Abstract. 08 choose=AIC) selects effects to enter or drop as in the previous example except that the significance level for entry is now 0. (). This value is used as the default confidence level for limits computed by the. I have a set of about 40 predictor variables for a set of 20K subjects. Since the variation of salaries is much greater for the higher salaries, it is. Bandyopadhyay (VCU) 5 / 68. ” The goal is to investigatedocumentation. Here is a worked example using your simple three observation dataset and a modified version of the PROC GLMMOD method posted by @Reeza. In conclusion, we saw different procedures used in SAS predictive modeling: PROC ADAPTIVEREG, PROC GLMSELECT, PROC HPGENSELECT, PROC TRANSREG, and PROC PLS with example & syntax. Details on the specifications in the OUTPUT statement follow. Examples focus on logistic regression using the LOGISTIC procedure, but these techniques can be readily extended to other procedures and statistical models. The horizontal direct product between matrices A and B is formed by the elementwise multiplication of their columns. The EFFECT statement enables you to construct special collections of columns for design matrices. 2" KLL"distance"isa"way"of"conceptualizing"the"distance,"or"discrepancy,"between"two"models. The PRINQUAL Procedure. 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. GLMSELECTDATA=SAS data set names the data set to be scored. 1 b2 0. A general linear model can be viewed as a linear combination of functions fi(x) of the predictors: f(x,θ) = f1(x)*θ1 +. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. baseball; proc contents varnum data=baseball;But PROC GLMMOD is not the only way to generate design matrices in SAS. . In the standard stepwise method, no effect. See the section Macro Variables Containing Selected Models for details. PROC GLMSELECT supports the MODELAVERAGE statement, which. The following DATA step generates the data: If you do not specify either the STOP= or SELECT= option, then the default is STOP=SBC. . Note that many procedures (for example, PROC GLM, PROC MIXED, PROC GLIMMIX, and PROC LIFEREG) do not allow different parameterizations of. For more information,. Hi there, I would like to persist the model (formula) produced by proc glmselect like so: PROC GLMSELECT DATA = WORK. Then effects are deleted one by one until a stopping condition is satisfied. For example, the following statements recover the selection for sample 1: proc glmselect data=simOut; freq sf1; model y=x1-x10/selection=LASSO(adaptive stop=none choose=SBC); run; The average model is not parsimonious—it includes shrunken estimates of infrequently selected parameters which often correspond to irrelevant regressors. Trending. Leutrain valdata = sashelp. The PRINQUAL Procedure. 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. These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. 1 Modeling Baseball Salaries Using Performance Statistics. GENMOD fits the. Create an item store, and then use the item store to score the new cases in ameshousing4. 08. 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. PROC GLM does not have an option, like the STB option in PROC REG, to compute standardized parameter estimates. Since the variation of salaries is much greater for the higher. This method starts with no variables in the model and adds variables one by one to the model. CPREFIX= n specifies that, at most, the first n characters of a CLASS variable name be used in creating names for the corresponding design variables. In the first step of the selection process, either A or B can enter the model. The idea is to calculate stratified values for the bluebook that base on these variables. 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. Dennis Fisher Dennis G. The HPMIXED Procedure. • Proc GLMSelect – LASSO – Elastic Net • Proc HPreg – High Performance for linear regression with variable selection (lots of options, including LAR, LASSO, adaptive. selection=stepwise. This example shows how you can use multimember effects to build predictive models. It supports running various algorithms that try to produce a parsimonious model based on those candidate variables. specifies that, at most, the first n characters of a CLASS variable label be used in creating labels for the corresponding design variables. It is the value of y when x = 0. The PSMATCH Procedure. A partial R 2 is provided when comparing a full. However, be aware that the procedures might ignore observations that have missing values for the variables in the model. sas. 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,. Other approaches for performing model averaging are presented in Burnham and Anderson , and. . For example, if you have a binary response you can use the EFFECT statement in PROC LOGISTIC. The GLMSELECT procedure performs effect selection in the framework of general linear models. A possible search term is "proc glmselect" outdesign site:. If you specify the WEIGHT statement, it must appear before the first RUN statement or it is. Example 42. 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. Example 49. 4 Multimember Effects and the Design Matrix. proc glmselect data=sashelp. The PROBIT Procedure. The HPLMIXED Procedure. sample sizes for training and validation data sets in marketing or credit risk are often very large and binning makesThis example shows how to use the elastic net method for model selection and compares it with the LASSO method. 49. Thanks. 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. PROC GLMSELECT supports several criteria that you can use for this purpose. Then &_QRSIND would be set to x1 x3 x4 x10 if the first, third, fourth, and tenth effects were selected for the model. As shown in the example, the macro can be used in subsequent analyses. Elastic Net # Observations (Training sample) 38: 38 # Variables: 7129. 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. . SAS/STAT ® Software Examples. The basic structure of PROC SURVEYFREQ code has some. The HPLOGISTIC Procedure. You can use spline effects in any SAS procedure. SAS/STAT User’s Guide documentation. k< 30 (not set in stone). The default is , where f is the formatted length of the CLASS variable. 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. As discussed by Agresti (2013), one such situation occurs when there is a large number of covariates, of which only a small subset are strongly. . Here’s an example: logit ˇ(x) = 0 + 1x 1 + 2x 2 + 3(x 1 3x 2):. . SAS/STAT 15. 25 validate=0. ( 2004 ). Within each category of statistical analysis, the examples are grouped by the SAS/STAT procedure that is being demonstrated. CLASS variables (like PROC GLM) and model selection (like PROC REG). 3789 Example 47. (PROC GLMSELECT) on SASHELP. 05. In this example, model selection that uses other information criteria and out-of-sample prediction. 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. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. Also consider GLMSELECT procedure. You can also specify criteria based on validation; this. The PROC GLM statement starts the GLM procedure. The horizontal direct product between matrices. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. Alternatively, you can use the OUTDESIGN= option in PROC GLIMMIX. Options / Examples: GLMSELECT= Input optional CLASS. You can find further discussion and formula for these criteria in the PROC GLMSELECT documentation. 2 Using Validation and Cross Validation. Subsections: 49. . Say your input effect list consists of x1-x10 . SAS Forecasting and Econometrics. This procedure supports a. ; run; Let’s look at the data. . Example include the "SELECT" procedures (GLMSELECT, QUANTSELECT, HPGENSELECT. 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. Documentation here:. 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. Until version 9. 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. Examples. The value must be between 0 and 1; the default value of 0. "However, to get inferential statistics and hypotheses tests, you should select a. The PRINCOMP Procedure. proc glmselect data=dojoBumps; effect spl = spline(x / knotmethod. LOGISTIC, PROC GENMOD, PROC GLMSELECT, PROC PHREG, PROC SURVEYLOGISTIC, and PROC SURVEYPHREG) allow different parameterizations of the CLASS variables. For example, suppose that the model contains the main effects A and B and the interaction A*B. For the reference level, all three dummy variables have a value of . 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. I used the example in the SAS/STAT 13. The following table shows how PROC GLMSELECT interprets values of the ORDER= option. It also demonstrates several features of the OUTDESIGN= option in the PROC GLMSELECT statement. They provide a Stepwise Selection example that shows. Baseball data set that is described in the section Getting Started: GLMSELECT Procedure. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. . Documentation Examples for Clustering Introduction. A variety of these nonsingular parameterizations are available. This example shows how you can use multimember effects to build predictive models. The procedure also provides graphical summaries of the selection process. sas. Analytics. 1: Modeling Baseball Salaries Using Performance Statistics. . Dep Mean, the sample mean of the dependent variable . The following statements provide. Statistical Graphics Using ODS. . Random partition into training, validation, and testing data Funda Gunes, in the Statistical Applications Department at SAS, presents LASSO Selection with PROC GLMSELECT. 4M63. Choose PROC GLMSELECT for “large p” problems and choose PROC REG for smaller numbers of predictors, e. Example include the "SELECT" procedures (GLMSELECT, QUANTSELECT, HPGENSELECT. In order to demonstrate the efficiency in screening model selection, this example. However, in some cases, you might not have sufficient. 4 Programming Documentation |The GLM Procedure Overview The GLM procedure uses the method of least squares to fit general linear models. This list can be used, for example, in the model statement of a subsequent procedure. Options for the smooth fit function include. 7129 # included in model. Example 42. The example below illustrates how SAS language tools for iteration across groups in datasets can be used instead. y: Dependent variable. If you a fitting a. baseball; proc contents varnum data=baseball;The GLMSELECT procedure also provides extensive capabilities for customizing effect selection. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. PROC GLM supports CLASS variables. SAS Web Report Studio. . Because the functionality is contained in the EFFECT statement, the syntax is the same for other procedures. We’ll investigate one-way analysis of variance using Example 12. Both PROC GLMSELECT and PROC REG can do stepwise regression. RANDOM FOREST – THE HIGH-PERFORMANCE PROCEDURE The SAS® code below calls the High-Performance Random Forest procedure, PROC HPFOREST. . SAS/STAT 9. 1: Modeling Baseball Salaries Using Performance Statistics. This list can be used, for example, in the model statement of a subsequent procedure. 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. 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. 05 results in 95% intervals. , the CVMETHOD= options in PROC GLMSELECT [25]), none appear to be available for bootstrap estimation of optimism as of SAS version 9. Among the statistical methods available in PROC GLM are regression, analysis of variance, analysis of covariance, multivariate analysis of variance, and partial corre-lation. proc glmselect data=ex7Data; class c:; model y = x: c:/ selection=lasso; run; Output 49. 35: 53. You can request leave-one-out cross validation by specifying PRESS instead of CV with the options SELECT=, CHOOSE=, and STOP= in the MODEL statement. . The HPGENSELECT Procedure. Proc Glmselect under three scenarios: forward, backward, stepwise. . Compared with the LASSO method, the elastic net method can select more variables, and the number of selected. 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. b: Slope or Coefficient. This variable is useful for matching BY groups with macro variables that PROC GLMSELECT creates. This example shows how you can use model selection to perform scatter plot smoothing. This example shows how you can use model selection to perform scatter plot smoothing. First in proc glmselect, I'm going to select the plots equal to option to all. The outcome is a binary yes/no response, so I would like to end with a logistic regression model. The PROBIT Procedure. PROC GLM analyzes data within the framework of General linear. The example. 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. 7. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. The _GLSInd macro contains the name of the selected variables. 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. Test; class AW LN PM(ref="FP"); MODEL Q = FN DR AW LN PM / selection = none stb showpvalues; ods output "Fit Statistics" = WORK. PROC GLMSELECT tries to thin labels to avoid conflicts. PROC QUANTSELECT saves the list of selected effects in a macro variable, &_QRSIND. proc glmselect data = sashelp. . . Consider a continuous random variable Y and a constant C. baseball plot=CriterionPanel;. However, for problems that have more predictors or that use much more computationally intense CHOOSE= criterion, sure independence screening (SIS) can run. It has many of the same input/output capabilities as PROC REG, but it does not provide as many diagnostic tools or allow interactive changes in the model or data. . This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. First we read in the data using a SAS® datastep (Figure 2). The simulated data for this example describe a two-week summer tennis camp. Examples of megamodels arising in genomic data analysis and nonparametric modeling are discussed. There is a lot that you can do with PLS. The following SAS/STAT software examples are grouped according to the type of statistical analysis that is being performed. This selection method is available in the GLMSELECT, LOGISTIC, PHREG, QUANTSELECT, and REG procedures. 2 Using Validation and Cross Validation. In that example, the default stepwise selection method based on the SBC criterion was used to select a model. In ordinary linear regression, as done in the REG, GLM, and GLMSELECT procedures, two commonly used tools are standardized. This example continues the investigation of the baseball data set introduced in the section Getting Started: GLMSELECT Procedure. Another example is the MCMC procedure, whose documentation includes an example that creates a design matrix for a Bayesian regression model . If STOP= n is specified, then PROC GLMSELECT stops selection at the first step for which the selected model has n effects. GLMSELECT fits the "general linear model" that assumes that the response distribution is normal and it directly models the response mean. – JJFord3. keyword <=name> specifies the statistics to include in the output data set and optionally names the new variables that contain the statistics. Three columns are created to indicate group membership of the nonreference levels. Building Sparse Regression Models with the GLMSELECT Procedure The GLMSELECT procedure selects effects in general linear models of the form y iD 0C 1x i1CC px ipC i; iD1;:::;n where the response y iis continuous and the predictors x i1;:::;x iprepresent main effects that consist of continuous or classification variables, and interaction effects or. First page loaded, no previous page available. 1 User's Guide documentation. The examples use the Baseball data set that is described in the section Getting Started: GLMSELECT Procedure. Examples of megamodels arising in genomic data analysis and nonparametric modeling are discussed. 49. This degree must be a positive integer. The PARMDISTRIBUTION request in the PLOTS= option in the PROC GLMSELECT. 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);. View more in. For example, if the number of observations in the data set is 100, then the following two PROC GLMSELECT steps are. PROC GLMSELECT with SELECTION = LASSO (CHOOSE=SBC) The use of PROC GLMSELECT (method #4) may seem inappropriate when discussing logistic regression. The GLMSELECT procedure fills this gap. For each unit increase in x, y changes by the amount represented by the slope. PROC GLMSELECT provides a variety of selection and stopping criteria. I was reminded of this fact recently when I wrote an article about model building with PROC GLMSELECT in SAS. Deciding when to stop a selection method is a crucial issue in performing effect selection. From the sequence of models. . 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. 7129 # included in model. proc logistic has a few different variable selection methods that can be specified in the model statement. ) The Sashelp. . 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 simulated data for this example describe a two-week summer tennis camp. Example 42. 001 choose = validate);. All statements other than the MODEL statement are optional and multiple SCORE statements can be used. . cars; model msrp = Cylinders EngineSize Horsepower Length MPG_City MPG_Highway Weight Wheelbase; store work. 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. It can be viewed as a stepwise procedure with a single addition. . 5. 15 SLS=0. The simulated data for this example describe a two-week summer tennis camp. The GLMSELECT Procedure. 15; in forward, an entry level. Currently loaded videos are 1 through 15 of 15 total videos. Please define your question in more detail. [1] PROC GLMSELECT provides the most modern and flexible options for model selection. Then &_QRSIND would be set to x1 x3 x4 x10 if the first, third, fourth, and tenth effects were selected for the model. DATA Step Programming . . comThe two models specified are the same. This variable is useful for matching BY groups with macro variables that PROC GLMSELECT creates. Since my outcome is binary, it seems like PROC GLIMMIX is the appropriate 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 HPFMM Procedure. Example 42. BY Statement. The HPCANDISC Procedure. 22 User's Guide. Afraid you'll need to loop through using the SAS macro language for proc logistic though. (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. Getting Started: GLMSELECT Procedure. For example, the BP_Optimal column is redundant because that column contains a 1 only when the BP_High and. The example below illustrates how SAS language tools for iteration across groups in datasets can be used. It can be viewed as a stepwise procedure with a single addition to or deletion from the set of nonzero regression coefficients at any step. The HPGENSELECT Procedure. ALPHA=p. SAS® 9. The CPREFIX= applies only when you specify the PARMLABELSTYLE=INTERLACED option in the PROC GLMSELECT statement. 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. 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 easiest way to create an effect plot is to use the STORE statement in a. . 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. 129965 -38. Efron et al. Regularization methods can be applied in order to shrink model parameter estimates in situations of instability. You can use these. Details. This example uses a microarray data set called the leukemia (LEU) data set (Golub et al. For example, if race="African American" or hospital="St. The MODELAVERAGE statement in PROC GLMSELECT is intended for when you use variable-selection methods to choose effects in a linear regression model. These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. Example 42. This example shows how you can use PROC GLMSELECT as a starting point for such an analysis. We used the defaults in stepwise, which are a entry level and stay level of 0. Further, there can be differences in p-values as proc genmod use -2LogQ tests, and proc glm use F-tests. 941651 -0. The PROC GLMSELECT procedure in SAS/STAT is a comprehensive tool for model selection and it performs effect selection in the framework of general linear models. In their code, they used lars algorithm to get a lasso multiple regression: * lasso multiple regression with lars algorithm k=10 fold validation; proc glmselect data=traintest plots=all seed=123; partition ROLE=sele. This example uses a microarray data set called the leukemia (LEU) data set (Golub et al. . These criteria fall into two groups—information criteria and criteria based on out-of-sample prediction performance. PROC GLMSELECT saves the list of selected effects in a macro variable, &_GLSIND. This section provides an example of using splines in PROC GLMSELECT to fit a GLM regression model. This example illustrates how you can use PROC HPGENSELECT to perform Poisson regression for count data. 3 Answers. 5. 877694553 0. When the input data set specified in the DATA= option in the PROC GLMSELECT statement contains an _ROLE_ variable and no PARTITION. specifies the criterion that PROC GLMSELECT uses to determine the order in which effects enter and/or leave at each step of the specified selection method. 5 Model Averaging. For example, if the name of the categorical variable is X and it has values 'A', 'B', and 'C', then the names of the dummy variables are X_A, X_B, and X_C. The salaries ( Sports Illustrated, April 20, 1987) are for the 1987. Study with Quizlet and memorize flashcards containing terms like What procedure do you use for correlation analysis?, What procedures can you use for linear regression?, First two steps to take before performing regression analysis on two continuous variables and more. 1. Practice: Using the SCORE Statement in PROC GLMSELECT. The data in testData will be used for Testing. . D. com. 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. OPTGRAPH Procedure . The GLMSELECT Procedure. With two outliers (example 5), the parameter estimate was reduced to 0. . . Figure 2 SAS® Datastep and NPAR1WAY Procedure Code. You use the CHOOSE= option of forward selection to specify the criterion for selecting one model from the sequence of models produced. 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. 72. Baseball data set that is described in the section Getting Started: GLMSELECT Procedure. Sorted by: 3. uses a forward-selection algorithm to select variables. uses a forward-selection algorithm to select variables. CLASS and EFFECT statements, if present, must. PROC GLMSELECT Statement. 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. The SAS code would be: data paula1; set paula0; proc glm; class year herd season; model milk= year herd season age age*age; run; My R code is: model1 = glm (milk ~ factor (year) + factor (herd) + factor (season) + age + I (age^2), data=paula1) anova (model1) I suspect that there is something wrong because all effects are statistically. 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. 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. The procedure offers options for customizing the selection with a wide variety of selection and stopping criteria. The default is , where f is the formatted length of the CLASS variable. One example can be seen in the boxplot below, where different bluebook distributions by car type can. For example, the following statements recover the selection for sample 1: proc glmselect data=simOut; freq sf1; model y=x1-x10/selection=LASSO(adaptive stop=none choose=SBC); run; The average model is not parsimonious—it includes shrunken estimates of infrequently selected parameters which often correspond to irrelevant regressors. The MODELAVERAGE. 1 documentation, with changes. For this example, I am using restricted cubic splines and four evenly spaced internal knots, but the same ideas apply to any choice of spline effects. carvalue(obs=10); var SequenceID policyno bluebook car_type car_use Car_Age_Months travtime; run; The Basic Idea of the Analysis . The default is , where is the formatted length of the CLASS variable. First let's make a sample dataset with a long character ID variable. Overview.