proc glmselect example. Example include the "SELECT" procedures (GLMSELECT, QUANTSELECT, HPGENSELECT. proc glmselect example

 
Example include the "SELECT" procedures (GLMSELECT, QUANTSELECT, HPGENSELECTproc glmselect example  Random partition into training, validation, and testing data Funda Gunes, in the Statistical Applications Department at SAS, presents LASSO Selection with PROC GLMSELECT

The HPMIXED Procedure. The overall appearance of graphs is controlled by ODS styles. 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. ) The Sashelp. First we read in the data using a SAS® datastep (Figure 2). where Probt is a parameter's p-value. ODS and Base Reporting. 985494 0 0. . 3 Scatter Plot. 05. selection=stepwise. 1 Modeling Baseball Salaries Using Performance Statistics. It is worth noting that the label for the MODEL statement in PROC REG is used by PROC SCORE to name the predicted variable. Global Plot Option. categories. The following call to PROC GLMSELECT displays the standardized regression coefficients. . . For a reference to this trick see Hastie Tibshirani Friedman-Elements of statistical learning 2nd ed -2009 page 661 "Lasso regression can be applied to a two-class classifcation problem by coding the outcome +-1, and applying a cutoff. Both PROC GLMSELECT and PROC REG can do stepwise regression. And I'll. proc glmselect data=ex7Data; class c:; model y = x: c:/ selection=lasso; run; Output 49. 3789 Example 47. 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. The definitions used in PROC GLMSELECT changed between the experimental and the production release of the procedure in SAS 9. Although designed for PROC GLM models, it can also be used as a model selection tool for logistic regression Flom and Cassell (2009). . For example, you might decide to use an information criterion to decide what effects to include and when to terminate the selection process. The procedure also provides graphical summaries of the selected search. Examples. SAS/STAT. If STOP= n is specified, then PROC GLMSELECT stops selection at the first step for which the selected model has n effects. Getting Started. This value is used as the default confidence level for limits computed by the. Examples of multivariate regression analysis. cars, I get the same results as those you provide in your article. Predictive performance of candidate models on data not used in fitting the model is one approach supported by PROC GLMSELECT for addressing this problem (see the section Using Validation and Test Data). proc glmselect data = sashelp. . . keyword <=name> specifies the statistics to include in the output data set and optionally names the new variables that contain the statistics. ) and the ADAPTIVEREG procedure. the PARTITION statement in PROC HPLOGISTIC [26]) or cross-validation (e. For a future analysis, it uses the OUTDESIGN= option to create an output data set that contains the continuous variables in the model and the dummy variables for the categorical variable, Origin. This default matches the default method in PROC. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. . . It is common in this graph for several coefficients to have similar values in the final model. In your example, DAY is measured on a circular scale: DAY = 1 and DAY = 366 occupy the same position in an annual cycle. ALPHA=p. 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. This variable is useful for matching BY groups with macro variables that PROC GLMSELECT creates. 2 Using Validation and Cross Validation. y = yTrue + 3*rannor(2); run; proc glmselect data=simData; model y=x1-x10/selection=LASSO(adaptive stop=none choose=sbc); run; ods graphics on; proc glmselect data=simData seed=3 plots=(EffectSelectPct ParmDistribution); model y=x1-x10/selection=LASSO(adaptive stop=none choose=SBC);. 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. 269958 36. 2 Using Validation and Cross Validation. 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. PROC GLMSELECT provides more selection options and criteria than PROC REG, and PROC GLMSELECT also supports CLASS variables. This selection method is available in the GLMSELECT, LOGISTIC, PHREG, QUANTSELECT, and REG procedures. 44. As with the other selection methods supported by PROC GLMSELECT, you can specify a criterion to choose among the models at each step of the LASSO algorithm with the CHOOSE= option. . The following example shows how to use this statement in practice. RANDOM FOREST – THE HIGH-PERFORMANCE PROCEDURE The SAS® code below calls the High-Performance Random Forest procedure, PROC HPFOREST. Salary example in proc glm Model salary ($1000) as function of age in years, years post-high school education (educ), & political a liation (pol), pol = D for Democrat, pol = R for Republican, and pol = O for other. From the sequence of models. Example: (Baseball) This data set (from the SAS Help) contains salary (for 1987) and performance (1986 and some career) data for 322 MLB players who played at least one game in both 1986 and 1987 seasons, excluding pitchers. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. Subsections: 49. Documentation Example 2 for PROC CLUSTER. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. . . The graph shows how the coefficients change as new terms enter the model. For example, if you compute the skewness of a univariate sample, you get an estimate for the skewness of the population. 5. Figure 2 SAS® Datastep and NPAR1WAY Procedure Code. The tennis ability of. baseball; proc contents varnum data=baseball;The GLMSELECT procedure also provides extensive capabilities for customizing effect selection. The following procedures support the STORE statement: GEE, GENMOD, GLIMMIX, GLM, GLMSELECT,. . 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. . y: Dependent variable. . 25 validate=0. Suppose an internet service provider plans to conduct a customer satisfaction survey by selecting a random sample of customers from all current customers (the. SAS® 9. Base SAS Procedures . The tennis ability of each camper was assessed and ratings were assigned at the. The following statements provide. 22 User's Guide. GENMOD fits the. Examples include the GLMMIX, GLMSELECT, LOGISTIC, QUANTREG, and ROBUSTREG procedures. SAS will perform forward selection with a very large number. Compared with the LASSO method, the elastic net method can select more variables, and the number of selected. The following statements produce analysis and test data sets. SAS will perform forward selection with a very large number of variables GLMSELECT fits the "general linear model" that assumes that the response distribution is normal and it directly models the response mean. 0001 Bla Bla 1 -4. An example of the PLS procedure in SAS. brfss2;. 05 in SAS PROC LOGISTIC). For this specific purpose, the. The tennis ability of. 8 Group LASSO Selection. Deciding when to stop a selection method is a crucial issue in performing effect selection. SAS/STAT: PROC MIXED, PROC CORR, PROC REG, PROC GLMSELECT; SAS/GRAPH: PROC GCHART, PROC GPLOT, PROC G3D; Base SAS ODS (RTF, HTML, PDF) SAS/ACCESS: PC FILES – PROC IMPORT and PROC EXPORT . , the CVMETHOD= options in PROC GLMSELECT [25]), none appear to be available for bootstrap estimation of optimism as of SAS version 9. Example 1. . proc glmselect data=ex7Data; class c:; model y = x: c:/ selection=lasso; run; Output 49. For example, the following statements create and run a macro that uses PROC GLM to perform LSMeans analyses. The results of the two examples are shown in Table 3 to Table 6 in below. The example uses the macro on the MODEL statement of PROC GLM. This example uses simulated data that consist of observations from the model. 1 sls=0. You can use a SAS autocall macro, %Marginal, to display marginal model plots. The LPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. Note that no students received a score of 200 (i. There is a lot that you can do with PLS. The HPGENSELECT Procedure. In that example, the default stepwise selection method based on the SBC criterion was used to select a model. Note that in this dataset, the lowest value of apt is 352. Say your input effect list consists of x1-x10 . One example can be seen in the boxplot below, where different bluebook distributions by car type can. This example demonstrates the usefulness of effect selection when you suspect that interactions of effects are needed to explain the variation in your dependent variable. This list can be used in the MODEL statement of a subsequent procedure. 1. The GLMSELECT procedure supports the PARTITION statement, which enables you to fit the model on training data and assess the fit on validation data. At each step, the variable that is added is the one that most improves the fit of the model. . PROC GLMSELECT Statement. Example 42. In that example, the default stepwise selection method based on the SBC criterion was used to select a model. For more information, see Chapter 56, “The GLMSELECT Procedure. 4 Multimember Effects and the Design Matrix. PROC GLMSELECT creates a SAS item store that is called YourModel. carvalue(obs=10); var SequenceID policyno bluebook car_type car_use Car_Age_Months travtime; run; The Basic Idea of the Analysis . For selection criteria other than significance level, PROC GLMSELECT optionally supports a further modification in the stepwise method. Note that many procedures (for example, PROC GLM, PROC MIXED, PROC GLIMMIX, and PROC LIFEREG) do not allow different parameterizations of. (PROC GLMSELECT) on SASHELP. PROC GLMSELECT combines features from these two procedures to create a useful new model selection tool. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. 1 User's Guide documentation. The PROC GLMSELECT code for building t he regression model and also scoring the validation data is . Examples Modeling Baseball Salaries Using Performance Statistics Using Validation and Cross Validation Scatter Plot Smoothing by Selecting Spline Functions Multimember Effects and the Design Matrix Model Averaging. . The tennis ability of. GLMSELECT treats a class variable as a single multi-degree of freedom test for inclusion/exclusion. EXAMPLE USING PROC NPAR1WAY in SAS® Now that we have investigated the K-S two sample test manually, let us demonstrate how easily the example presented in (Table 1) [8] can be handled using the SAS® procedure NPAR1WAY. EFFECT MyPoly=POLYNOMIAL (x1 x2/degree=4 MDEGREE=2); generates the terms , , , , ,, and . Hi there, I would like to persist the model (formula) produced by proc glmselect like so: PROC GLMSELECT DATA = WORK. This example shows how you can use both test set and cross validation to monitor and control variable selection. . For example, if the number of observations in the data set is 100, then the following two PROC GLMSELECT steps are mathematically equivalent, but the second step is computed much more efficiently:. 49. heart out=heart; by sex; run; /* Run the parameter selection procedure and capture the selections with ODS */ proc glmselect data=heart; by sex; model weight = ageAtStart height / selection=lasso; ods output selectedEffects=se; run; /* define a macro for each. Most of those are better explained in the LOGISTIC regression procedure so maybe finding some good example of that is an easier starting point? @tpakhomova wrote: I am using PROC GLMSELECT for a multiple linear regression model that has categorical variables, which have more than 2 levels, as explanatory variables. Another example is the MCMC procedure, whose documentation includes an example that creates a design matrix for a Bayesian regression model . 2. 99 <. Usage Note 22590: Obtaining standardized regression coefficients in PROC GLM. 2 Using Validation and Cross Validation. Summary of the EFFECTPLOT statement. Connect and share knowledge within a single location that is structured and easy to search. Features. If you have requested n -fold cross validation by requesting CHOOSE= CV, SELECT= CV, or STOP= CV in the MODEL statement, then a variable _CVINDEX_ is. All I have done using proc glm so far is to output parameter estimates and predicted values on training datasets. During each week they reported on behaviours from their most recent sexual encounter. EXAMPLE The following example uses simulated data to illustrate how you can use PROC GLMSELECT in model development and exploit its facilities to avoid some of the pitfalls of traditional implementations of variable selection methods. 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. Finally,. [1] PROC GLMSELECT provides the most modern and flexible options for model selection. You can write the group LASSO method in the equivalent Lagrangian form, which is an example. Subsections: 49. 3 Scatter Plot Smoothing by Selecting Spline Functions. Statistical Analysis CategoriesFor example: ods graphics on; proc plm plots=all; lsmeans a/diff; run; ods graphics off; For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS. The CPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. . The outcome is a binary yes/no response, so I would like to end with a logistic regression model. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. 05: proc glmselect data = evals;The GLMSELECT Procedure. When a BY statement appears, the procedure expects the input data set to be sorted in order of the BY variables. A variety of model selection methods are available, including the LASSO. . . 05. cars; model msrp = Cylinders EngineSize Horsepower Length MPG_City MPG_Highway Weight Wheelbase; store work. as option for proc glmselect I get: Effect Parameter DF Estimate StandardizedEst StdErr tValue Probt Intercept Intercept 1 9. I recommend that you switch to PROC GLMSELECT, which has many more variable selection techniques and also provides many more diagnostic tables and graphs. CLASS and EFFECT statements, if present, must precede the MODEL statement. ods trace on; proc hpforest data=sashelp. Proc Logistic, and %StepSvyreg vs. Proc Glmselect under three scenarios: forward, backward, stepwise. The PROBIT Procedure. 2" KLL"distance"isa"way"of"conceptualizing"the"distance,"or"discrepancy,"between"two"models. The HPLMIXED Procedure. For. We used the defaults in stepwise, which are a entry level and stay level of 0. Re-create the model that was built in the previous practice with a few changes. This example uses a microarray data set called the leukemia (LEU) data set (Golub et al. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. For more information on permanent SAS data sets, refer to the section "SAS Files" in SAS Language Reference: Concepts. After settling on a final model, it is often desirable to assess of the relative importance of the predictors in the model. Videos. This example uses a microarray data set called the leukemia (LEU) data set (Golub et al. How can salary be predicted from performance? data baseball; set sashelp. Examples of tobit analysis. 49. For selection criteria other than significance level, PROC GLMSELECT optionally supports a further modification in the stepwise method. (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. They provide a Stepwise Selection example that shows. . 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. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. The default is the degree of the specified polynomial. CLASS and EFFECT statements, if present, must. where is the residual and is the leverage of the ith observation. Q&A for work. • Proc GLMSelect – LASSO – Elastic Net • Proc HPreg – High Performance for linear regression with variable selection (lots of options, including LAR, LASSO, adaptive. The following statements produce analysis and test data sets. Elastic Net # Observations (Training sample) 38: 38 # Variables: 7129. This variable is useful for matching BY groups with macro variables that PROC GLMSELECT creates. com. 12 weeks of observation. Example 44. . The definitions used in PROC GLMSELECT changed between the experimental and the production release of the procedure in SAS 9. The second call writes the design matrix for. Documentation Example 4 for PROC CLUSTER. PROC GLMSELECT creates a macro variable named _GLSMOD that contains the names of the dummy variables. My output does not contain predictions for the missing values in the dependent variable. Examples: GLMSELECT Procedure. Code the outcome as -1 and 1, and run glmselect, and apply a cutoff of zero to the prediction. The GLMSELECT procedure uses the keyword 'L1' instead of 'lambda' . 941651 -0. . Examples of megamodels arising in genomic data analysis and nonparametric modeling are discussed. + fp(x)*θp SAS provides several methods for packaging. So half of the data in analysisData will be used in Validation and half in Training. run; randomly subdivides the "inData" data set, reserving 50% for training and 25% each for validation and testing. The PROC GLM statement starts the GLM procedure. A variety of these nonsingular parameterizations are available. CVMETHOD=BLOCK < ( n )> CVMETHOD=RANDOM < ( n )> CVMETHOD=SPLIT < ( n )> CVMETHOD=INDEX ( variable) specifies how the training data are subdivided into parts. For more information,. Statistical Graphics Using ODS. BY Statement. In the first step of the selection process, either A or B can enter the model. First, I ran: proc glmselect data=sashelp. 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. The following table shows how PROC GLMSELECT interprets values of the ORDER= option. This example shows how you can use the SCREEN= option to speed up model selection when you have a large number of regressors. . However, for problems that have more predictors or that use much more computationally intense CHOOSE= criterion, sure independence screening (SIS) can run. PROC REG can do this with SELECTION=FORWARD and INCLUDE=2 option in the model statement if you specify product and loanAmount first (include = 2 forces the first two listed variables in all models). We’ll investigate one-way analysis of variance using Example 12. For more about the OUTDESIGN= option, see "The. See the GLMSELECT documentation for various ways to search/stop in the parameter space. These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. Documentation Examples for Clustering Introduction. This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs model selection in the framework of general linear models. from %StepSvylog vs. ALPHA=number. Re: Potential issue with lsmeans in proc mixed (output: Non-est) As pointed out by @PaigeMiller , missing data cell is the most common cause of a non-estimable lsmeans. Table 45. The default is , where is the formatted length of the CLASS variable. PROC GLMSELECT labels some of the series plots. Then effects are deleted one by one until a stopping condition is satisfied. Perform search. This example shows how you can use multimember effects to build predictive models. 5. This list can be used in the MODEL statement of a subsequent procedure. Students were taught using one of three teaching methods, called “basal,” “DRTA,” and “Strat. The GLMSELECT procedure performs effect selection in the framework of general linear models. . PROC GLMSELECT supports the MODELAVERAGE statement, which. 1 Model selection Backward Elimination. You can name the fractions of the data that you want to reserve as test data and validation data. 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. proc logistic has a few different variable selection methods that can be specified in the model statement. Perform search. ” 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. Baseball data set that is described in the section Getting Started: GLMSELECT Procedure. PROC GLMSELECT Statement. . In the examples, both entry model (&SLENTRY) and depart model (&SLSTAY) significant level are 0. You can use the MODELAVERAGE statement in PROC GLMSELECT to perform a basic bootstrap analysis. The PARMDISTRIBUTION request in the PLOTS= option in the PROC GLMSELECT. 129965 -38. For example, the BP_Optimal column is redundant because that column contains a 1 only when the BP_High and. sas. The nonnumeric arguments that you can specify in the STOP= option are shown in Table 42. If we define the angle theta as 2*pi* (DAY/365), then we convert from polar coordinates (assuming that radius = 1) to. Example 42. 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. Use your favorite search engine to see other examples of generating a design matrix by using PROC GLMSELECT and then using the design columns in a subsequent regression analysis. The syntax Group * spl includes an interaction effect between the classification variable and. First in proc glmselect, I'm going to select the plots equal to option to all. This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs model selection in the framework of general linear models. In this example, the YHat variable in the Pred data set contains the predicted values. Documentation Example 1 for PROC CLUSTER. The "Parameter Estimates" table in Figure 44. ”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. Since the variation of salaries is much greater for the higher salaries, it is appropriate to apply a log transformation to the salaries before doing the model selection. If you a fitting a. Both the REG and GLMSELECT procedures provide extensive options for model selection in ordinary linear regression models. 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. The GLMSELECT procedure fills this gap. 1. This method starts with no variables in the model and adds variables one by one to the model. Funda Gunes, in the Statistical Applications Department at SAS, presents LASSO Selection with PROC GLMSELECT. . The horizontal direct product between matrices. . proc print data=work. Options for the smooth fit function include. 1: Modeling Baseball Salaries Using Performance Statistics. For example, specifying. I was reminded of this fact recently when I wrote an article about model building with PROC GLMSELECT in SAS. 6. If the outcomes are ±1 then a cutoff of 0 would be on the predicted values used to determine if the regression predicts an observation is a –1 or a +1. Simple Linear Regression. SAS/STAT ® Software Examples. Example 42. For our fourth example we added one outlier, to the example with 100 subjects, 50 false IVs and 1 real IV, the real IV was included, but the parameter estimate for that variable, which ought to have been 1, was 0. proc glmselect data=sashelp. PROC GLMSELECT creates a SAS item store that is called YourModel. For this example, PROC GLMSELECT runs only slightly faster when SCREEN=SIS than it does when SCREEN=SASVI, although it runs about twice as fast as it does when SCREEN=NONE. 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. Here's sample code for PROC GLMSELECT: proc glmselect data=input; model y = x1-x5 / selection=forward(select=sl) stats=bic details=all; run; The sub-option SELECT=SL specifies that variable selection is based on the significance level of the F statistic (similar to PROC REG, the default would be different: SBC). 4 Multimember Effects and the Design Matrix. . An example of code: PROC. 1 and the significance level to stay is 0. Example 1 for PROC GLMSELECT /**/ /* S A S S A M P L E L I B R A R Y */ /* */ /* NAME: glsdt */ /* TITLE: Details Section Examples for PROC. 15 SLS=0. It supports running various algorithms that try to produce a parsimonious model based on those candidate variables. See Table 60. sas. For example, if you generate all pairwise quadratic interactions of N continuous variables, you obtain "N choose 2" or N*(N-1). 08. Further, there can be differences in p-values as proc genmod use -2LogQ tests, and proc glm use F-tests. To use PROC PLM you must first use the STORE statement in a regression procedure to create an item store that summarizes the model. The _GLSInd macro contains the name of the selected variables. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. The GLMSELECT procedure offers extensive capabilities for customizing the. 6 from the text. But sometimes there are problems. uses a forward-selection algorithm to select variables. In this example, model selection that uses other information criteria and out-of-sample prediction. This list can be used, for example, in the model statement of a subsequent procedure. You specify the GLMSELECT procedure with the following code. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. For example, the following statements use the same data for testing. 269958 36. 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. 1 SLS=0. selects effects to enter or drop as in the previous example except that the significance level for entry is now and the significance level to stay is . . You can find further discussion and formula for these criteria in the PROC GLMSELECT documentation. Use the spline bases as explanatory variables in the model. 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). 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. . The HPLOGISTIC Procedure. ODS Graph Names. 2 Using Validation and Cross Validation. The definitions used in PROC GLMSELECT changed between the experimental and the production release of the procedure in SAS 9. A variety of model selection methods are available, including the LASSO method of Tibshirani ( 1996) and the related LAR method of Efron et al. You can use the PROC GLMSELECT statement in SAS to select the best regression model based on a list of potential predictor variables. . The PSMATCH Procedure. Trending. The EFFECTPLOT statement enables you to create plots that visualize interaction effects in complex regression models. 0001 where Probt is a parameter's p-value. It causes the GLMSELECT procedure to resample B times from the data (essentially, generates bootstrap samples) and performs variable selection and fitting on each resample. keyword <=name> specifies the statistics to include in the output data set and optionally names the new variables that contain the statistics. Sorted by: 3. The GLMSELECT procedure supports a variety of model selection methods for general linear models.