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. Note that many procedures (for example, PROC GLM, PROC MIXED, PROC GLIMMIX, and PROC LIFEREG) do not allow different parameterizations of. Unlike the GLMSELECT procedure, the REGSELECT procedure does not perform model selection by default. Example 44. comFor example, there are many ways to solve for the least-squares solution of a linear regression model. (). 3789 Example 47. PS Answer: Look at the Data Step in the example you linked to. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. The PARMDISTRIBUTION request in the PLOTS= option in the PROC GLMSELECT. The following sections describe the ODS graphical. The documentation for the PLM procedure includes more information and examples. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. If you omit this option, then the input data set named in the DATA= option in the PROC GLMSELECT statement is scored. 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. Then &_QRSIND would be set to x1 x3 x4 x10 if the first, third, fourth, and tenth effects were selected for the model. For example, the following. For more information,. Example 42. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. This selection method is available in the GLMSELECT, LOGISTIC, PHREG, QUANTSELECT, and REG procedures. Leutrain valdata = sashelp. 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. It also produces output that allow further analyses with REG and/or GLM. This option affects the PROC REG option TABLEOUT; the MODEL options CLB, CLI, and CLM; the OUTPUT statement keywords LCL, LCLM, UCL, and UCLM; the PLOT statement. A variety of model selection methods are available, including forward, backward, stepwise, LASSO, and least angle regression. 4M63. 5 Model Averaging. 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. . . as option for proc glmselect I get: Effect Parameter DF Estimate StandardizedEst StdErr tValue Probt Intercept Intercept 1 9. Although designed for PROC GLM models, it can also be used as a model selection tool for logistic regression Flom and Cassell (2009). You can specify information criteria or criteria based on significance levels. The MODEL statement fits the regression model and the OUTPUT statement writes an output data set that contains the predicted values. g. The overall appearance of graphs is controlled by ODS styles. Information on the tables will be written to the log. Then &_QRSIND would be set to x1 x3 x4 x10 if the first, third, fourth, and tenth effects were selected for the model. The following statements create B=5,000 bootstrap sample, fit the model on each, and output the predicted mean at each point in the input data set. 05 in SAS PROC LOGISTIC). (both point estimates and interval estimates) Here is my code. Say your input effect list consists of x1-x10 . The procedure also provides graphical summaries of the selection process. The following sections describe the ODS graphical displays produced by PROC GLMSELECT. 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. From the sequence of models produced, the selected model is chosen to yield the minimum AIC statistic. The following global-plot-option applies to all plots produced by PROC PLM. . The HPMIXED Procedure. . proc glmselect data=ex7Data; class c:; model y = x: c:/ selection=lasso; run; Output 49. 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. 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. Shared Concepts and Topics. These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. Suppose an internet service provider plans to conduct a customer satisfaction survey by selecting a random sample of customers from all current customers (the. In ordinary linear regression, as done in the REG, GLM, and GLMSELECT procedures, two commonly used tools are standardized. 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. First let's make a sample dataset with a long character ID variable. 49. Research and Science from SAS. proc logistic has a few different variable selection methods that can be specified in the model statement. But I also need to use the fitted model to make prediction on testing dataset. Using binary responses in PROC GLMSELECT is not truly a logistic regression. The MODEL statement in PROC GLMSELECT includes 18 independent variables, but the final LASSO model contains only seven variables. Consider a model with one classification variable A with four levels, 1, 2, 5, and 7. 3789 Example 47. PROC GLMSELECT provides more selection options and criteria than PROC REG, and PROC GLMSELECT also supports CLASS variables. SAS/STAT User’s Guide documentation. In your example you changed the default settings of stepwise. 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. First we read in the data using a SAS® datastep (Figure 2). In this example, the YHat variable in the Pred data set contains the predicted values. PROC GLMSELECT provides a variety of selection and stopping criteria. Improved ALLMIXED SAS macro application. For more information, see Chapter 56, “The GLMSELECT Procedure. 15; run; proc glmselect data=data; class c1 c2 c3; model y = x1 x2 x3 c1 c2 c3 x1*x2 x1*c1 /selection=stepwise(select=SL SLE=0. 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 GLMSELECT Procedure. As with the other selection methods that PROC GLMSELECT supports, you can specify a criterion to choose among the models at each step of the LASSO algorithm by using the CHOOSE= option. 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. 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. This section provides some background about the LASSO method that you need in order to understand the group LASSO method. Can you please provide some code example? This is a code example, which does not work: proc GLMSELECT data=sashelp. The "Parameter Estimates" table in Figure 44. Figure 2 SAS® Datastep and NPAR1WAY Procedure Code. Currently loaded videos are 1 through 15 of 15 total videos. First we read in the data using a SAS® datastep (Figure 2). 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. Abstract. The GLMSELECT procedure offers extensive capabilities for customizing the selection by providing a wide variety of selection and stopping criteria, including significance level–based and validation-based criteria. For example, the following statements create and run a macro that uses PROC GLM to perform LSMeans analyses. proc print data=work. The simulated data for this example describe a two-week summer tennis camp. This list can be used, for example, in the model statement of a subsequent procedure. 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. The horizontal direct product between matrices. Sorry I am still a SAS newby. For example, suppose that the model contains the main effects A and B and the interaction A*B. 0001 Bla Bla 1 -4. 02 <. PROC GLMSELECT tries to thin labels to avoid conflicts. It fills the gap of allowing variable selection with CLASS variables. A variety of these nonsingular parameterizations are available. This section provides an example of using splines in PROC GLMSELECT to fit a GLM regression model. The GLMSELECT procedure offers extensive capabilities for customizing the. In the first step of the selection process, either A or B can enter the model. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. 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. so you can create the splines directly in the grammar of the procedure. . The example. 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. Documentation Example 4 for PROC CLUSTER. See the section Macro Variables Containing Selected Models for details. Options / Examples: GLMSELECT= Input optional CLASS. Proc Logistic, and %StepSvyreg vs. PROC GLM does not have an option, like the STB option in PROC REG, to compute standardized parameter estimates. (Others include PROC CATMOD and PROC GLMSELECT. 35: 53. 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. I was reminded of this fact recently when I wrote an article about model building with PROC GLMSELECT in SAS. 49. If SELECT=SL, PROC GLMSELECT uses the traditional stepwise method as implemented in PROC REG. 49. The procedure offers extensive capabilities for customizing the. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. keyword <=name> specifies the statistics to include in the output data set and optionally names the new variables that contain the statistics. PROC GLMSELECT fits an ordinary regression model. PROC GLMSELECT creates a SAS item store that is called YourModel. 0001 where Probt is a parameter's p-value. 1 Answer. brfss2;. – JJFord3. Re: Lasso Logistic Regression using GLMSELECT procedure. Model_Fit "Parameter Estimates" =. In addressing these examples, built-in facilities of the procedure to handle validation and test data are highlighted in addition to techniquesPROC QUANTSELECT saves the list of selected effects in a macro variable, &_QRSIND. Both the REG and GLMSELECT procedures provide extensive options for model selection in ordinary linear regression models. EFFECT MyPoly=POLYNOMIAL (x1 x2/degree=4 MDEGREE=2); generates the terms , , , , ,, and . SAS/STAT ® Software Examples. This algorithm for SELECTION= LASSO is used in PROC GLMSELECT. 269958 36. com. The MODEL statement in PROC GLMSELECT includes 18 independent variables, but the final LASSO model contains only seven variables. 3789 Example. You can use these. Direct comparisons between PROC REG and PROC GLMSELECT are made. The easiest way to create an effect plot is to use the STORE statement in a. BY Statement. By default, MAXMACRO=100. 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 have requested n -fold cross validation by requesting CHOOSE= CV, SELECT= CV, or STOP= CV in the MODEL statement, then a variable _CVINDEX_ is. 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. sas. com PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. 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. The PARMDISTRIBUTION request in the PLOTS= option in the PROC GLMSELECT. 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. All statements other than the MODEL statement are optional and multiple SCORE statements can be used. The simulated data for this example describe a two-week summer tennis camp. The example below illustrates how SAS language tools for iteration across groups in datasets can be used instead. 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. PROC GLMSELECT with SELECTION = LASSO (CHOOSE=SBC) The use of PROC GLMSELECT (method #4) may seem inappropriate when discussing logistic regression. 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. The HPLOGISTIC Procedure. Example 42. After settling on a final model, it is often desirable to assess of the relative importance of the predictors in the model. Introduction to Power and Sample Size Analysis. Base SAS Procedures . selects effects to enter or drop as in the previous example except that the significance level for entry is now 0. The LPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. These examples use simulated data for a customer satisfaction survey. 4. The HPCANDISC Procedure. Compared with the LASSO method, the elastic net method can select more variables, and the number of selected. 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. This example shows how you can combine variable selection methods with model averaging to build parsimonious predictive models. 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). PROC GLMSELECT creates a macro variable named _GLSMOD that contains the names of the dummy variables. . selection=stepwise (select=SL SLE=0. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. 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. Until version 9. 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. NOSEPARATE. which are available in SAS through PROC GLMSELECT. 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: proc glmselect; model y=x1-x10/selection=forward (stop=CV) cvMethod=split (100); run; proc glmselect; model y=x1-x10/selection=forward (stop=PRESS); run; Example 42. The first call writes the design matrix that PROC GLM uses (internally) for the default reference levels. 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 . If you specify the WEIGHT statement, it must appear before the first RUN statement or it is. Since my outcome is binary, it seems like PROC GLIMMIX is the appropriate procedure. The GLMSELECT procedure supports the OUTDESIGN= option, which enables you to output a design matrix for the variables in a regression model. Create an item store, and then use the item store to score the new cases in ameshousing4. 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). For example, if you have a binary response you can use the EFFECT statement in PROC LOGISTIC. . Documentation here:. GLMSELECTDATA=SAS data set names the data set to be scored. 49. In that example, the default stepwise selection method based on the SBC criterion was used to select a model. 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. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. As shown in the example, the macro can be used in subsequent analyses. A variety of model selection methods are available, including the LASSO method of Tibshirani ( 1996) and the related LAR method of Efron et al. . . baseball plot=CriterionPanel;. This example shows how you can use PROC LIFEREG and the DATA step to compute two of the three types of predicted values discussed there. comThe GLMSELECT procedure performs effect selection in the framework of general linear models. . The following code selects a model with the default settings:. The GLMSELECT Procedure. Global Plot Option. It can be viewed as a stepwise procedure with a single addition. . Using the Output Delivery System. Bandyopadhyay (VCU) 5 / 68. 49. 269958 36. SAS Help CenterIt can be viewed as a stepwise procedure with a single addition to or deletion from the set of nonzero regression coefficients at any step. ScoreExample; /* store the model */ quit;. "One"of"these" models,"f(x),is"the"“true”"or"“generating”"model. The model statement has the main effects of female and prog, as well as their interaction; the interaction is specified by taking the product of the two main effect terms. 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. The example uses the macro on the MODEL statement of. Say your input effect list consists of x1-x10 . PROC GLMSELECT with SELECTION = LASSO (CHOOSE=SBC) The use of PROC GLMSELECT (method #4) may seem inappropriate when discussing logistic regression. 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). 6 from the text. . ( 2004 ). Table 1. 05 results in 95% intervals. Elastic Net # Observations (Training sample) 38: 38 # Variables: 7129. The following DATA step generates the data for this example. Apply each bootstrap-sample-derived model to the original sample dataset, and measure the performance metric. SAS/IML Software and Matrix Computations. You can use a SAS autocall macro, %Marginal, to display marginal model plots. See the GLMSELECT documentation for various ways to search/stop in the parameter space. , the CVMETHOD= options in PROC GLMSELECT [25]), none appear to be available for bootstrap estimation of optimism as of SAS version 9. The simple linear regression model is a linear equation of the following form: y = a + bx. 8 Group LASSO Selection. A partial R 2 is provided when comparing a full. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. The following sections describe the ODS graphical displays produced by PROC GLMSELECT. 5. ODS Graph Names. In the examples, both entry model (&SLENTRY) and depart model (&SLSTAY) significant level are 0. It also demonstrates several features of the OUTDESIGN= option in the PROC GLMSELECT statement. But, there are quite big difference in how the two procedure works. 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. cars, I get the same results as those you provide in your article. , 1999 ), which is used in the paper by Zou and Hastie ( 2005 ) to demonstrate the performance of the. PROC GLMSELECT uses the traditional stepwise method as implemented in PROC REG. specifies the maximum degree of any variable in a term of the polynomial. 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 on permanent SAS data sets, refer to the section "SAS Files" in SAS Language Reference: Concepts. 13 shows that for this example the parameters that correspond to only levels 3 and 5 of c1 are in the selected model. appropriate sample, if needed, can be obtained by using the SURVEYSELECT procedure. . But, as discussed by Robert Cohen (2009), a selection of good predictors for a logistic model may be identified by PROC GLMSELECT when 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 backward elimination technique starts from the full model including all independent effects. 8); run; Because. For example, if the number of observations in the data set is 100, then the following two PROC GLMSELECT steps are. . (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. Are you trying to create variables, or specify interaction terms in a model statement. 6 Elastic Net and External Cross Validation. For more information, see Chapter 56, “The GLMSELECT Procedure. Example 1. Example 42. If STOP= n is specified, then PROC GLMSELECT stops selection at the first step for which the selected model has n effects. SAS/STAT 15. Hi there, I would like to persist the model (formula) produced by proc glmselect like so: PROC GLMSELECT DATA = WORK. Dennis Fisher Dennis G. 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. 4 Programming Documentation |The GLM Procedure Overview The GLM procedure uses the method of least squares to fit general linear models. 1. 4 Multimember Effects and the Design Matrix. The PROC GLM statement starts the GLM procedure. . SAS® 9. Learn about SAS Training - Statistical Analysis path If you do not specify either the STOP= or SELECT= option, then the default is STOP=SBC. Usage Note 22590: Obtaining standardized regression coefficients in PROC GLM. 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. 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. 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. Figure 2 SAS® Datastep and NPAR1WAY Procedure Code. . For example, you might decide to use an information criterion to decide what effects to include and when to terminate the selection process. If you have requested -fold cross validation by requesting CHOOSE= CV, SELECT= CV, or STOP= CV in the MODEL statement, then a variable _CVINDEX_ is included in. Usage Note 60240: Regularization, regression penalties, LASSO, ridging, and elastic net. CLASS Variable Parameterization. However, be aware that the procedures might ignore observations that have missing values for the variables in the model. When the input data set specified in the DATA= option in the PROC GLMSELECT statement contains an _ROLE_ variable and no PARTITION. . Overview: GLMSELECT Procedure. . The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. This value is used as the default confidence level for limits computed by the. When a WEIGHT statement is used, a weighted residual sum of squares. 3 Scatter Plot. If you have requested -fold cross validation by requesting CHOOSE= CV, SELECT= CV, or STOP= CV in the MODEL statement, then a variable _CVINDEX_ is included in the output data set. You can use the MODELAVERAGE statement in PROC GLMSELECT to perform a basic bootstrap analysis. LOGISTIC, PROC GENMOD, PROC GLMSELECT, PROC PHREG, PROC SURVEYLOGISTIC, and PROC SURVEYPHREG) allow different parameterizations of the CLASS variables. carvalue(obs=10); var SequenceID policyno bluebook car_type car_use Car_Age_Months travtime; run; The Basic Idea of the Analysis . 15 SLS=0. The GLMSELECT procedure has the following advantages of the GLMMOD procedure: The procedure supports the EFFECT statement, which you can use to define spline effects,. baseball; proc contents varnum data=baseball;The GLMSELECT procedure also provides extensive capabilities for customizing effect selection. Getting Started;. The GLMSELECT procedure performs effect selection in the framework of general linear models. Read Less. [1] PROC GLMSELECT provides the most modern and flexible options for model selection. Q&A for work. For more about the OUTDESIGN= option, see "The. Example 42. The idea is to calculate stratified values for the bluebook that base on these variables. The definitions used in PROC GLMSELECT changed between the experimental and the production release of the procedure in SAS 9. 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. In this example, model selection that uses other information criteria and out-of-sample prediction. 1 Model Selected by Adaptive Lasso. The tennis ability of. 129965 -38. OPTGRAPH Procedure . You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. In that example, the default stepwise selection method based on the SBC criterion was used to select a model. 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. 001 choose = validate);. 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. Videos. PROC GLMSELECT provides several methods for partitioning. Example 42. Options for the smooth fit function include. Next, we’ll use proc univariate to perform a Kolmogorov-Smirnov test to determine if the sample is normally distributed: /*perform Kolmogorov-Smirnov test*/ proc univariate data=my_data; histogram Values / normal(mu=est sigma=est); run; At the bottom of the output we can see the test statistic and corresponding p-value of the Kolmogorov. . section we briefly discuss some better alternatives, including two that are newly implemented in SAS in PROC GLMSELECT. "However, to get inferential statistics and hypotheses tests, you should select a. 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. . 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. Global Statements. data-set-name). You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. From the sequence of models. At each step, the variable that is added is the one that most improves the fit of the model. Learn more at PROC GLMSELECT supports several criteria that you can use for this purpose. 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. We also have basline data on their demographics. The GLMSELECT Procedure. An example of the PLS procedure in SAS. Learn more about TeamsPROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. Example 1.