Error Procedure Genmod Not Found
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Usage Note 16955: "Error: Procedure xxxx not found" due to SAS/STAT not installed under Education Analytical Suite license The following error message might appear when you submit a SAS/STAT procedure where xxxx is replaced with the procedure name--such as, but not limited to, LOGISTIC, GENMOD, or MIXED: ERROR: Procedure xxxx not found. If you have the sas/stat Education Analytical Suite bundle installed, which is licensed primarily by universities and colleges, the error might be due to installing with an incorrect SAS Installation Data (SID) file. In this case, only BASE SAS software is installed by default. To check your installation, submit the following SAS code from the SAS editor: proc setinit noalias; run; Check the SAS Log to make sure SAS/STAT is listed and not completely expired. Then, open Windows Explorer or My Computer and see if the STAT folder exists. The default location for this folder is: "c:\program files\sas\sas 9.1". If there is no "c:\program files\sas\sas 9.1\STAT" folder, then SAS/STAT did not get installed. To remedy the problem, use the latest SID file and install SAS again. During the install, you can choose to 'Add components to SAS' and make sure the Select Component screen lists "Education Analytical Suite". To obtain the latest SID file, contact SAS Technical Support at (919)-677-8008 and provide your SAS site number. Operating System and Release Info
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in the log window when you have misspecified a variable name somewhere in your program. Example 8.9. The following example illustrates the "Note: Variable is uninitialized" and "Error: Variable not found" https://onlinecourses.science.psu.edu/stat480/node/69 messages SAS displays in the log window to warn you of such problems: First, http://www.ats.ucla.edu/stat/sas/webbooks/reg/chapter4/sasreg4.htm note that there are two places in which a variable name was misspecified in this program. In the calculation of volume in the DATA step, the height of the tree is referred to as hght rather than hght_ft in which the heights were actually stored. And, in the PRINT procedure, the height of the tree not found is referred to as height. Well, okay, so the programmer, is a little confused! Launch and run the SAS program, and review the log window to see the two messages that SAS displays in this situation. Common ways to "lose" variables include: misspelling a variable name using a variable that was dropped from the data set at some earlier time using the wrong data set committing a logic error, error procedure genmod such as using a variable before it is created If the source of the problem is not immediately obvious, submitting a CONTENTS procedure can often help you sniff out the problem. As you may recall from an earlier lesson, the CONTENTS procedure provides, among other things, the names of the variables contained in a SAS data set. ‹ 8.4 - Invalid Data up 8.6 - Input Reached Past the End of the Line › Printer-friendly version Navigation Start Here! Ready, Set, Go! Search Course Materials Faculty login (PSU Access Account) Lessons Lesson 1: Getting Started in SAS Lesson 2: Reading Data into a SAS Data Set - Part I Lesson 3: Reading Data into a SAS Data Set - Part II Lesson 4: Assignment Statements and Numeric Functions Lesson 5: If-Then-Else Statements Lesson 6: Creating List Reports Lesson 7: Writing Programs That Work - Part I Lesson 8: Writing Programs That Work - Part II8.1 - Missing Semicolons 8.2 - Invalid Options, Names, or Statements 8.3 - Missing Quotation Marks 8.4 - Invalid Data 8.5 - Variable Not Found 8.6 - Input Reached Past the End of the Line 8.7 - Missing Values Generated 8.8 - Summary Lesson 9: The Format Procedure Lesson 10: The REPO
4 - Beyond OLS Chapter Outline 4.1 Robust Regression Methods 4.1.1 Regression with Robust Standard Errors 4.1.2 Using the Proc Genmod for Clustered Data 4.1.3 Robust Regression 4.1.4 Quantile Regression 4.2 Constrained Linear Regression 4.3 Regression with Censored or Truncated Data 4.3.1 Regression with Censored Data 4.3.2 Regression with Truncated Data 4.4 Regression with Measurement Error 4.5 Multiple Equation Regression Models 4.5.1 Seemingly Unrelated Regression 4.5.2 Multivariate Regression 4.6 Summary In this chapter we will go into various commands that go beyond OLS. This chapter is a bit different from the others in that it covers a number of different concepts, some of which may be new to you. These extensions, beyond OLS, have much of the look and feel of OLS but will provide you with additional tools to work with linear models. The topics will include robust regression methods, constrained linear regression, regression with censored and truncated data, regression with measurement error, and multiple equation models. 4.1 Robust Regression Methods It seems to be a rare dataset that meets all of the assumptions underlying multiple regression. We know that failure to meet assumptions can lead to biased estimates of coefficients and especially biased estimates of the standard errors. This fact explains a lot of the activity in the development of robust regression methods. The idea behind robust regression methods is to make adjustments in the estimates that take into account some of the flaws in the data itself. We are going to look at three robust methods: regression with robust standard errors, regression with clustered data, robust regression, and quantile regression. Before we look at these approaches, let's look at a standard OLS regression using the elementary school academic performance index (elemapi2.dta) dataset. We will look at a model that predicts the api 2000 scores using the average