For example, if you generate all pairwise quadratic interactions of N continuous variables, you obtain "N choose 2" or N*(N-1). Conclusion. 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. Usage Note 22605: Assessing the relative importance of effects in generalized linear models. Note that no students received a score of 200 (i. 08. Notice how PROC GLMSELECT handles the missing value in the third observation: because the X1 value is missing, the procedure puts a missing value into all interaction effects. Information on the tables will be written to the log. The following sections describe the ODS graphical. Example 42. 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. Say your input effect list consists of x1-x10. The data give the scores of students on a reading comprehension test. It also demonstrates the use of split classification variables. Usage Note 22590: Obtaining standardized regression coefficients in PROC GLM. You can use the MODELAVERAGE statement in PROC GLMSELECT to perform a basic bootstrap analysis. (PROC GLMSELECT) on SASHELP. The following statements provide. This section provides an example of using splines in PROC GLMSELECT to fit a GLM regression model. Examples include the GLMMIX, GLMSELECT, LOGISTIC, QUANTREG, and ROBUSTREG procedures. Dennis Fisher Dennis G. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. Learn more at PROC GLMSELECT supports several criteria that you can use for this purpose. If you specify more than one BY statement, only the last one specified is used. Learn about SAS Training - Statistical Analysis path If you do not specify either the STOP= or SELECT= option, then the default is STOP=SBC. The GLMSELECT Procedure. The GLMSELECT procedure supports a variety of model selection methods for general linear models. Elastic Net Coefficient. Share LASSO Selection with PROC GLMSELECT on LinkedIn ; Read More. This algorithm for SELECTION=LASSO is used in PROC GLMSELECT. One example can be seen in the boxplot below, where different bluebook distributions by car type can. Apply each bootstrap-sample-derived model to the original sample dataset, and measure the performance metric. You request the criterion panel by specifying the PLOTS=CRITERIA option in the PROC GLMSELECT statement. If you specify the VAR=SAMPLE option for COMMONRISKDIFF(TEST=MR), PROC FREQ uses the sample variance estimateDATA=SAS data set names the data set to be scored. 2 (or downloaded from SAS Web site)*/ proc glmselect data=Remission; model remiss=cell smear infil li blast temp v1-v10/selection=lasso; quit;LOGISTIC, PROC GENMOD, PROC GLMSELECT, PROC PHREG, PROC SURVEYLOGISTIC, and PROC SURVEYPHREG) allow different parameterizations of the CLASS variables. The QUANTLIFE Procedure. The PRINQUAL Procedure. The following statements show how you can use PROC GLMSELECT to implement this strategy: proc glmselect data=dojoBumps; effect spl = spline(x / knotmethod=multiscale(endscale=8) split details); model bumpsWithNoise=spl; output out=out1 p=pBumps; run; proc sgplot data=out1; yaxis display=(nolabel); series x=x. Introduction to Power and Sample Size Analysis. The HPFMM Procedure. Notice how PROC GLMSELECT handles the missing value in the third observation: because the X1 value is missing, the procedure puts a missing value into all interaction effects. The second call writes the design matrix for. Getting Started;. . As an example for the remainder of the paper. 4. 1 and the significance level to stay is 0. The HPLOGISTIC Procedure. The example below illustrates how SAS language tools for iteration across groups in datasets can be used. All statements other than the MODEL statement are optional and multiple SCORE statements can be used. The SELECT. PROC GLMSELECT performs advanced model selection in the framework of. The example also uses k-fold external cross validation as a criterion in the CHOOSE= option to choose the best model based on the penalized regression fit. However, for problems that have more predictors or that use much more computationally intense CHOOSE= criterion, sure independence screening (SIS) can run faster by orders. The GLMSELECT procedure uses the keyword 'L1' instead of 'lambda' . The cross-validation method uses is leave-one-out, meaning the model is refitted N-1 number of times. A variety of model selection methods are available, including forward, backward, stepwise, the LASSO method of Tibshirani (), and the related least angle regression method of Efron et al. The PSMATCH Procedure. However, be aware that the procedures might ignore observations that have missing values for the variables in the model. Enter terms to search videos. The focus of this example is to show how you use the LASSO method and how you can switch the modes of execution of PROC HPGENSELECT. If you do not specify a label on the MODEL statement, then a default name such as MODEL1 is used. This list can be used, for example, in the model statement of a subsequent procedure. The following DATA step generates the data: If you do not specify either the STOP= or SELECT= option, then the default is STOP=SBC. From the sequence of models. An example is PROC REG, which does not support the CLASS statement, although for most regression analyses you can use PROC GLM or PROC GLMSELECT. A general linear model can be viewed as a linear combination of functions fi(x) of the predictors: f(x,θ) = f1(x)*θ1 +. It can be viewed as a stepwise procedure with a single addition. You can turn this into a macro variable to make generating dummies fast and simple. 5 Model Averaging. 35: 53. The CPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. Leutest plots = coefficients; model y = x1-x7129 / selection = elasticnet (steps = 120 L2 = 0. But with PROC GLMSELECT (unlike GLMMOD) you get the right (design-) variable names immediatly (no renaming needed)! ods html close; ods preferences; ods html; proc. PROC QUANTSELECT saves the list of selected effects in a macro variable, &_QRSIND. In this example, the YHat variable in the Pred data set contains the predicted values. The LPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. Backward Elimination (BACKWARD) The backward elimination technique starts from the full model including all independent effects. PROC GLMSELECT provides several methods for partitioning. This section provides some background about the LASSO method that you need in order to understand the group LASSO method. See the section Macro Variables Containing Selected Models for details. . If you have any query, feel free to ask in the. 941651 -0. The following code selects a model with the default settings:. This procedure supports a. In the standard stepwise method, no effect. Further, there can be differences in p-values as proc genmod use -2LogQ tests, and proc glm use F-tests. . The idea is to calculate stratified values for the bluebook that base on these variables. The procedure offers extensive capabilities for customizing the selection with a wide variety of selection and stopping. PROC GLMSELECT provides a variety of selection and stopping criteria. Say your input effect list consists of x1-x10 . We will introduce a numeric ROW variable that we can later use to merge the design matrix back with the input data. This example uses data from Cole and Grizzle to illustrate a commonly occurring repeated measures ANOVA design. Efron et al. The nonnumeric arguments that you can specify in the STOP= option are shown in Table 42. from %StepSvylog vs. The HPMIXED Procedure. Elastic net isn't supported quite yet. PS Answer: Look at the Data Step in the example you linked to. For more information, see Chapter 5, Introduction to Analysis of Variance Procedures, and Chapter 52, The GLM Procedure. junkmail maxtrees=1000 vars_to_try=10. ODS and Base Reporting. . . Alternatively, you can use the OUTDESIGN= option in PROC GLIMMIX. sas. Within each category of statistical analysis, the examples are grouped by the SAS/STAT procedure that is being demonstrated. The GLM Procedure：最小二乘法模型，包括回归、方差分析、协方差分析、多元方差分析、偏相关。 The GLMMOD Procedure：广义线性模型设计; The GLMPOWER Procedure：预测力和样本大小的. Note that many procedures (for example, PROC GLM, PROC MIXED, PROC GLIMMIX, and PROC LIFEREG) do not allow different parameterizations of. Three columns are created to indicate group membership of the nonreference levels. The EFFECT statement enables you to construct special collections of columns for design matrices. First page loaded, no previous page available. . 3 Scatter Plot Smoothing by Selecting Spline Functions This example shows how you can use model selection to perform scatter plot smoothing. ) and the ADAPTIVEREG procedure. . Then &_GLSIND would be set to x1 x3 x4 x10 if, for example, the first, third, fourth, and tenth effects were selected for the model. If you specify the WEIGHT statement, it must appear before the first RUN statement or it is. It also demonstrates several features of the OUTDESIGN= option in the PROC GLMSELECT statement. Direct comparisons between PROC REG and PROC GLMSELECT are made. In the standard stepwise method, no effect can enter the model if removing any effect currently in the model would yield an improved value of the selection criterion. For example, the following statements create and run a macro that uses PROC GLM to perform LSMeans analyses. Using the Output Delivery System. This example uses a microarray data set called the leukemia (LEU) data set (Golub et al. As shown in the example, the macro can be used in subsequent analyses. 1-15 of 17. The following example. . cuto (the default is 0. The GLMSELECT Procedure. References. During each week they reported on behaviours from their most recent sexual encounter. Random partition into training, validation, and testing dataFunda Gunes, in the Statistical Applications Department at SAS, presents LASSO Selection with PROC GLMSELECT. Then effects are deleted one by one until a stopping condition is satisfied. The following sections describe the ODS graphical displays produced by PROC GLMSELECT. This selection method is available in the GLMSELECT, LOGISTIC, PHREG, QUANTSELECT, and REG procedures. RANDOM FOREST – THE HIGH-PERFORMANCE PROCEDURE The SAS® code below calls the High-Performance Random Forest procedure, PROC HPFOREST. How can salary be predicted from performance? data baseball; set sashelp. Baseball data set that is described in the section Getting Started: GLMSELECT Procedure. In this example, model selection that uses other information criteria and out-of-sample prediction. Thanks. . The data in testData will be used for Testing. If you specify a TESTDATA= data set in the PROC GLMSELECT statement, then you cannot also specify the TEST= suboption in the PARTITION statement. Since the variation of salaries is much greater for the higher salaries, it is. It is the value of y when x = 0. SAS has a new procedure, PROC HPGENSELECT, which can implement the LASSO, a modern variable selection technique. For example, the statement. Examples focus on logistic regression using the LOGISTIC procedure, but these techniques can be readily extended to other procedures and statistical models. The following call to PROC GLMSELECT includes an EFFECT statement that generates a natural cubic spline basis using internal knots placed at specified percentiles of the data. Simple Linear Regression. A possible search term is "proc glmselect" outdesign site:. Example 42. sas. You can use the PROC GLMSELECT statement in SAS to select the best regression model based on a list of potential predictor variables. SAS/STAT 15. 2 Using Validation and Cross Validation. 2" KLL"distance"isa"way"of"conceptualizing"the"distance,"or"discrepancy,"between"two"models. It illustrates how you can use the experimental EFFECT statement to generate a large collection of B-spline basis functions from which a subset is selected to fit scatter plot data. CLASS and EFFECT statements, if present, must precede the MODEL statement. 5 Model Averaging. If STOP= n is specified, then PROC GLMSELECT stops selection at the first step for which the selected model has n effects. Value of ORDER= Levels Sorted By . The following sections describe the displayed output produced by PROC GLMSELECT. 2 Using Validation and Cross Validation. . Enter terms to search videos. 4 and SAS® Viya® 3. Suppose we want to fit a multiple linear regression model that uses (1) number of hours spent studying, (2) number of prep exams taken and (3) gender to predict the final exam score of students. Ideally, you would be able to run GLMSELECT once with elastic net to determine an optimal value of L2 to then plug into the model averaging. The GLMSELECT procedure is the best way to create a. Videos. PROC GLMSELECT Statement. When a WEIGHT statement is used, a weighted residual sum of squares. 1: Modeling Baseball Salaries Using Performance Statistics. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. The PARMDISTRIBUTION request in the PLOTS= option in the PROC GLMSELECT. Unfortunately, it doesn’t do “all subsets selection”, but it does forward, backward, and stepwise selection. 6 Elastic Net and External Cross Validation. LASSO. The HPLMIXED Procedure. , 1999 ), which is used in the paper by Zou and Hastie ( 2005 ) to demonstrate the performance of the. The horizontal direct product between matrices A and B is formed by the elementwise multiplication of their columns. You can now leverage these macro variables and the output data set created by PROC GLMSELECT to perform postselection analyses that match the selected models with the appropriate BY-group observations. The GLMSELECT procedure supports nonsingular parameterizations for classification effects. 1. BY Statement. 6 from the text. The MODEL statement in PROC GLMSELECT includes 18 independent variables, but the final LASSO model contains only seven variables. baseball plot=CriterionPanel;. PROC GLMSELECT creates a SAS item store that is called YourModel. Then &_GLSIND would be set to x1 x3 x4 x10 if, for example, the first, third, fourth, and tenth effects were selected for the model. The MODELAVERAGE. ODS Graph Names. . 44. . What is Proc MiAnalyze… “Multiple imputation does not attempt to estimate each missing value through simulated values, but rather to represent a random sample of the missing values. Details. selects effects to enter or drop as in the previous example except that the significance level for entry is now 0. This example treats the parameters that correspond to the same spline and CLASS variable as a group and also uses a collection effect to group otherwise unrelated parameters. 2. A variety of model selection methods are available, including the LASSO. Mathematical Optimization, Discrete-Event Simulation, and OR. The following DATA step contains 100 observations for a count response variable (Y), a continuous variable (Total) to be used in a later analysis, and five categorical variables (C1. The simulated data for this example describe a two-week summer tennis camp. . This example shows how you can use multimember effects to build predictive models. DIFFERENCES IN THE PROC SURVEYFREQ AND PROC FREQ CODE . 1 Modeling Baseball Salaries Using Performance Statistics. 4M63. In addition, you can use a collection effect to construct a group of three of the continuous effects, as shown in the following statements: proc glmselect data=traindata plots=coefficients; class c1-c5; effect s1=spline(x1); effect s2=collection(x2 x3 x4); model y = s1 s2 x5 c:/ selection=grouplasso(steps=20 choose=sbc rho=0. Examples: GLMSELECT Procedure. The GLMSELECT procedure supports a variety of model selection methods for general linear models. PROC GLMSELECT tries to thin labels to avoid conflicts. The following DATA step generates the data for this example. The following statements produce analysis and test data sets. The following sections describe the ODS graphical displays produced by PROC GLMSELECT. However, for problems that have more predictors or that use much more computationally intense CHOOSE= criterion, sure independence screening (SIS) can run. . The PROC GLM statement starts the GLM procedure. (Although, in this example, the item store is saved to your Work library, you can use a LIBNAME statement to save these item stores to permanent locations. 49. The following DATA step generates the data for this example. Students were taught using one of three teaching methods, called “basal,” “DRTA,” and “Strat. For example, specifying. sas. specifies the maximum degree of any variable in a term of the polynomial. 1 included in Base SAS 9.