SCSUG 2011 Papers - Index

47 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	"Let's Get SAS To Do It!"	Anna Vincent	This paper will show that a person can be new to SAS and create beneficial reports. Armed with the SAS Basic Programming Essentials Training Manual and using the `ODS` and ExcelXP Tagset, data results will be moved from SUDAAN to SAS and finally into `EXCEL` without touching the data, using the names of the risk factor variables, or how many values those variables had. The jumbled SUDAAN output will be transformed into something that is informative and easy to read. And when my supervisor wanted charts with confidence intervals, the simple solution was incorporating a chart template into the syntax!	dataset, proc step, sample size, confidence interval, sample, excel	source	22
2	A SAS Macro Tool for Selecting Differentially Expressed Genes in Microarray Data	Huanying Qin	DNA Microarrays measure the expression of thousands of genes simultaneously. Commercial software such as JMP/Genomics and GeneSpring use T-tests, ANOVA, or mixed models for statistical analysis to identify differentially expressed genes. These methods are valid for larger sample sizes. We work with an immunology laboratory that often needs to analyze data from experiments with less than 10 samples. The researchers developed an `EXCEL`-based process to select differentially expressed genes from microarray experiments with a small sample size. This process required complex, manual manipulation of data and could take weeks to complete. We created a SAS MACRO to automate the whole process. The program reads microarray data from and provides a summary report in `EXCEL`. The researchers can easily modify the parameters and repeat the analysis. The program made it possible to reduce data processing time from weeks to minutes with no mistakes related to manual manipulation	excel, sample, macro parameter, macro variable, macro definition, sample size, variable list, do loop, macro loop, proc sql	source	7
3	A Simulation Study for Power Analysis in a Longitudinal Study Using SAS	Ravi Lingineni	Background: In many longitudinal studies, a subject's measurement is not available for each time period. To study the incomplete data across time and the influence of subjects on their repeated observations, mixed-effect regression models (MRMs) can be used. The purpose of this research is to study the power characteristics of the likelihood ratio test for hierarchical correlated data using MRMs. Methods: We conducted simulation study based upon 10,000 replicates of data. We fitted a random intercept model employing a longitudinal design. We studied the effect of varying sample size and the number of measurements (time points) on power. Data was generated assuming a mixed random model with normally distributed error with mean of 0 and a pre-specified variance. We assumed a 20% drop out rate	sample size, sample, power analysis, proc step, likelihood function, proc mixed, random intercept	source	4
4	A Well Designed Process and QC Tool for Integrated Summary of Safety Reports	Huei-Ling Chen	The ISS (Integrated Summary of Safety) is a key component in submissions for drug approvals in the pharmaceutical industry. This report consists of multiple reports from clinical studies that focus on drug safety and are generally programmed in SAS. The ISS uses relevant data from one or more clinical studies to generate tables and figures from integrated data. Various methods are used to verify the results found on the ISS reports. One method focuses on whether the ISS analysis results from the integrated data are consistent with the results from each of the individual studies. This paper introduces a well- designed process and validation tool to ensure the output consistency and integrity of the ISS reports with the individual underlying studies	adverse events, out=, dataset, treatment arm, proc sql, clinical trial, proc step	source	6
5	Advice to Health Services Researchers: Be Cautious Using the "Where" Statement in SAS Programs for Nationally Representative Complex Survey Data	Hemalkumar Mehta	Health services researchers often conduct research with nationally representative survey data where participants or patients are not sampled randomly but sampled using complex stratified multistage probability designs. Such datasets include cluster, strata and weight information which are essential for extrapolation of results to a national level. Several `PROC` Survey procedures are available in SAS 9.2 which enables analysis of such data while preserving the complex sampling design and extrapolation of results. The first step researchers often perform is selection of a population of interest, i.e. selection of participants with certain inclusion criteria, from the main dataset. This can be accomplished in SAS using the 'where' statement in data steps. However, use of the where statement for selecting a population of interest can defeat the purpose of the sampling design of such data and limits researcher's ability to generalize results	proc step, data science, sample, random sampling, proc surveymeans, proc calis, where statement, binary variable, confidence interval	source	10
6	An Introduction to SAS Hash Programming Techniques	Kirk Paul Lafler	SAS users are always interested in learning techniques that will help them improve the performance of table lookup, search, and sort operations. SAS software supports a `DATA STEP` programming technique known as a hash object to associate a key with one or more values. This presentation introduces what a hash object is, how it works, the syntax required, and simple applications of it use . Essential programming techniques will be illustrated t o sort data and search memory-resident data using a simple key to find a single value	hash table, lookup, sas log, composite key, proc sort	source	8
7	An Introductory Look at the Situational Context Inherent in the RBI Using Two Modeling Approaches	Ryan Sides	The RBI (run batted in) is a popular statistic in Major League Baseball that is extremely dependent on the situational context (i.e. , which bases are occupied by runners and the number of outs in an inning) experienced by the hitter. This paper offers insight into how much this situational context affects the RBI , providing a couple of related modeling approaches that account for this information and, thus, an approach for improving a player's evaluation . The first model used to accomplish this goal is a standard multiple regression model, where the number of at -bats in each possible situation a player experiences are the independent variables. Subtracting a player's model predicted RBI count, Î , from his actual RBI total from the season produces a performance statistic that can be described as the number of runs batted in above or below average that player achieved for the season	regression analysis, correlation, prediction interval, predictor variable, sample, proc step, multiple regression, normality test, proc reg, histogram, response variable	source	14
8	At Random - Sampling with PROC SURVEYSELECT	Patricia Hettinger	`PROC SURVEYSELECT` can be a very useful tool in sampling data. This paper covers some basic features including comparisons with the ranuni function so often seen by the author	proc surveyselect, proc step, sample, proc freq, raw data, proc ttest, sample size	source	15
9	Becoming a Better Programmer with SAS Enterprise Guide 4.3	Michael Walters	Both existing and new users of SAS are turning to SAS Enterprise Guide to write and run their code. Long-time users are accustomed to typing all their code in the Program Editor window and simply clicking Submit. New users do not have this same set of expectations and are more willing to point and click on occasion. But the truth is becoming clear; the winning programmer will be the one who has the expertise to use the best of both worlds-either coding or clicking, depending on which is more efficient for a given task. SAS Enterprise Guide 4.3 contains new functionality that can help anyone become a better programmer. This paper addresses the all-important question: When is it appropriate to code, and when to click? The purpose here is to expose new users as well as those familiar with SAS to tips and best practices that will enable them to return to the office as better programmers	sas enterprise guide, proc step, excel, dataset, proc transpose, substr function, sas programmer, work library	source	40
10	Can't Decide Whether to Use a DATA Step or PROC SQL? You Can Have It Both Ways with the SQL Function!	Jeremy Poling	Have you ever thought that it would be nice if you could execute a `PROC SQL` SELECT statement from within a `DATA STEP`? Well, now you can! This paper describes how to create a `DATA STEP` SQL function using the `FCMP` procedure and the RUN_MACRO function. The SQL function accepts a character expression analogous to a SELECT statement as its only argument. By using the SQL function, you now have the ability to integrate the `DATA STEP` and the `SQL` procedure	proc sql, proc fcmp, select, proc step	source	7
11	Choosing the Road Less Traveled: Performing Similar Tasks with Either SAS DATA Step Processing or with Base SAS Procedures	Kathryn McLawhorn	When you are seeking a solution that requires SAS code, do you choose the path that is marked with `DATA STEP` programming? Or, do you prefer the path that applies Base SAS procedures? Programmers often tend to use just one path, based on their programming skills and personal preferences. In many situations, you can use either `DATA STEP` logic or procedure code to arrive at the same solution. To help broaden your horizons, this paper provides examples of common situations in which using either method leads to the same result. While this paper compares coding methods, it does not promote one particular method nor examine differences in efficiency. The discussion is aimed at intermediate to high-level users. Whether you are a loyal `DATA STEP` coder or a devoted proponent of procedure syntax, this paper offers some alternative techniques to help diversify your programming knowledge and skills	proc step, by group processing, proc report, proc tabulate, proc print, sas base, proc freq, numeric variable, class variable, proc transpose, proc means	source	12
12	Don't Gamble with Your Output: How to Use Microsoft Formats with ODS	Cynthia L. Zender	Are you frustrated when `EXCEL` does not use your SAS formats for number cells? Do you lose leading zeros on ZIP codes or ID numbers? Does your character variable turn into a number in `EXCEL`? Don't gamble with your output! Learn how to use the HTMLSTYLE and TAGATTR style attributes to send Microsoft formats from SAS to `EXCEL`. This paper provides an overview of how you can use the HTMLSTYLE attribute with HTML-based destinations and the TAGATTR attribute with the `TAGSETS`.EXCELXP destination to send Microsoft formats from SAS to `EXCEL` using the Output Delivery System (`ODS`) STYLE= overrides. Learn how to figure out what Microsoft format to use and how to apply the format appropriately with `ODS`. A job aid is included in the paper; it lists some of the most common Microsoft formats used for numeric data. The examples in this paper demonstrate `PROC PRINT`, `PROC REPORT`, and `PROC TABULATE` coding techniques	excel, proc step, ods xml, proc report, ods html, proc print, date variable, format statement, character variable, sysdate9, user-defined format, numeric variable, proc tabulate, xls, sas output, output delivery system, dataset	source	30
13	Ethnicity and Race: When Your Output Isn't What You Expected	Philamer M. Atienza	In SAS, when a classification variable is used to group observations with the same values and a formatted value is used for grouping data, unexpected results may come out of the procedure. If there is more than one unformatted value used for several distinct categorizations but with the same format label, SAS uses the unformatted lowest value to create the output. Understanding the behavior of SAS when storing the unformatted values will help avoid potential mistakes in using formats and nested classification variables. This paper examines two scenarios when a variable for both ethnicity and race is used in `PROC TABULATE` to create an output data set: (1) with, and (2) without the use of a format	proc tabulate, proc step, sample, proc freq	source	5
14	Evaluation of Promotional Campaigns in the Utilities Industry Using a Transfer Function Time-Series Model	Fujiang Wen	Promotional campaigns are often used by utilities industries to increase total sales level of their products or services. Evaluation of the effectiveness of the campaigns is a key component for the utilities to effectively use their resources because the campaigns normally require expenses. A transfer function for the ti me-series model was applied for an analysis of the direct bill insert advertising c to promote a new toilet replacement program for Dallas Water Utilities (DWU) customers. The analysis was based on the numbers of customers who participated in the program from August, 2007 to September, 2010. A point intervention function was used to indicate three times of the advertising campaigns, with the model to quantify the promotional effect. As a resu lt, an exponential function was identified to describe the effect	time series analysis, chi-square test	source	10
15	Exploring trends in topics via Text Mining SUGI/Global Forum proceedings abstracts	Zubair Shaik	Many organizations across the world have already realized the benefits of text mining to derive valuable insights from unstructured data. While text mining has been mainly used for information retrieval and text categorization, in recent years text mining is also being used for discovering trends in textual data. Given a set of documents with a time stamp, text mining can be used to identify trends of different topics that exist in the text and how they change over time. We apply Text Mining using SAS Text Miner 4.3 to discover trends in the usage of SAS tools in various industries via analyzing all 8,429 abstracts published in SUGI/SAS Global Forum from 1976 to 2011. Results of our analysis clearly show a varying trend in the representation of various industries in the conference proceedings from decade to decade. We also observed a significant difference in the association of key concepts related to statistics or modeling during the four decades	proc freq, dataset	source	11
16	Finding Oracle Table Metadata: When PROC CONTENTS Is Not Enough	Jeremy Poling	Complex Oracle databases often contain hundreds of linked tables. For `SAS/ACCESS` Interface to Oracle software users who are unfamiliar with a database, finding the data they need and extracting it efficiently can be a daunting task. For this reason, a tool that extracts and summarizes database metadata can be invaluable. This paper describes how Oracle table metadata can be extracted from the Oracle data dictionary using the `SAS/ACCESS` `LIBNAME` statement in conjunction with traditional SAS `DATA STEP` programming techniques. For a given Oracle table, we discuss how to identify the number of observations and variables in the table, variable names and attributes, constraints, indexes, and linked tables. A macro is presented which can be used to extract all the aforementioned metadata for an Oracle table and produce an HTML report	key, foreign key, proc contents, call symputx, macro variable, proc step, data dictionary, oracle, libname statement, sas programmer, constraint, lookup, dataset, autocall macro, proc sql, dictionary tables	source	12
17	Intro to Longitudinal Data: A Grad Student "How-To" Paper	Elisa L. Priest	Grad students learn the basics of SAS programming in class or on their own. Although students may deal with longitudinal data in class, the lessons focus on statistical procedures and the datasets are usually ready for analysis. However, longitudinal data may be organized in many complex structures, especially if it was collected in a relational database. Once students begin working with 'real' longitudinal data, they quickly realize that manipulating the data so it can be analyzed is its own time consuming challenge. In the real world of messy data, we often spend more time preparing the data than performing the analysis. Students need tools that can help them survive the challenges of working with longitudinal data. These challenges include combining datasets, counting repeat observations, performing calculations across records, and restructuring repeating data from multiple observations to single observation	proc step, longitudinal data, dif function, proc contents, proc sort, proc transpose, adam dataset, retain, proc freq, by group processing	source	14
18	Kass Adjustments in Decision Trees on Binary/Interval Target Variable	Manoj Immadi	Kass adjustment maximizes the independence between the two branches after the split. But how will these adjustments work on interval and binary target variable is a big question? This paper describes split search algorithm in decision trees for selecting useful inputs, followed by comparing the decision tree results of binary target variable and an interval target variable and also provides insights to Kass adjustments/Bonferroni correction in decision trees	response variable, proc hpsplit, post hoc test, missing value, data mining	source	10
19	Looking Beneath the Surface of Sorting	Andrew T. Kuligowski	Many things that appear to be simple turn out to be a mask for various complexities. For example, as we all learned early in school, a simple drop of pond water reveals a complete and complex ecosystem when viewed under a microscope. A single snowflake contains a delicate crystalline pattern. Similarly, the decision to use data in a sorted order can conceal an unexpectedly involved series of processing and decisions. This presentation will examine multiple facets of the process of sorting data, starting with the most basic use of `PROC SORT` and progressing into options that can be used to extend its flexibility. It will progress to look at some potential uses of sorted data, and contrast them with alternatives that do not require sorted data. For example, we will compare the use of the BY statement vs. the CLASS statement in certain PROCs, as well as investigate alternatives to the `MERGE` statement to combine multiple datasets together	proc sort, proc step, missing value, by group processing	source	10
20	Peeking into Windows to improve your SAS Performance	Michael Patrick Welch			source	27
21	Practically Perfect Presentations	Cynthia L. Zender	Presentation: `PROC REPORT` is a powerful reporting procedure, whose output can be "practically perfect" when you add `ODS` STYLE= overrides to your `PROC REPORT` code. This hands-on workshop will feature several `PROC REPORT` programs that produce default output for `ODS HTML`, RTF and PDF destinations. Workshop attendees will learn how to modify the defaults to change elements of `PROC REPORT` output, such as HEADER cells, DATA cells, SUMMARY cells and LINE output using `ODS` STYLE= overrides. In addition, attendees will learn how to apply conditional formatting at the column or cell level and at the row level using `PROC FORMAT` techniques and CALL DEFINE techniques. Other topics include: table attributes that control interior table lines and table borders, use of logos in output and producing "Page x of y" page numbering. The rest of this document is divided into two parts: the slides used in the Hands-On Workshop presentation and the attendee worksheet	proc report, proc step	source	40
22	Prediction on Diabetes Using Data Mining Approach	Pardha Repalli	The main purpose of this paper is to predict how likely the people with different age groups are being affected by diabetes based on their life style activities and to find out factors responsible for the individual to be diabetic. Hence it is interesting to implement statistical techniques in medical f ield to understand which age group of people are being affecting by diabetes	proc hpsplit, response variable, data cleaning, validation set, regression analysis	source	10
23	PROC SURVEYFREQ: Why Do a Three Way Table in SAS When We Want Two Way Table Information?	Hemalkumar Mehta	A `PROC SURVEYFREQ` procedure in SAS has an advantage over `PROC FREQ` in that it incorporates multi-stage probability sampling design into the analyses. Several nationally representative data have multi-stage probability sampling design. Most of the time we need two way table information for the group of our interest, e.g.: patients with a certain disease. There are two ways to get group specific results in `PROC SURVEYFREQ`: (i) use "by statement" (ii) do "three way tabulation." "By statement" will provide group specific results but it will not give valid domain analysis and it will not preserve the sampling design. Hence, the results will not be generalizable to the population level. "Three way tables" will provide group specific results with valid domain analysis while preserving the sampling design	by group processing, proc step, proc surveyfreq, proc calis, relative risk, sample, odds ratio, random sampling, sample size, proc freq, confidence interval	source	14
24	PROC TABULATE: Getting Started	Arthur L. Carpenter	Although `PROC TABULATE` has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented	proc step, proc tabulate, missing value, proc format, output delivery system, format statement	source	19
25	Reading and Processing Mystery Data Sets	Jimmy A. DeFoor		macro variable, proc sql, do loop, symbol table, variable, macro loop, proc step, dataset, stored macro, call symput, data dictionary, length statement, array, macro definition	source	13
26	SAS Information Studio - Map Your Way Through the Data	Alejandro Farias	This reference document can serve as a summary instructional tool for SAS Information Studio and is written to assist those responsible for providing access to data, such as an information architect, for data consumers. Topics covered include: Selecting Tables Table Relationships Selecting Data Items Organizing Data Items Creating a Custom Category or Calculated Data Item Single and Combination Filters Prompted Filters Test Queries * Resource Replacement/Moving/Saving Information Maps	data dictionary, dataset	source	17
27	SAS-Implementations Supporting Satellite, Aircraft, and Drone-based Remote Sensing Endeavors and Their Influences in the Classroom	Cecil R. Hallum	SAS has been a significant "partner" over a career spanning 40 years for this researcher. This presentation summarizes key SAS applications in satellite, aircraft and drone-based remote sensing endeavors (beginning in the early "70's when SAS was first implemented at N ASA/Johnson Space Center). The coverage includes a discussion of recent multivariate strategies implemented in SAS geared toward finding missing bodies in digital imagery collected from drone flights as well as current research oriented toward improving t he accuracies and speeds of such capabilities. Discussion of the impact of this research in the classroom for educational purposes at the university and high school levels is emphasized as well. This presentation is also being done as a follow-up to a request from a SAS representative in attendance at a 2007 SCSUG meeting	sample, proc step, maximum likelihood estimation, proc surveyselect, likelihood function	source	16
28	Statistical comparison of relative proportions of bacterial genetic sequences	Jose F Garcia-Mazcorro		sample, proc step, proc mixed, confidence interval, proc glimmix, beta distribution, proc calis, anova, response variable, repeated measures, binomial distribution, covariance, group by, predictor variable, data science, outlier, correlation	source	19
29	Take a Fresh Look at SAS Enterprise Guide: From point-and-click ad hocs to robust enterprise solutions	Christopher W. Schacherer	Early versions of SAS Enterprise Guide (EG) met with only lukewarm acceptance among many SAS programmers. As EG has matured, however, it has proven to be a powerful tool not only for end-users less familiar with SAS programming constructs, but also for experienced SAS programmers performing complex ad hoc analyses and building enterprise class solutions. Still, many experienced SAS programmers fail to add EG to their SAS toolkit. They face the barriers of an unfamiliar interface, new nomenclature, and uncertainty that the benefits of using EG outweigh the time spent mastering it. Especially for this group, (but also for analysts new to SAS), the present work attempts to orient new EG users to the interface and nomenclature while teaching them how to achieve common data management and analytic tasks they perform with ease in SAS. In addition, EG concepts and techniques that focus on using EG as a development environment for producing end-user analytic solutions are described	sas enterprise guide, proc step, sas programmer, sas enterprise guide project, out=, proc sort, where statement, proc sql, if-then-else, sas programming, libname statement, macro variable, excel	source	26
30	The easy way to include XML formatted data into your SAS reports	Mary Grace Crissey	Originally designed to meet the challenges of large-scale electronic publishing, XML is playing an increasingly important role in the exchange of a wide variety of data on the Web and elsewhere. Many heterogeneous information systems have chosen XML as their preferred method of data exchange - especially in pharmaceutical and medical environments, with usage growing in the financial and educational domains. This paper illustrates a case study when I was given student test records and school demographic information as an XML file and asked to run standard SAS reports based on those inputs. With the stand alone utility tool , SAS XML Mapper 9.21, I was able to unlock the mystery of the XML "foreign" data and its hidden metadata structure . Interactively running the XML Mapper tool, I am able to visually ensure the hierarchical tree structure conforms to the given schema and thus come up with the syntax necessary to build a map to feed into the SAS `LIBNAME` engine	libname engine, dataset, sas base, proc step, libname statement, raw data, sas library, excel	source	7
31	The FILENAME Statement: Interacting with the World Outside of SAS	Christopher W. Schacherer	The `FILENAME` statement has a very simple purpose-to specify the fileref (or, file reference) that serves as the link to an external file or device. The statement itself does not process any data, specify the format or shape of a dataset, or directly produce output of any type, yet this simple statement is an invaluable SAS construct that allows SAS programs to interact with the world outside of SAS. Through specification of the appropriate device type, the `FILENAME` statement allows SAS to symbolically refer to external disk files for the purpose of reading and writing data, interact with FTP Servers to read and write remote files, send e-mail messages, and gather data from external programs-including the local operating system and remote web services. The current work explores these uses of the `FILENAME` statement and provides examples of how you can use the different device types to perform a variety of data management tasks	filename statement, web service, input statement, macro variable, infile statement, dataset, proc step, put statement, excel, file statement, flat file, missing value, sas programmer, txt, sas log, delimited file, fixed-width file, macro quoting function	source	27
32	The Greatest Hits: ODS Essentials Every User Should Know	Cynthia L. Zender	Just when you think you know every song (feature) in the `ODS` hit parade, you discover that there's an option or destination or feature that has you singing its praises because the feature boosted your reports to the next level. This paper covers some of the essential features and options of `ODS` that every user needs to know to be productive. This paper shows concrete code examples of the `ODS` "Greatest Hits". Come to this session and learn some of the essential reasons why `ODS` and Base SAS rock!	ods html, excel, proc step, proc tabulate, proc print, proc report, output delivery system, sas log, proc reg, ods pdf, file statement, proc template, dataset, output, proc univariate, ods output, ods csv, ods rtf, anova, by group processing, ods trace, estimate, select, txt, user-defined format, sas output	source	34
33	The Top 10 Head Scratchers: SAS Log Messages That Prompt a Call to SAS Technical Support	Kim M. Wilson	As a `DATA STEP` programmer, you know the sigh of relief that comes when your job finishes and your SAS log is clear of any errors or warnings. When these messages do occur, most of the time they are intuitive enough so that you can move directly to the offending code, make the necessary changes, and complete the job successfully. However, what do you do about those perplexing messages that sometimes appear, making you scratch your head in puzzlement? This paper examines the top 10 errors, notes, and warnings that prompt `DATA STEP` programmers to call SAS Technical Support. The discussion addresses the common causes of the messages and provides solutions so that you can reduce your troubleshooting time and effort	character variable, input statement, options statement, sample, infile statement, merge statement, sas log, error message, dataset, length statement, numeric variable, put statement, by group processing, user-defined format, if-then-else, proc print, proc step	source	9
34	The Way of the Semicolon or Three Things I Wish I Had Known Before I Ever Keyed One	Patricia Hettinger	Learning SAS or teaching it to someone else can be very difficult. The author has found that understanding three main aspects of SAS very helpful. These are of course, the semicolon, the physical nature of a SAS data set and the two major data types. The intended audience is for those who are new to SAS or new to training others in it	dataset, proc step, proc contents, proc print, sysdate9, numeric variable	source	8
35	They're Closing Down the Office in Kalamazoo and You've Been Tapped to Run Their In-house "Production" Reporting System, OH MY!	David Welch	The author's experience in running a homegrown reporting system for a major corporation is described as well as these features of the system: Setting the SAS environment using command line switches and the autoexec.sas file. Controlling program start times within the SAS `DATA STEP`. Creating and maintaining report distribution lists using SAS formats. Modularizing reports using the SAS macro language and `%INCLUDE` The benefits of this ad hoc system will be compared to other alternatives. The author will discuss the lessons learned from this assignment and suggestions for making projects easily supportable for others that may follow. Introduction A unit of a major corporation needed reports to run various aspects of their business. Due to realignment in the company, the group that had formerly supplied database services to the unit was sold to an outside firm	create table, data step, format statement, macro definition	source	15
36	Tracking and Reporting Account Referral Activity using Hash tables and SAS BI	James L. Beaver, Tobin E. Scroggins	One of the tasks of the analysis division at the bank is to keep track of new account referrals made by bank representatives based upon location, sales territory and manager. This is made more difficult because over time representatives may change their sales territory, no longer be active, report to different managers or more than one manager, or their reporting entities may change over time. The reporting requirements include being able to report on all activity based upon current sales territory and manager as well as to report on activity based upon sales territory and manager at the time the referral was made. Referrals by sales territory need to be able to report all referrals in that territory over a period of time by all representatives as well as only those sales made by currently active representatives. To handle these requirements, a slowly changing dimension table is created as part of a data warehouse	hash table, dimension table, slowly changing dimension, fact table, agent, cpu time	source	9
37	Traffic Lighting: The Next Generation	Julie VanBuskirk	Traffic lighting is a tool intended to let a reader quickly evaluate data and at--a--glance sort out good versus bad performance. As the name implies, traditional traffic lighting generally separates data into three categories highlighted with red, yellow, and green based on performance. However, if all the cells in a table are boldly colored in primary colors, the reader is unable to achieve their goal of easily sorting between good and bad results. In addition, colorblind users will not be able to even distinguish between colors. In this paper we will present the visual design changes made to our output and the SAS techniques behind them. We sought to reduce the amount of decoration in our tables and focus on what was important, the data! We employed a more subtle use of cell shading and borders to effectively draw the reader's eye to the results that most need their attention	proc format, proc step, macro variable	source	9
38	Type Less, Do More: Have SAS do the typing for you	Jeanina M. Worden			source	5
39	Understanding the Anatomy of a SAS Deployment: What's in My Server Soup?	Mark L. Schneider	Did you want to better understand the pieces of a SAS metadata-based deployment and where they go and what they do? This paper highlights the major components of a metadata-based SAS deployment of solutions and business intelligence. Using the concepts of topology and tiers as a framework, this paper illustrates the location of key components and how they can be distributed across machines	sas base, business intelligence, sample, data mining, sas enterprise guide, proc sql, dataset	source	18
40	Unveiling the Power of Cascading Style Sheets (CSS) in ODS	Kevin D. Smith	Prior to SAS 9.2, `PROC TEMPLATE` was the only choice for writing `ODS` styles. SAS 9.2 added the capability of writing styles using Cascading Style Sheets (CSS) syntax. However, the accepted CSS syntax was primarily a remapping of `PROC TEMPLATE` elements and attributes to CSS classes and properties. While this remapped syntax was a step in the right direction, no new capabilities were added to the styles themselves. CSS support in SAS 9.3 takes a bigger leap toward the power of CSS seen on the Internet today. While most people think of CSS as an HTML-only technology, it is a style syntax that can also be applied to other `ODS` outputs such as PDF, RTF, and ExcelXP. In fact, CSS in SAS 9.3 works with any of the `ODS` outputs that use styles. This paper uncovers the hidden power of CSS in SAS 9.3 and shows you things that were never before possible in previous versions of SAS as well as directions SAS will be taking in the future	proc template, proc step, ods html, dataset	source	23
41	Use of Cutoff and SAS Code node in SAS Enterprise Miner to determine appropriate probability cutoff point for decision making with binary target models	Yogen Shah	This paper illustrates the effective use of Cutoff and SAS Code node in SAS Enterprise Miner to change the default cut off value of predicted probability during decision making with binary target models. SAS, by default uses cut -off value of 0.5 to predi ct a binary outcome from predicted probabilities i.e. the chance of primary outcome is same as that of a secondary outcome. A cut -off value of 0.5 is unacceptable because the observed proportion of a primary outcome in a given population can never be 50%. SAS EM provides Cutoff node to adjust probability cut-off point based on model's ability to predict true positive, false positive & true negative. We need to add small snippets of SAS code under "score" section of SAS Code node to account for the new cut -off value in scoring dataset. This paper introduces a Technique to analyze probability cut-off using SAS Enterprise Guide as well	type i error, data mining, sas enterprise guide, response variable	source	7
42	Using Proc Logistic, SAS Macros and ODS Output to evaluate the consistency of independent variables during the development of logistic regression models. An example from the retail banking industry	Alexandros Vidras	Predictive models are used extensively in customer relationship management analytics and data mining to increase the effectiveness of marketing campaigns. Logistic regression remains at the forefront in analytics as the most popular technique used to predi ct customer behavior. Particularly with direct mail marketing, logistic regression models are built using previous campaigns that span several months in length, posing a major challenge to statisticians to devise a way to not only capture seasonality acros s these campaigns but to also evaluate the stability of these models. Millions of dollars are spent annually on marketing activities that utilize logistic regression models. Therefore the predictive ability and robustness of logistic models is essential for executing a successful direct mail campaign	predictor variable, logistic regression, logistic distribution, regression analysis, estimate, proc logistic, proc step, response variable, sample, proc calis, out=, retail banking	source	9
43	Using SASHELP to Validate Attributes	Sadia Abdullah	SASHELP is a SAS library that is made available at the start of a session. It contains views or tables which provide information about the session. These views or tables contain what can be considered as the metadata of the entire SAS session. This paper gives an introduction to SASHELP and how it can be used in various situations. It lists some important views from this library and mentions the application of all these views	dictionary tables, proc sql, proc step, do loop, where statement, macro variable, sashelp, excel	source	6
44	What's Hot, What's Not - Skills for SAS Professionals	Kirk Paul Lafler, Charles E. Shipp	As a new generation of SA S user emerges, current and prior generations of users have an extensive array of procedures, programming tools, approaches and techniques to choose from. This presentation identifies and explores the areas that are hot and not -so-hot in the world of the professional SAS user. Topics include Enterprise Guide, `PROC SQL`, `PROC REPORT`, `PROC FORMAT`, Macro Language, `ODS`, `DATA STEP` programming techniques such as arrays and hashing, business intelligence, sasCommunity.org, JMP, `SAS/GRAPH`, `SAS/STAT`, and	sas 9, sas base, proc step	source	6
45	When It's Not Random Chance: Creating Propensity Scores Using SAS EG	Josie Brunner	While randomized samples are ideal for hypothesis testing, they are not always possible, especially when evaluating programs where there is a self-selection process for treatment or control group. One quasi-experimental design approach is to use propensity scores to match treatment and control units to reduce selection bias in observable pre-treatment characteristics. This paper will focus on why and when propensity score analysis (PSA) should be included in a research design and will demonstrate how propensity scores can be created very simply using SAS EG 4.3	propensity score, logistic regression, regression analysis, logistic distribution, sas enterprise guide, sample, standard deviation, box plot, response variable	source	13
46	You Can't Get There From Here If You Don't Know Where Here Is. Improving the SAS Enterprise Guide Data Characterization Task	Patricia Hettinger	How many times have you been fooled by the name of a data element but found completely different values than you would expect? SAS Enterprise Guide has a characterization task that could give you better understanding but it has several drawbacks. One is that it will run frequencies on all character data regardless of length or number of distinct values. This can result in some variables being dropped from the output due to too many distinct values. It also results in frequencies not being run for numeric values at all. Another is that minimum, maximum, number of missing values and number of non-missing values will be calculated only for numeric variables when this information would be useful for any variable. A third issue is the likelihood of your system hanging when attempting to analyze large data sets with many variables. This paper details how you can overcome these obstacles as well as incorporating your profile results into a useful mapping document	proc step, missing value, proc freq, response variable, call symput, proc sql, proc report, sas enterprise guide, macro variable, raw data, excel	source	16
47	You Could Be a SAS Nerd If . . .	Kirk Paul Lafler	Are you a SAS nerd? Wiktionary, a wiki -based Open Content dictionary, defines "nerd" as a person who has good technical or scientific skills, but is generally introspective or introverted. Another definition is a person who is intelligent but socially and physically awkward. Obviously there are other definitions for "nerd", many of which are associated with derogatory terms or stereotypes. This presentation focuses not on the negative descriptions associated with being a nerd, but on the many positive traits SAS users possess. So let's see how nerdy you actually are using the mostly unscientific, but fun, "Nerd" detector	sas programmer, sas log	source	4

Back to Conf go 2012 go 2010

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.