HOME
QUICK SEARCH
AUTHORS
KEYWORDS
HELP
BUGSSESUG 2010 Papers - Index
92 papers. Source: SESUG (SouthEast SAS Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %RESTRUCT - SAS macro with Proc Univariate | Milorad Stojanovic | PROC UNIVARIATE has a long history as a part of the SAS Base software package. It is very handy and useful. It allows users to quickly analyze raw as well as final data at a glance. It does, however, come with a price - users often have to browse many pages of PROC UNIVARIATE report results. Macro %RESTRUCT relieves you of that burden by summarizing the data you need to examine most. The summary results are presented in a very condensed form. Users should specify their source of data, statistics they would like to have and the output file location. |
proc univariate, proc step, outlier, macro variable, proc transpose, dataset, macro loop, %if-%then-%else, macro definition | source | 7 |
| 2 | %SBSBOXPLOT: A SAS Macro for Generating Side-by-Side Boxplots | Jason A. Schoeneberger, Grant B. Morgan, Bethany A. Bell |
Good research practice includes a basic exploration of data prior to engaging in more sophisticated, inferential analyses. Generating location and dispersion summary statistics such as the mean or median and standard deviation or interquartile range help describe variable distributions. Whereas these descriptive statistics can be reported in tabular or narrative format, they can also be reported visually. One efficient graphical display that clearly communicates a host of summary statistics, including the mean, median, 10th, 25th, 75th, and 90th percentiles, and the minimum and maximum values is the box-and-whisker plot (Tukey, 1977), often simply referred to as a boxplot. Thus, when examining a boxplot, a reader can quickly assess measures of central tendency and dispersion, as well as the shape of the distribution for any given continuous variable. Moreover, side-by-side boxplots allow easy communication of patterns evident in data by viewing graphical displays in clusters or blocks to accentuate relationships among categorical or discrete variables or factors (e.g., examining standardized test scores by subject and student race/ethnicity or adolescent BMI by student gender and age). However, the SAS programming necessary to generate side-by-side boxplots is not intuitive and cumbersome at best. To ease this burden, using various data management procedures in conjunction with PROC BOXPLOT, SBSBOXPLOT was developed to facilitate the generation of side-by-side boxplots for displaying distributional information disaggregated by factors important for a basic understanding of one's data. This paper provides the macro programming language, as well as results from an executed example of the macro. |
box plot, proc step, proc boxplot, response variable, data visualization, proc sql, proc univariate, dataset, proc freq, data governance, predictor variable, macro variable, proc means | source | 16 |
| 3 | %whatChanged: A Tool for the Well-Behaved Macro | Frank C. DiIorio | The power and usefulness of macros is undeniable. Also above dispute is the ability of a poorly constructed macro to introduce chaos in programs that use it. Many of these undesirable side effects can be prevented by following some simple design and coding practices. One of these practices, and the focus of this paper, is that the macro should produce only what is described in its documentation. If, for example, it is supposed to create a dataset and a macro variable containing the number of observations in the dataset, then these items should be the only changes to the SAS environment once the macro terminates. There are no built-in SAS tools that compare "snapshots" of resources and settings. Not surprisingly, %whatChanged, the macro described in this paper, performs this task. This paper very briefly discusses some principles of good macro design. It then describes the design, coding, and use of %whatChanged. While discussion of the macro is useful in and of itself, the other goal of the paper is to give the reader some insight and motivation to develop similar tools. The paper is appropriate for anyone charged with developing, debugging, or enhancing macros. A basic knowledge of the macro language is assumed. | macro variable, create table, order by, sas log, macro facility, out=, %if-%then-%else, proc sql, macro definition | source | 14 |
| 4 | A Macro for Calculating Summary Statistics on Left Censored Environmental Data using the Kaplan-Meier Method | Dennis J. Beal | Calculating summary statistics such as the mean, standard deviation, and an upper confidence limit on the mean is straightforward when the data values are known. However, environmental data often are reported back from the analytical laboratory as left censored, meaning the actual concentration for a given contaminant was not detected above the method detection limit. Therefore, the true concentration is known only to be between 0 and the reporting limit. The nonparametric Kaplan-Meier product limit estimator has been widely used in survival analysis on right censored data, but only recently has this method been applied to left censored data. Kaplan-Meier can be used on left censored data with multiple detection limits with minimal assumptions. This paper presents a SAS macro that calculates the mean, standard deviation, and standard error of the mean of a left censored environmental data set using the nonparametric Kaplan-Meier method. Kaplan-Meier has been shown to provide more robust estimates of the mean and standard deviation of left censored data than other methods such as simple substitution and maximum likelihood estimates. This paper is for intermediate SAS users of SAS/BASE. |
censoring, standard deviation, proc calis, sample, left censoring, proc means, survival analysis, confidence interval, maximum likelihood estimation, likelihood function, macro variable | source | 6 |
| 5 | A SAS Macro to Compute Added Predictive Ability of New Markers Predicting a Dichotomous Outcome | Kevin F. Kennedy, Michael J. Pencina |
Risk prediction is an important field in applied statistics. For example, in clinical research, predicting the development of adverse medical conditions is the object of many studies. However, this concept is multi-disciplinary. Many published models exist to predict dichotomous outcomes, ranging widely from a model to predict winners in the NCAA basketball tournament to a recently published risk model to predict the likelihood of a bleeding complication after percutaneous coronary intervention. It is important to note, however, these models are dynamic, as changes in population and technology lead to discovery of new and better methods to predict outcomes. An important consideration is when to add new markers to existing models. While a significant p-value is an important condition, it does not necessarily imply an improvement in model performance. Traditionally, receiver operating characteristic (ROC) curves and its corresponding area under the curve (AUC) are used to compare models, with a statistical test due to DeLong et al. for two correlated AUCs. However, the clinical relevance of this metric has been questioned by researchers. To address this issue, Pencina and D'Agostino have proposed two statistics to evaluate the significance of novel markers. The Integrated Discrimination Improvement (IDI) measures the new model's improvement in average sensitivity without sacrificing average specificity. The Net Reclassification Improvement (NRI) measures the correctness of reclassification of subjects based on their predicted probabilities of events using the new model with the option of imposing meaningful risk categories. | logistic distribution, proc step, proc logistic, macro definition, create table, proc sql, logistic regression | source | 13 |
| 6 | A SAS/AF Application for Organizing the Data Management Activities of the CHIMES Follow Up Study | Emily A. Mixon, Valisa R. Brown, Karen B. Fowler |
The CMV & Hearing Multicenter Screening (CHIMES) Study is a multi-center study to define the contribution of congenital cytomegalovirus (CMV) infection in childhood hearing loss. The CHIMES study consists of two parts, the screening study and the follow up study. The follow up study consists of children who test positive for congenital CMV infection. The participants are followed from infancy to 4 years of age to monitor any changes in their hearing status. All data for the CHIMES study are entered by scanning TELEform designed data forms and using the TELEform software to read, process, and verify the data. Once the data are verified they are saved in a comma separated value (CSV) format. SAS programs are run on the data while in the CSV files for identifying logic and possible data entry errors prior to making the data permanent in SAS datasets. Once the data are converted to SAS data files any changes made to the data are tracked using SAS/FSEDIT screens. Using the data in the SAS data files, a listing of CMV confirmation results received for the enrollment visit of new study participants may be generated. This SAS/AF application has been developed to help organize the data management tasks of the follow up study. Separate frames with icons are used to list the twenty-four pre-SAS programs and sixteen FSEDIT screens the user may choose to run with a third frame available for generating the list of CMV confirmation results by way of an ODS PDF report form. |
dataset, proc step, sas output | source | 7 |
| 7 | A Serious Look Macro Quoting | H. Ian Whitlock | You can make decisions macro with %IF and do looping with %DO-loops. But there are times when you don't understand why the beast does what it does. Now what? It is time to come to this presentation. It is time to take a serious look at macro quoting. I have often said that anyone who thinks macro quoting is simple, probably doesn't understand the problem; so I have been there. Now I want to explain how simple it can be. Everything relevant to this paper is in BASE SAS. Although the examples have been executed on a PC under Windows, the examples are independent of any particular operating system. You might think this paper is a compendium of all the macro quoting functions. It is not. It is more about the subject of macro quoting and understanding than about the macro quoting functions. On the other hand, a minimum set of quoting functions is discussed. | macro facility, macro quoting function, macro variable, macro put, macro function, &variable, put statement, macro definition, macro call, macro trigger, filename statement, %if-%then-%else | source | 15 |
| 8 | A Taste of ADaM | Beilei Xu | The Analysis Data Model (ADaM) and the ADaM Implementation Guide (ADaMIG) published by the Clinical Data Interchange Standards Consortium (CDISC) provide the fundamental principles of analysis datasets and specifications for two standard data structures: the subject-level analysis dataset ADSL and the Basic Data Structure (BDS) which is a general structure that provides "one PROC away" readiness for many common analyses. This paper presents the detailed steps used in drug project work to create ADaM BDS datasets, illustrated by ADLP, a dataset to support analysis of lipids endpoints. Particular emphasis is given to the following implementation considerations: 1. number of ADaM datasets needed; 2. derivation of analysis endpoints, analysis windows, analysis values, and imputation of missing values; and 3. setup of analysis flags and population flags. Keywords: ADaM, CDISC, analysis dataset |
adam dataset, bds, change from baseline, proc step, flag variable, data science, missing value, cdisc | source | 9 |
| 9 | A View Toward Performance | Edward Heaton | Often we need to preprocess our data through a DATA STEP before we submit it to some SAS Procedure. When our data are very large -- billions of rows -- this processing of reading the entire dataset, making the modifications to each row, writing the entire dataset back out to disk, and then reading then entire dataset back into the PROC can take a very long time. The author proposes the use of a DATA view to process the data one row at a time and then to send that row directly to the PROC for processing. With very large datasets the time savings can be on the order of 60%. |
cpu time, dataset, proc step, proc freq, proc sql | source | 4 |
| 10 | Ad Hoc and Statistical Model Visualization Using JMP, SAS, and Excel | Jon T. Weisz | Generally speaking, models are abstractions of real-life systems used to facilitate understanding and to aid in decision making. However, models mean different things in different disciplines. Engineers, financial analysts and statisticians all employ modeling, as a core tool, but each discipline would define models differently. Even with the differences in how models are defined and developed, all disciplines need ways to communicate models, perform what-if analyses and simulations. This paper will highlight the use of the different model profilers in JMP to visualize and perform what-if analysis and Monte Carlo simulation for models defined using SAS/STAT, Microsoft EXCEL and engineering tools |
monte carlo, excel, proc nlin, sensitivity analysis | source | 10 |
| 11 | Analysis and Visual Review of Error Matrices in SAS Stat Studio | Robert Seffrin | The United States Department of Agriculture's National Agricultural Statistics Service uses ground reference data and satellite imagery to create an annual Cropland Data Layer (CDL) land cover classification product. The CDL and survey data are used to estimate crop acreage. A review of the quality of the CDL begins with a cross tabulation of the CDL against a validation data set to produce an error matrix, also referred to as a confusion matrix or contingency table. The power of SAS Stat Studio is employed to calculate the statistics using SAS/IML and IMLPlus and to generate insightful linked graphics and maps to review the quality of the land cover classification at the state and county level. |
data cleaning, reference data, validation set, proc freq | source | 10 |
| 12 | Application Dispatcher: Some Tweaks and Tricks | Carol A. Martell | In the process of converting a ColdFusion/MySQL dynamic website to one that uses SAS/INTRNET Application Dispatcher to query data, we developed a few workarounds to cope with miscellaneous obstacles. The paper presents techniques enriching the dictionary tables and tricks for passing and parsing multiple values from a single form field. This paper is for the intermediate to advanced SAS user having familiarity with Application Dispatcher |
cpu time, dictionary tables, proc step | source | 7 |
| 13 | At Your Service: Your Roadmap to Support from SAS | Kathryn A. Council | At YOUR service. How to make the most of the products and services from SAS Publishing, SAS Education, Technical Support, and support.sas.com. You have world class support from SAS at your fingertips. But where do you start? How do you navigate the sea of information available to you from SAS? How do you find the resources you need to do your job? This paper will provide you with practical tips, tricks, and techniques to find exactly what you need to use SAS. You'll save time, learn about upcoming content and, best of all, become a more proficient and expert user of SAS. This paper will benefit the new user and the seasoned SAS user alike. The intended audience is anyone interested in learning more about how to effectively use the services available from SAS. | sas base, sas programming | source | 6 |
| 14 | Automation of Data Updates: A Case Study | Carry W. Croghan | In December of 2009, the United States Environmental Protection Agency initiated a year long study to measure a suite of traffic-related air pollutants adjacent to I-15 in Las Vegas, Nevada. Measurements were collected simultaneously at four sites and stored at five minute intervals. The data were transmitted daily to a central computer using WinCollect Data Evaluation and Reporting Software (Version: 3.3). Once a week, the data were exported into an EXCEL (2003) spreadsheet which was then translated to a SAS dataset. To efficiently update the SAS database on a weekly basis several different SAS tools were utilized including PROC IMPORT, PROC UPDATE, %INCLUDE, and batch calls with parameter pass throughs. Each of these tools will be described with details including why they were utilized. By automating the process of updating the SAS database, the time to complete the weekly data extraction task and the probability of human error were greatly reduced. |
options statement, by group processing, proc step | source | 4 |
| 15 | Bars and Lines: A Quick Introduction To PROC GBARLINE | Garland D. Maddox Jr. | Ever had a need to create a vertical bar chart and then overlay it with one or more plots? PROC GBARLINE provides a convenient and elegant method of doing just that. This paper provides a brief introduction to PROC GBARLINE with an emphasis on using some of the enhancements introduced in SAS Version 9.2. This is a basic to intermediate presentation |
proc step, order by, proc gchart, bar chart, response variable | source | 6 |
| 16 | Building the Better Macro: Best Practices for the Design of Reliable, Effective Tools | Frank C. DiIorio | The SAS macro language has power and flexibility. When badly implemented, however, it demonstrates a chaos-inducing capacity unrivalled by other components of the SAS System. It can generate or supplement code for practically any type of SAS application, and is an essential part of the serious programmer's tool box. Collections of macro applications and utilities can prove invaluable to an organization wanting to routinize work flow and quickly react to new programming challenges. But the language's flexibility is also one of its implementation hazards. The syntax, while sometimes rather baroque, is reasonably straightforward and imposes relatively few spacing, documentation, and similar requirements on the programmer. In the absence of many rules imposed by the language, the result is often awkward and ineffective coding. Some amount of self- imposed structure must be used during the program design process, particularly when writing systems of interconnected applications. This paper presents a collection of macro design guidelines and coding best practices. It is written primarily for programmers who create systems of macro-based applications and utilities, but will also be useful to programmers just starting to become familiar with the language. | macro variable, macro facility, macro definition, macro loop, macro put, data cleaning, keyword parameter, %macro parameters, sas log, %if-%then-%else, error message, sysrc, automatic macro variable, symbol table, autocall macro, macro parameter, options statement, proc step, macro function, dictionary tables | source | 16 |
| 17 | Business Intelligence 2.0: Are we there yet? | Gregory S. Barnes Nelson | Business intelligence (BI) has been with us for years now, and, we would argue, not much has changed. Advances in the consumer communications and the entertainment world have dwarfed commercial software applications. Consumers have unprecedented access to information and tools with which to consume information. Social media, mobile access, augmented reality and 3D views of pictures and video have blurred the lines between our private and work personas and have fundamentally changed the way the consumer utilizes information. Conversely, BI has seemed to lack in any real innovation. In this paper, we will outline what the next chapter of innovation should contain for business intelligence, analytics and data integration. Some hints of these features exist today, while others have lagged behind as the industry has endured a number of mergers and acquisitions. The vendors that have survived the economic and industry turmoil can now turn from treading water to making waves. | business intelligence, data integration | source | 10 |
| 18 | By Your Command: Executing Windows DLLs from SAS Enterprise Guide | Darryl Putnam | Have you ever wanted to use system shell commands (copying and moving files, creating directories, etc) from SAS Enterprise Guide 4.1? SAS Enterprise Guide default installation turns off this functionality. You can re-enable the X command using -ALLOWXCMD, but do you really want just anyone to be able to issue commands like format C on your SAS server? I think not. Luckily you can run external DLLs from SAS under Windows by using the SASCBTBL Attribute table and MODULE family of call routines and functions. This paper will demonstrate how to setup the SASCBTBL table to call a select number of Windows DLLs. A brief description of MSDN library and how to turn the Windows function into SAS callable routines will be presented. | sas enterprise guide, txt | source | 7 |
| 19 | Customizing Proc Import Code | Carolyn D. Williams | PROC IMPORT is a great SAS tool that I use often when converting raw data to SAS. Usually SAS zips through the IMPORT procedure and the output is exactly what I expect. However occasionally there are glitches and 'gotcha' with numeric and character data types which can really make for a very bad day. The GUESSROWS= option value is valid for 1 to 32767 rows beyond that, the data type is pretty much set. What happens at observation number 82767 and SAS has defined a numeric storage location but the raw data has the value 302~40.33 or some other non numeric values for the next 200 records before it once again encounters valid numeric fields. This poster takes a serious and sometimes humorous walk down the yellow brick road to find the key to the import wizard's heart so that we might learn how to customize import statements and tame the bad (wicked) data |
proc import, proc step, macro variable, raw data | source | 3 |
| 20 | Detecting Medicaid Data Anomalies Using Data Mining Techniques | Shenjun Zhu, Qiling Shi, Aran Canes |
The purpose of this study is to use statistical and data mining techniques in Base SAS and SAS Enterprise MinerTM to proactively reduce the number of false positives caused by data anomalies in Medicaid pharmacy claim data when employing a rule-based approach to identify overpayments. Typically rule-based techniques are based on specific state Medicaid laws and policies using certain formulas to detect and identify over charged payments. False positives are defined as an identified overpayment that is erroneously positive when a claim was paid correctly due to data anomalies or unknown factors. False positives substantially increase the amount of time and resources spent by the auditors. The specific objective of the study is to detect and reduce data anomalies by examining the relationships among key variables such as Medicaid amount paid (MAP), average wholesale price (AWP) and quantity of service in Medicaid pharmacy claim data. Pharmacy claim data were simulated and the overpayment was calculated by a rule-based approach developed by AdvanceMed Corporation. Different data mining techniques such as the studentized residual, leverage, Cook's distance, DFFITS and clustering were utilized to capture the abnormal claims and reduce the number of false positives. The results of this analysis indicated that the clustering statistical method is the best approach to detect these kinds of data anomalies, followed by the DFFITS method. | type i error, outlier, regression analysis, insurance claims, cook's distance, data mining | source | 9 |
| 21 | Developing a Model for Person Estimation in Puerto Rico for the 2010 Census Coverage Measurement Program | Colt S. Viehdorfer | One of the goals of the 2010 Census Coverage Measurement (CCM) program is to estimate net coverage error for persons in housing units for the 2010 Census, with Puerto Rico results being calculated independently from the rest of the United States. General logistic regression is being used for the estimation of net error for the 2010 Census as opposed to using a post-stratification method, as was done in previous census coverage measurement surveys. Unlike post-stratification, logistic regression allows the use of continuous variables. This paper will outline the steps that I took to develop a logistic regression model for net coverage error estimation in Puerto Rico, using data from the 2000 Census and its coverage measurement survey. I will explain how I determine the main effects to be included in the model using various exploratory and statistical techniques and will also examine different model selection procedures for deciding on a final model, which will include main effects and interactions. The main effects that I have chosen to use in the model in this paper will be proposed to be used as the main effects for the model for the 2010 CCM. However, specific interaction terms to include in the model will be determined using the procedures outlined in this paper once the actual 2010 CCM data becomes available. | sample, logistic distribution, logistic regression, likelihood function, data cleaning, main effect, proc step, interval variable, regression analysis, proc surveylogistic, proc logistic, nominal variable, sample weight, feature selection | source | 12 |
| 22 | Distance Mapping in Health and Health Care: SAS as a Tool for Health Geomatics | Barbara B. Okerson | Geomatics is the science concerned with using mathematical methods with spatial data to learn about the earth's surface. Health geomatics is used to improve the understanding of relationships between people, location, time, and health. Geomaticists assist in discovering and eliminating disease, aid in public health disease prevention and health promotion initiatives, and support healthcare service planning and delivery through defining spatial relationships. Distance mapping is one of many tools in the geomatics toolbox that furthers this understanding. SAS provides a comprehensive set of graphics tools and procedures as part of the SAS/GRAPH product that can be used for distance mapping, including the new GEOCODE procedure and the GEODIST function, available with SAS 9.2. This paper combines the graphics available in SAS/GRAPH with other available SAS tools to explore spatial relationships in the health care arena. Examples include maps, grid squares, area charts, and X-Y plots. The analytic graphics in this paper were developed with version 9.1.3 or 9.2 of SAS executing on a Windows XP platform. SAS 9.2 is required for the GEOCODE procedure. The graphics represented in this paper are not platform-specific and can be adapted by both beginning and advanced SAS users. |
proc step, data mining | source | 12 |
| 23 | ES_ANOVA: A SAS Macro for Computing Point and Interval Estimates of Effect Sizes Associated with Analysis of Variance Models | Jeffrey D. Kromrey, Bethany A. Bell |
Measures of effect size are recommended to communicate information on the strength of relationships. Such information supplements the reject/fail-to-reject decision obtained in statistical hypothesis testing. Because sample effect sizes are subject to sampling error, as is any sample statistic, computing confidence intervals for these statistics is a useful strategy to represent the magnitude of uncertainty about the corresponding population effect sizes. This paper provides a SAS macro for computing common effect sizes associated with analysis of variance models. By utilizing data from PROC GLM ODS tables, the macro produces point and interval estimates of eta-squared, partial eta-squared, omega-squared, and partial omega-squared. This paper provides the macro programming language, as well as results from an executed example of the macro. |
effect size, sample, confidence interval, f distribution, anova, degrees of freedom, proc step, main effect, proc glm, predictor variable, null hypothesis, proc genmod, response variable, sas base, macro definition | source | 10 |
| 24 | Evolve from a Carpenter's Apprentice to a Master Woodworker: Creating a Plan for Your Reports and Avoiding Common Pitfalls in REPORT Procedure Coding | Allison McMahill Booth | Do you sometimes get into a routine of using the same techniques to create PROC REPORT code only to end up with reports that no longer suit your needs? You're sure that there's a better way to code in PROC REPORT, but you don't know where to start. If you're frustrated with the time that you spend creating reports that are less than ideal, consider taking the time to create a coding plan before you begin to code. Although PROC REPORT coding is flexible and can be used with various SAS applications, there are pitfalls. Just like an inexperienced carpenter's apprentice, you might code in familiar ways that you know will work, rather than in ways that work optimally with PROC REPORT. Making a coding plan and being aware of the pitfalls will enable you to code PROC REPORT easily, thereby taking you from an apprentice-level carpenter to a master-level woodworker. |
proc report, proc step, sample, sas log, length statement, if-then-else, character variable, sas output, proc format, class variable, label statement, automatic variable, output delivery system, format statement | source | 18 |
| 25 | ExcelXP on Steroids: Adding Custom Options To The ExcelXP Tagset | Mike Molter | The multitude of options available with ODS's ExcelXP tagset has allowed users access to dozens of EXCEL features when creating spreadsheets from SAS, but not all of them. ExcelXP is a SAS-made tool, but because it is a tagset, users have the ability to modify it. In this paper we'll discuss strategies for adding simple functionality to ExcelXP. Users of all levels will not only see the brief, intuitive tagset code used to produce the required XML for these specific examples, but will also realize the power over their output that this and other TAGSETS give them. Those with more experience with XML and tagset coding will learn a little more about the inner workings of ExcelXP as well as general strategies for adding any functionality to any tagset. |
excel, proc step, put statement, xls, macro parameter, proc template, macro loop | source | 15 |
| 26 | Fitting multivariate random-effects models using SAS PROC GLIMMIX | Lei Li, Namasivavayam Ambalavanan, Abhik Das |
Multinomial ordinal data arise when measures of an outcome are scaled into ordered categories. The ordinal data can be analyzed using multinomial logit models (Agresti 2002). But the analysis is often complicated by the clustering nature of the data. One approach to handling intra-cluster correlation is logistic regression using a random intercept. Another approach is using random-intercept multivariate logit model, in which multiple logits of the outcomes are assumed to share a common random intercept. The multivariate modeling approach can be further extended by incorporating a vector of Multivariate Normal random intercepts to allow more flexibility in the correlation structure among multivariate outcomes. In this paper we describe a continuation-ratio logit model with Multivariate Normal random intercepts and use SAS PROC GLIMMIX to fit the model to survival data among premature infants admitted into hospital neonatal intensive care units. |
random intercept, logistic regression, proc glimmix, covariance, response variable, proc step, correlation, logistic distribution, regression analysis, proc mixed, proc calis | source | 8 |
| 27 | Fun With Functions | Yogini Thakkar | How often do we sit back and take the time to improve our SAS programming skills by learning to use something better than what we have routinely used in the past? I invite you to join me as we come out of our comfort zone to examine what is new in SAS 9 and find out how we can use these new tools to improve our code. In this paper we will revisit some commonly used functions and see how they have been improved in SAS9, and find out how this will help us simplify our code while reducing the chance of errors. A few things that will be covered in this paper include examining how the FIND function can work better than the INDEX function and how the new COMPRESS function helps us minimize code. We will also look at the new ANY and NOT functions. | sas 9, compress function, missing value, character variable, character functions, index | source | 10 |
| 28 | Geocoding Crashes in Limbo | Carol A. Martell, Daniel Levitt |
In North Carolina, crash locations are documented only with the road names for the nearest intersection. Some projects require more accurate location for importing into ArcGis. We use a combination of SAS and GoogleEarth to more accurately pinpoint crash locations. This paper discusses how we streamline a manual process to make it feasible. | dataset, excel | source | 5 |
| 29 | Guide to ODS Graphics Editor in SAS 9.2 | Mirjana Stojanovic | This paper describes the new ODS graphics editor in SAS v. 9.2. In this presentation we talk about a review of ODS graphics. It will be provided and explained in more detail about new ODS graphics editor and the ODS graphics designer and how it differs from the ODS graphics editor. ODS Graphics Editor is a point-and-click editor to edit graphs that are produced by procedures that use ODS Statistical Graphics. What are pre-requisites for editing a graph? How to create an editable graph? How to customize graph properties to satisfy your needs? Paper explores some of the new features of the ODS graphics in SAS 9.2. |
proc step, output delivery system | source | 10 |
| 30 | How to Monitor "Don't Know" and "Refusal" Non-responses in a Large National Survey - Using Simple SAS Macros, a Few PROCs, and Data Steps | Mariah Mantsun Cheng, Timothy Monbureau |
Monitoring data quality is a critical and sometimes daunting task during any data collection effort. One simple way to assess the quality of typical interview data involves tracking the number of valid and missing responses to survey items. Such tracking may lead to the early detection of problematic questions, enabling researchers to redesign instruments before proceeding further with the potentially costly collection of flawed data. By recoding data into the 4 outcome categories of "Don't Know," Refusal," "Legitimate Skip," and "Valid Response," we use SAS Macros and the common SAS procedures of CONTENTS, TRANSPOSE, FREQ, APPEND, and SUMMARY in some uncommon ways to determine response rates for specific questions, larger questionnaire sections, and the survey as a whole. Our technique also facilitates the informative calculation of both relative response rates, which are based on the number of legitimate responses, and absolute response rates, which are based on the total number of respondents. We use the well-known Longitudinal Study of Adolescent Health (Add Health) Wave IV interview as an illustration here. | data cleaning, proc means, proc step, macro definition, proc freq, proc append, dataset, proc contents, macro variable | source | 10 |
| 31 | How to run an error check to stop SAS | Dan Blanchette | abort, sas 9 | source | 4 | |
| 32 | How To Use Proc SQL select into for List Processing | Ronald J. Fehd | The SAS macro language is simple, yet powerful. List Processing with PROC SQL is also simple, yet powerful. This Hands On Workshop paper provides programmers with knowledge to use the PROC SQL SELECT INTO clause with the various SQL dictionaries to replace macro arrays and %do loops. Expected audience is intermediate to advanced users, and macro programmers. |
proc sql, proc step, macro variable, create table, macro put, macro call, %if-%then-%else, character functions, macro definition, order by, proc print, group by, options statement, select, variable list, macro parameter | source | 40 |
| 33 | IF and %IF You Don't Understand | H. Ian Whitlock | Many beginning SAS macro programmers are confused about the difference between the DATA STEP IF and a %IF in a DATA STEP generated by a macro, and when to use which. Simple examples are used and discussed so that you may never be confused about this issue again. This presentation is appropriate to all operating systems and SAS products where code is written. |
if-then-else, %if-%then-%else, set statement, macro variable, subsetting if, macro loop, macro definition, macro facility | source | 6 |
| 34 | If You Can't Learn It From A Book, Why Are You Reading This? | Stephen M. Noga | People management, an inexact science if ever there was one, is often thrust upon individuals as they move up the corporate ladder. With promotion comes added responsibility whether a person is ready for it or not. How is a person supposed to learn to manage other people? Seminars, and online courses, and books, Oh My! The choices are numerous and chances are that knowledge will be obtained. However, how did you learn to deal with missing data where none was expected? You experienced the frustration after your program produced erroneous results and you learned how to prepare for this situation the next time. Management is no different. Welcome to my observations from twenty-one years of managing SAS programmers. | sas programmer, statistical programmer | source | 6 |
| 35 | If you Have Programming Standards, Please Raise Your Hand: An Everyman's Guide | Dianne Louise Rhodes | When a new project starts, or a new manager takes over an old project, the programming team is faced with a new culture. Often, this means that they are asked to adhere to yet another set of programming standards, use style sheets, and are subject to peer reviews. This process is usually short lived, because of the lack of a practical approach to applying standards and enforcement and failure to budget time and resources in the project schedule. This paper goes through a step by step process of developing programming standards, classifying them and entering them into a database. This database can then be used to develop style sheets and check lists for peer review and testing. Through peer reviews and in preparation for them, programmers learn good programming practices. We describe in detail the most common standards; why and how they should be applied. | proc step, macro variable | source | 8 |
| 36 | intro-to-sasgraph [Compatibility Mode] | Mike Kalt | Presentation: This session introduces two of the most commonly used SAS/GRAPH procedures - GCHART and GPLOT - and illustrates how they are used to produce bar charts, pie charts, scatter plots, and line plots. The session will also demonstrate how to export graphs produced by SAS/GRAPH to Web pages, Microsoft Office products, and PDF files. |
source | 24 | |
| 37 | It's Five O'Clock Somewhere!!! Handling Dates And Times In SAS | Toby Dunn, Sarah Woodruff |
Dates and Times - we use these concepts every day and they are strewn throughout our SAS code. So what about dates and times cause us problems? Why do they cause so many programmers, both new and seasoned, headaches? More importantly, do they have to be so hard to deal with? We are here to say "No, they don't." In this paper we will take you through the ins and outs of working with SAS date, DATETIME, and time values. Once armed with a range of concepts and functions you will come to see just how easy it is to work with date and time values in SAS, whether used in open or macro code. Once you have mastered these skills you too will be singing "It's Five O'Clock Somewhere" and looking to kick back with your favorite beverage. |
sysdate9, date variable, datepart, put statement, macro function, macro facility, attrib statement, format statement, proc step, datetime, time variable, year, input statement, proc format, macro put, decimal, macro variable, time, weekday, informat statement | source | 21 |
| 38 | LAG Function Combined with Conditional Functions - Useful in Identifying Differences in Like Data | Andrew Hummel | The LAG function is useful in identifying subtle differences in rows with similar data. This is especially valuable when the data set contains a large number of rows. Additionally, when conditional functions are used in conjunction with the LAG function specific limits can be used to flag only particular differences between rows. | lag function | source | 7 |
| 39 | Leave Your Bad Code Behind: 50 Ways to Make Your SAS Code Execute More Efficiently | William E. Benjamin Jr. | This laundry list of tips, gathered from over 25 years of SAS programming experience, will show 50 ways to help SAS programmers make their code run faster and more efficiently. General topics will include doing more than one thing in each DATA STEP, combining steps to make simple tasks take less code, using macro variables to simplify maintenance, using built in features rather than writing your own code, ways to save disk space, using sorts for more than just sorting data, and ways to make the program code just read better (code that is easier to read is easier to maintain). The list is broken into categories to allow readers to find something they can use faster. This list can work as a primer for making code changes that will improve the systems and processes to which it is applied. |
macro variable, data step, txt, out=, character variable, filename statement, dataset, %if-%then-%else | source | 20 |
| 40 | Macro Quoting - How and Why | H. Ian Whitlock | Macro quoting requires precision understanding. Sometimes it is correct to quote and sometimes it is wrong. To quote correctly you must know what needs to be hidden (or masked) from whom during what part of the process and how code going to the SAS compiler gets unquoted. Examples will be used to enable you to develop the intuition needed to know what to quote, how to do it, and how to remove the quoting when it is needed. A minimum number of quoting functions will be introduced along the way to accomplish these tasks. The student should have some experience writing SAS code and at least a minimum knowledge of how to generate it using the macro facility. In particular the instructions %MACRO, %IF, %DO, %LET, and %PUT should be recognized along with some simple functions such as %UPCASE and %SUBSTR or %SCAN. Although the student should know the purpose of these tools, s/he need not have a great deal of experience using these tools. The course is appropriate to any operating system and any SAS product which involves the need to understand and/or write code. |
macro call, macro function, macro quoting function | source | 2 |
| 41 | Making Sense of Census Data | Robert S. Matthews | The United States Census Bureau publishes a vast amount of data on many different facets of the U.S. population. One of the most utilized resources is the data available from the decennial census. This data is summarized and stratified in many different ways and can be used for a myriad of purposes. The task of actually extracting particular pieces of information from the census data can be daunting since there are literally hundreds of files containing thousands of individual variables. Not only is the data itself voluminous, but the documentation is also very extensive. An example of extracting three variables from the 2000 census data is presented in this paper to illustrate the tasks involved in reading, understanding, and using the data. | dataset, input statement | source | 7 |
| 42 | Merging into Hash: Some Practical Examples of Converting MERGE Statements into Hash Objects | Ying Liu | Merging data from two or more datasets is a common process as we manipulate our data for reporting and analysis. Prior to SAS version 9, the MERGE statement was the most common approach to accomplish this task for a DATA STEP programmer. Although MERGE is effective and robust, there are some potential downsides; the most common is the cost of sorting. To use a MERGE the datasets normally must go through multiple sorts. Each data set has to be sorted by the common variables before the merge step occurs; and the results dataset often has to be sorted in to its most common access order after the merge. The CPU time can significantly increased because of the sorting procedures; in addition, if the datasets are large there is a need for extended disk space to accommodate the sort. With the introduction of a memory search method - the SAS HASH object, merging with a HASH look-up table method substantially improves the data management process, not only increasing efficiency but also improving code transparency. This paper will illustrate some techniques used to convert programmes using a MERGE statement to programmes using a HASH Object in the match-merge table relationship. Both one to one and one to many relationships will be covered. |
hash table, merge statement, left join, inner join, sample, data step, unique key, proc compare, cpu time | source | 23 |
| 43 | Mobile Macros - Get Up to Speed Somewhere New Fast | Patricia Hettinger, Oakbrook Terrace |
Have you ever been faced with this scenario? It's your first day on the job and you know absolutely nothing about the data you're supposed to be working with. The documentation is scanty and people are too busy to give a detailed orientation - if they know enough to do so in the first place. This paper gives some macros the reader can use anywhere and explains some SAS concepts behind them. It also details how you can use SAS Enterprise Guide to create input screens for more flexibility. | where statement, dataset, proc step | source | 12 |
| 44 | ODS Layout for RTF - A custom tagset | Richard A. DeVenezia | ODS LAYOUT is a preproduction feature in SAS 9.0 through SAS 9.2. Preproduction means it is not officially supported nor fully developed. Regardless of its status, LAYOUT is a powerful tool for the PRINTER and PDF destination. It will let you "easily mix graphics, images, text, and tables, and arrange them on a page," producing eye popping print or print copy. Hold it, wait, backup... PRINTER and PDF destination only? What about that RTF report I wanted to create? This paper will demonstrate that the production ODS system has the ability to extend the standard RTF tagset to do what ODS LAYOUT does. Details of the Tagset and RTF languages will be discussed. |
proc step, where statement | source | 8 |
| 45 | Point-and-Click Programming Using SAS Enterprise Guide | Mira Shapiro, Kirk Paul Lafler |
SAS Enterprise Guide empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end-users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multi-platform enterprise data sources, the delivery of data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documentation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab-delimited and EXCEL input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and EXCEL; and visually manage projects using flowcharts and diagrams. |
excel, sas enterprise guide, dataset, data governance, sas output, proc sql | source | 13 |
| 46 | Potential Change in Reliability Measures Based on Decreased Sample Size for the Census Coverage Measurement Survey | Vincent Thomas Mule | As part of the evaluation of the 2010 Censuis, the U.S. Census Bureau conducts the Census Coverage Measurement (CCM) Survey. This survey produces the net coverage results of undercounts or overcounts of the Census. In addition to net coverage, our program has been asked to estimate the components of census coverage that include erroneous enumerations and omissions. A proposal was made to reduce our sample from the planned 300,000 housing units to help offset the costs of the operational enhancements to try to help reduce the nonsampling error in our estimates. This paper shows the results of a simulation study to assess the potential change in reliability measures for our proposed estimates. This paper will show results of the study and how SAS Procedures like PROC SURVEYSELECT and PROC SGPANEL were used. |
sample, cv, proc calis, sample size, logistic regression, regression analysis, cluster sampling, logistic distribution, correlation, confidence interval, proc step, proc surveyselect, sample weight, imputation, main effect | source | 12 |
| 47 | Proc Report Data = Subject.Event_Chronology; | Christina Carty, Elizabeth R. Spence |
Data management staff at the VA Cooperative Studies Program Coordinating Center located in Perry Point, Maryland was challenged with the task of generating a report to display in chronological order selected data points collected on 10 case report forms. The information was used to determine if MI or other cardiac events occurred for a given patient. Because the classification results play an essential role in the final analysis reporting for the study, it was very important that the data be conveyed accurately in a user-friendly fashion; not an easy task for a large number of variables and two thousand patients! Since the chronology lists were produced during the ongoing recruitment phase of the study, a mechanism was employed to screen each subject's data to assure all required records were received, and no queries were left unresolved. Required values were extracted from each form dataset and manipulated where necessary, then compiled into a master database containing multiple observations for each subject. Finally, a complete chronology report was created and sent to an outside lab for MI classification as auditing was performed simultaneously to track IDs throughout the process. This paper will demonstrate how Base SAS was used to filter and manipulate the data. We will then show how PROC REPORT and the Output Delivery System (ODS) were utilized to format and display the data in an easy-to-read MS Word document. |
randomization, proc report, proc step, adverse events, crf | source | 12 |
| 48 | PROC TABULATE: Doing More | Arthur L. Carpenter | Although PROC TABULATE has been a part of Base SAS since early version 6, 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. Once the foundation is laid a number of intermediate level techniques, options, and statements that provide the TABULATE programmer with a wide range of power and flexibility are presented. These techniques include those that are often underutilized even by the more experienced TABULATE programmer. |
proc step, proc tabulate, missing value, proc format, format statement | source | 18 |
| 49 | PROC_CODEBOOK, Automating the Review and Documentation of SAS Files | James Terry | If a SAS file has variable names and formats for categorical variables, highly recommended practices, the PROC_CODEBOOK macro can be called to produce a comprehensive, well formatted and easy to read codebook. In the heading the codebook will provide codebook title(s), file name, file label, and date created, number of observations and number of variables and file organization. The body of the codebook will provide the variable name, label, type, format and length, the mean, the range of values, frequency category, number and percent. Optional footnotes provide additional documentation. In addition, a codebook warning report is produced for categories that are not used, for variables with all missing data and variables that are constant. To produce a codebook, the user provides one or two titles for the codebook (title1 and title2), file organization (%LET organization = user defined) and calls the macro, it is that easy. |
proc step, nominal variable, proc contents, interval variable, proc freq | source | 3 |
| 50 | Producing Maps with SAS Enterprise Guide | Harmon L. Jolley | A variety of maps can be created using SAS/GRAPH programs. However, writing SAS programs to create these maps may not be in your repertoire, or you may just prefer to use your workstation's mouse whenever possible. This paper illustrates a point-and-click, process flow approach to producing maps using SAS Enterprise Guide |
dataset, sas enterprise guide | source | 7 |
| 51 | QC Your SAS and RDBMS Data Using Dictionary Tables | Harry Droogendyk | In the context of our daily occupations we are always examining data. Whether we're testing ETL processes that populate data marts, verifying data pulled for testing, or just becoming acquainted with unfamiliar data there are some rudimentary things we do typically do. Simple analysis of continuous variables such as min, max, mean etc... and frequency distributions of categorical variables are often used to provide quick insight. This paper presents a macro that does the QC work for you, driving the process from dictionary tables, whether the data is from SAS datasets or sourced from any of the DB systems accessible by SAS. | frequency analysis, proc step, proc contents, proc freq, dictionary tables, dataset, libname statement, proc sql, by group processing, numeric variable, macro parameter, macro variable | source | 20 |
| 52 | Recoding "ALL THAT APPLY" Variables from Handhelds and Portable Computers | Wafa Handley, Barbara Bibb, Lilia Filippenko, Jay Levinsohn, Donna Medeiros |
How do you turn "ALL THAT APPLY" (ATA) questions into useful data structures? With the advent of Computer Assisted Interviewing (CAI) as a primary venue for survey data collection, most commercially available CAI software provides developers with tools to interface with the software. The tools enable developers to access the CAI software database to create data structure documents with variable names and attributes to facilitate data processing. With the introduction of handhelds as a mode of data collection, the range of methods and tools to collect data is quite broad. As yet there is no dominant mobile tool such as the Blaise questionnaire development software, so converting handheld database structures or handheld storage conventions for certain types of data for SAS typically requires new programming. Prior to the use of handhelds for data collection, application programmers at RTI International adopted three approaches to facilitate data analysis for the ATA survey items. The approaches utilize Blaise as the questionnaire development application used in the CAI software, and the SAS system software to produce the final data files. ATA responses from the handhelds were stored as one variable per question compared to individual response storage options available with larger capacity portable devices. The single variables were then "deconstructed" using SAS Decimal to Binary conversion functions to create analysis variables representing chosen responses. Our presentation will: (1) demonstrate the need for recoding "All That Apply Variables", (2) describe the different approaches taken in recoding, and (3) review the different approaches for raison d'être. | dataset, data science | source | 7 |
| 53 | Sample Size Calculation to Evaluate Mediation Analysis | Rajendra Kadel | Mediation analysis is used to review the comparative change in the amount of strength of association of the primary predictor with the outcome after adjustment for the mediator. Mediation models are very widely used in social sciences and biomedical sciences. Before conducting mediation studies, researchers want to know the sample size (i.e. number of subjects) required for achieving the adequate power when testing for mediation. To the author's knowledge, there is no any SAS procedure that produces sample size calculation for mediation analysis. The author presents a macro that implements the methodology for sample size calculation for mediation analysis written by Vittinghoff et al. (2009). It implements the methods to calculate sample sizes for the linear regression models. Very basic understanding of SAS is enough to use this macro. MACRO programming skill is not expected. When the macro is invoked, a series of windows will pop-up asking user to input required information. Output will be presented in SAS output window as well as in Microsoft word document. SAS products involved for this sample size calculation are Base SAS and SAS MACRO. This macro is tested on Windows SAS9.1 and above. Keyword: SAS MACRO, Mediation Analysis, linear regression models, sample size | sample size, sample, proc power and sample size, mediation analysis, treatment effect, proc reg, correlation, regression analysis, type i error, macro definition | source | 12 |
| 54 | SAS Abbreviations Are Your Friends; Use a Template Method to Code! | Elizabeth Ceranowski | Often, coders find themselves using the same procedures or sequence of procedures over and over again. It would be very useful to create a "shell" of a procedure or program that can be used to cut down on repetitive typing. (For example, to submit the generic code "PROC PRINT; run;"). This quick demonstration will show how to create/edit/use SAS abbreviations to quickly interject code into the Enhanced Editor window in SAS for Windows or in the code node in SAS Enterprise Guide. This technique can be easily used by any level of SAS user from beginning to expert! |
sas enterprise guide, proc report, tranwrd function, proc step | source | 11 |
| 55 | SAS and Relational Databases: What You Must Know | Patricia Hettinger | It is a rare shop that has data in just SAS format. DB2, Teradata, Oracle, SAP - all of these relational databases have their own idiocyrancies that may not become apparent until your DBA calls. Just because you can assign a LIBNAME to a database and use PROC SQL doesn't mean it will run the same way as it would against a SAS source. This paper will address what you must know before using SAS/ACCESS for more efficient queries and consistent results. |
proc sql, key, dataset, foreign key, lookup, data integrity, proc step, relational database, sas enterprise guide, data step | source | 12 |
| 56 | SAS Formats: Effective and Efficient | Harry Droogendyk | SAS formats, whether they be the vanilla variety supplied with the SAS system, or fancy ones you create yourself, will increase your coding and program efficiency. (In)Formats can be used effectively for data conversion, data presentation and data summarization, resulting in efficient, data-driven code that's less work to maintain. Creation and use of user-defined formats, including picture formats, are also included in this paper. | user-defined format, proc print, date variable, sysdate9, data step, dataset, proc step, format statement, input statement, proc format, put statement, e., numeric variable, excel, proc sql | source | 16 |
| 57 | SAS Macros for Estimating the Attributable Benefit of an Optimal Treatment Regime | Jason Brinkley | For many diseases there are several treatment options and sometimes there is no consensus on the best treatment to give to individual patients. What works well for some patients may be a very bad option for others. One may be interested in developing an algorithm that assigns treatments in such a way that each individual patient receives the one that is best for them. We define the optimal treatment regime as the strategy that most greatly reduces the number of poor outcomes. Using large observational databases, many can already form statistical models that determine the effectiveness of such treatment strategies. But it would be useful to have a single measure that indicates the effectiveness of imposing such a strategy. One such measure is the attributable benefit of a treatment regime, which uses notions from causal inference to estimate the proportion of poor outcomes that could have been prevented had a particular strategy been implemented. This talk will present two easy to use SAS macros for estimating the attributable benefit of the optimal treatment regime and it's variance from a logistic regression model-based treatment regime. The first macro uses a simple disease/treatment model that is supplied by the user. The second macro provides a doubly-robust estimate of attributable benefit that incorporates the simple disease/treatment model with a propensity model toward treatment. While the theory behind estimating these measures is complex, users with an understanding of PROC LOGISTIC in SAS can implement and interpret the output of these macros. |
regression analysis, confidence interval, proc calis, estimate, proc logistic, sample, logistic distribution, potential outcomes, proc step, propensity score, macro definition, robust estimator, interaction effect, logistic regression | source | 12 |
| 58 | SAS Maps as Tools to Display and Clarify Healthcare Outcomes | Barbara B. Okerson | Changes in healthcare and other industries often have a spatial component. Maps can be used to convey this type of information to the user more quickly than tabular reports and other non-graphical formats. SAS, SAS/GRAPH and ODS graphics provide SAS programmers with the tools to not only create professional and colorful maps, but also the ability to display spatial data in a meaningful manner that aids in the understanding of changes that have transpired. This paper illustrates the creation of a number of different maps for displaying change over time with examples from the healthcare arena. Examples include choropleth, bubble, and distance maps and introduce the new GEOCODE procedures. Results included in this paper were created with version 9.1.3 of SAS on a Windows XP platform and use Base SAS, SAS/STAT and SAS/GRAPH. SAS Version 9.1 or later is required for ODS graphics extensions. SAS Version 9.2 is required for the DATAIMPORT and GEOCODE procedures. The techniques represented in this paper are not platform-specific and can be adapted by both beginning and advanced SAS users. |
proc step, proc rank | source | 9 |
| 59 | SAS Proc Report and ODS ExcelXP Tagsets to Produce Customized Excel Output Without DDE | Mira Shapiro | After completing what appeared to be a routine request for a data-driven report with PROC TABULATE, one of my tools of choice, the additional requirement of an asterisk in selected cells in the requested EXCEL spreadsheet became an opportunity to explore some other techniques and add some new skills to my SAS repertoire. While it would have been possible to take the PROC TABULATE output and use DDE to create the desired spreadsheet, I decided to use PROC REPORT and ODS ExcelXP TAGSETS. The solution to producing this report required the use of many features of PROC REPORT including across variables, nested columns, call define, and style. The most interesting challenge was that the requester wanted the results delivered via an EXCEL spreadsheet with an asterisk in some of the columns based on two variables that were not being displayed in the report. The solution evolved into a short SAS program with a PROC REPORT step doing all of the data summarization and the majority of the formatting. This paper will briefly outline the DDE approach that might have been taken and will detail the solution that makes use of PROC REPORT and ODS ExcelXP TAGSETS. This presentation is for SAS Users who want to use some of the advanced features of PROC REPORT and gain an understanding of how to exploit PROC REPORT's underlying column structure. In addition, in a client server SAS implementation where DDE is not feasible, this approach gives an alternative for producing customized EXCEL output. |
proc report, proc step, excel, proc tabulate, proc sql, ods excel, macro variable | source | 10 |
| 60 | SAS Programmer's Paradise: New Goodies in SAS Enterprise Guide 4.3 | Audimar Bangi, Chris Hemedinger, Stephen Slocum |
Are you looking for a SAS development environment that actually helps you write and manage SAS programs? It's here! This paper describes the many new productivity enhancements for programmers in SAS Enterprise Guide 4.3. There is a new program editor with syntax completion for hundreds of SAS procedures and statements, support for parentheses matching, built-in function help and disambiguation, program "tidy" functions, and more. There is also a SAS code analyzer that can read your program and turn it into a process flow, or modify it to optimize the program steps to run in parallel on a grid computing environment. The SAS Enterprise Guide project file now supports relative paths, making it easier to manage your projects, programs, and data in standard source management systems. And, it has never been easier to turn your SAS program into a store process so that you can share your expertise with the world. | sas enterprise guide, sas programmer, sas enterprise guide project, sas log, macro definition, macro function, macro variable | source | 11 |
| 61 | SAS tips for Institutional Researchers to Track Student Outcomes Efficiently | Vijayalakshmi Sampath | Analysts and programmers in the Institutional Research (IR) field at Higher Education Institutes often work on research studies where a cohort of students must to be tracked across semesters (Longitudinal studies) in order to summarize their outcomes such as retention, course success rate, and graduation. Some studies also involve tracking outcomes and summarizing results for different types of student cohorts for a given semester (Cross Sectional studies). Since student information in College databases is constantly changing, researchers use static versions of the data for all official reporting. The static IR files in many colleges are generated after the Census date for every semester, resulting in multiple data files. As a result, tracking students across multiple files or tracking multiple cohorts becomes challenging and tedious for multi-year studies. This paper presents innovative SAS programming logic to track and output results of various student outcomes under the multiple semester and the multiple cohort scenarios. It utilizes Macro programming and Arrays within and outside of the DATA STEP in a robust manner to achieve this. |
proc freq, proc step, where statement, dataset | source | 7 |
| 62 | SAS-L and Beyond | F. Joseph Kelley | In the beginning was SAS-L (http://listserv.uga.edu/archives/sas-l.html ). And it was good, but did not reach far enough. So a Usenet newsgroup was added: comp.soft-sys.sas (http://groups.google.com/group/comp.soft-sys.SAS/TOPICS?hl=en&lnk ). It, too, was good. But more was needed. Now there is a Wiki: sasCommunity.org(http://www.sascommunity.org/wiki/Main_Page ), and the discussion forums at support.sas.com (http://support.sas.com/forums/index.jspa). There is also Lex Jansen's site (http://lexjansen.com/) and many more. These will give you a good place to start and offer support, code examples, best practices, information, gossip, jokes and arguments. This presentation will go over these and perhaps provide some information on future developments. |
excel, sas enterprise guide | source | 14 |
| 63 | SAS/Data Integration Studio - Creating and Using a Generated Transformation | Jeff Dyson, Financial Risk |
SAS/DATA Integration Studio (DI Studio) transformations are packaged tasks that give data architects the tools needed to construct ETL job flows. While DI Studio has built in transforms that satisfy the majority of an architect's needs, there are occasions when the user must create a custom routine to meet a non-standard requirement. The DI Studio generated transformation is a perfect solution to fill this void when these non-standard requirements are repeatable. |
sas data integration studio, macro variable | source | 10 |
| 64 | SAS/GRAPH Elements You Should Know - Even If You Don't Use SAS/GRAPH | Arthur L. Carpenter | We no longer live or work in a line printer - green bar paper environment. Indeed many of today's programmers do not even know what a line printer is or what green bar paper looks like. Our work environment expects reports which utilize various fonts, with control over point size and color, and the inclusion of graphic elements. In general we are expected to produce output that not only conveys the necessary information, but also looks attractive. In the line printer days little could be done with the style and appearance of reports, and consequently little was expected. Now a great deal of control is possible, and we are expected to take advantage of the tools available to us. We can no longer sit back and present a plain vanilla report. The Output Delivery System gives us a great deal of the kind of control that we must have in order to produce the kinds of reports and tables that are expected of us. Although we will often include graphical elements in our tables, it turns out that a number of options, statements, and techniques that are associated with SAS/GRAPH can be utilized to our benefit even when we are NOT creating graphs. Learn how to take advantage of these graphical elements, even when you are not using SAS/GRAPH. |
title statement, footnote statement, proc step, box plot, regression analysis | source | 15 |
| 65 | Set, Match, Merge ... Don't You Love SAS | Peter W. Eberhardt, Ying Liu |
One table has accounts, another has new transactions. One table has patient data, another has treatments. No matter what your business or application it is rare that a single data set or table has everything you need to answer the questions you are expected to answer. SAS has a simple and powerful mechanism to bring your tables together: the DATA STEP Merge. But this simple operation can easily run awry. In this paper we will explain the basics of the DATA STEP merge and the issues and problems you will encounter if you do not understand its workings. This paper is geared towards beginning SAS programmers, but it can be a useful refresher for anyone |
in=, out=, missing value, proc step, proc sql, proc print, set statement, order by, data merging | source | 19 |
| 66 | Solving Kenken Puzzles - By Not Playing | John R. Gerlach | Solving Kenken puzzles requires more than making sure that numbers are used only once in a row and column of a matrix. Unlike Sudoku puzzles that can use any symbol and have sub-matrices, Kenken puzzles require actual integers and have contiguous cells, called cages. And, unlike a sub-matrix that contains a unique collection of numbers or symbols, Kenken puzzles have cages that must contain natural numbers representing a total as a function of its assigned arithmetic operation. For example, consider a 4x4 Kenken puzzle having a cage containing 3 cells whose total is 11 as a function of simple addition. One possible set of 3 numbers would be: 4+3+4=11. The objective is to complete the grid using numbers ranging from 1 to N that satisfies both cage arithmetic and row / column uniqueness. Depending on the size of the NxN grid, the number (and size) of the cages, as well as the arithmetic operations used, a Kenken puzzle offers a formidable challenge for logic puzzle fans. However, rather than play the game of considering numerous possible sets ranging from two integers, for subtraction and division, to N-digits, for addition and multiplication, this paper proposes a SAS solution that obtains the viable sets for each cage straight-away and solves the puzzle by identifying the only appropriate collection of cage-specific sets. | do loop, macro variable, macro loop, dataset, missing value, proc sql, macro definition | source | 10 |
| 67 | Stationarity Testing in High Frequency Seasonal Time Series | David A. Dickey | Time series quite often show patterns that repeat periodically. Monthly retail sales provide a good example. If the seasonality is very regular, seasonal dummy variables can be used to give, for example, a monthly effect for each month. With this approach, the January effect is assumed to be the same regardless of the year. Seasonal ARMA error terms can be added to make some local modifications. An alternate model that is useful when the seasonality changes over the years is the seasonal unit root model. Motivated by Box and Jenkins' approach to modeling international airline ticket sales, this method takes a span d difference for seasonality d, e.g. d=12 for monthly data, and analyzes these seasonal span differences. Using the backshift operator B, the polynomial (1-Bd) represents the span d difference. Tables of percentiles for testing that the polynomial has unit roots (as does 1-Bd) are available (Dickey, Hasza, Fuller, 1984, henceforth "DHF") for seasonal periods d=2, 4, and 12. As with ordinary (d=1) unit root tests, these are nonstandard distributions that shift when typical deterministic inputs like seasonal means are included in the model. It is possible that a user may want to test for unit roots at a longer lag, for example one might suspect periodicity 24 or 7x24=168 in hourly data and hence might ask if unit roots at those lags give an appropriate model. This paper deals with large d results for unit root tests. Some features emerge that are nicer than those of the shorter period cases. This paper is a slight modification of a paper (Dickey, 2008) delivered at the 2008 Joint Statistics Meetings. A followup paper with more mathematical detail and somewhat improved but more complex adjustments is to appear in the Journal of the Korean Statistical Society in 2010. | unit root, null hypothesis, regression analysis, normal distribution, sample, time series analysis | source | 10 |
| 68 | Statistical Analysis - The First Steps | Jennifer L. Waller | For both statisticians and non-statisticians, knowing what data look like before more rigorous analyses is key to understanding what analyses can and should be performed. After all data has been cleaned up and statistical analysis begins, it is a good idea to perform some descriptive and basic inferential statistical tests. How to run and look at descriptive statistics using PROC FREQ, PROC MEANS, and PROC UNIVARIATE, and how to plot your data using some of the statistical graphics options in SAS 9.2 will be presented. Additionally, how to perform and interpret one- and two sample t-tests using PROC TTEST and chi-square tests using PROC FREQ will be presented. |
proc step, proc means, nominal variable, interval variable, sample, box plot, chi-square test, standard deviation, proc freq, data science, proc ttest, proc univariate, ordinal variable, variable list, inferential statistics, heteroscedasticity, confidence interval, proc sgplot, histogram, homoscedasticity, missing value, one-sample t-test, class variable, frequency analysis, sample size, outlier | source | 20 |
| 69 | Take a Whirlwind Tour Around SAS 9.2 | Diane Hatcher | A new bevy of SAS 9.2 capabilities are available in the platform for Business Analytics. Come and join us for a whirlwind tour of what's new across the suite of client applications for Business Intelligence and Data Integration. We'll start the trip with SAS Management Console which highlights updates to the metadata architecture that will surface throughout the rest of the tour. Other featured stops include Information Map Studio, Enterprise Guide, the Add-in for Microsoft Office, Web Report Studio, and BI Dashboard in the Information Delivery Portal. We'll even take a jaunt over to Data Integration Studio to see how user productivity can be taken even higher. Finally, we'll wrap up the tour with some integration with Microsoft Sharepoint. Experience the excitement of the new capabilities and features that integrate intelligence across your organization. | sas enterprise guide, sas management console, line chart, data mining | source | 8 |
| 70 | Take Control: Understanding and Controlling Your Do-Loops | Sarah Woodruff | The term "loop" describes any control structure that causes a set of programming logic to be executed iteratively. Not knowing when and how to use these structures properly cause many to produce SAS code that is at best an illegible jumble and at worst a virtually useless quagmire. This paper performs an in-depth examination of the underlying concepts involved in DO-loop construct theory, including basic constructs such as DO While and DO Until loops as well as advanced constructs such as the DO-loop of Whitlock (DoW). In addition, the paper describes sound yet simple methods to help determine which loop is needed and rules for how to easily create them. "Using loops is one of the most complex aspects of programming; knowing how and when to use each kind of a loop is a decisive factor in constructing high-quality software" - Steve McConnell (Code Complete, 2nd Edition). | conditional do loop, macro loop, by group processing, if-then-else, put statement, set statement, continue, macro facility, macro variable, goto, return, output, where statement, macro put, sas programmer, macro definition | source | 21 |
| 71 | The Data Step; Your Key To Successful Data Processing In SAS | Donald E. Kros | The DATA STEP is one of the basic yet more powerful of the building blocks in SAS programming. Its power allows users to create the data sets that are used in a SAS program's analysis and reporting procedures. Understanding the basic structure, functioning, and components of the DATA STEP is fundamental to learning how to create your own SAS data sets. In this paper, we will discuss what a SAS DATA STEP is, why it is needed, how the DATA STEP works, and what information you can supply in your SAS DATA STEP to assist with getting the most out of a single data pass. |
dataset, raw data, pdv | source | 13 |
| 72 | The DOW-Loop Unrolled | Paul M. Dorfman | The DoW-loop is a nested repetitive DATA STEP programming structure. It is intentionally organized to isolate DO-loop instructions related to a certain break-event from actions performed before and after the loop in a logically natural manner, i.e. in full rhythm with the DATA STEP implied-loop automatics. Readily recognizable in its most well-known form by the DO UNTIL (LAST.ID) construct, which naturally lends itself to control-break processing of key-grouped data, the DOW-loop is much more generic and morphologically diverse. In this paper, we examine the internal logic of the DOW-loop, use the power of example and the DATA STEP debugger to reveal its aesthetic beauty and pragmatic utility. Flagging each observation in a group based on conditions within the group in a single DATA STEP, natively aggregating cumulative totals, dynamically splitting a file in a number of files by ID via a hash - the DOW-loop lends itself to all these and other tasks as an ideal logical vehicle by radically simplifying the alignment of stream-of-consciousness and SAS DATA STEP code. |
set statement, missing value, hash table, output, by group processing, dataset, automatic variable, proc sql | source | 15 |
| 73 | The MEANS/SUMMARY Procedure: Getting Started | Arthur L. Carpenter | The MEANS/SUMMARY procedure is a workhorse for most data analysts. It is used to create tables of summary statistics as well as complex summary data sets. The user has a great many options which can be used to customize what the procedure is to produce. Unfortunately most analysts rely on only a few of the simpler basic ways of setting up the PROC STEP, never realizing that a number of less commonly used options and statements exist that can greatly simplify the procedure code, the analysis steps, and the resulting output. This tutorial begins with the basic statements of the MEANS/SUMMARY procedure and follows up with introductions to a number of important and useful options and statements that can provide the analyst with much needed tools. With this practical knowledge, you can greatly enhance the usability of the procedure and then you too will be doing more with MEANS/SUMMARY. |
proc step, proc means, output, class variable, standard deviation | source | 10 |
| 74 | The Next Generation: SAS/STAT 9.22 | Maura E. Stokes, Bob Rodriguez, Robert A. Cohen |
Caught up with SAS/STAT 9.2? Want to add even more statistical tools to your arsenal? Then get ready for the 9.22 release of SAS/STAT software, which works with Base SAS 9.2 to deliver methodological advances in addition to customer-requested features. New functionality for postprocessing equips the modern linear modeling procedures with comparable capabilities, including the latest techniques for testing complex research hypotheses. The new SURVEYPHREG procedure fits Cox regression models to survey data. The EFFECTPLOT statement uses ODS Graphics to create plots of effects from models produced by the GENMOD, LOGISTIC, and ORTHOREG procedures. Exact Poisson regression is one of several new exact methods for categorical data analysis. Other new features include updated spatial analysis capabilities, classification variable support in the ROBUSTREG procedure, and model averaging in the GLMSELECT procedure. This talk provides an overview of these exciting new enhancements to the statistical software. |
regression analysis, likelihood function, maximum likelihood estimation, proc step, estimate, proc genmod, poisson regression, logistic distribution, poisson distribution, proc glmselect, binomial distribution, negative binomial regression, proc glm | source | 20 |
| 75 | The Path, The Whole Path, And Nothing But the Path, So Help Me Windows | Arthur L. Carpenter | Have you ever needed to determine a directory path dynamically? Have you needed to know the name of the currently executing program and where it resides? There are a number of tools available to the SAS programmer that can be used to determine path and location information. These tools range from common to obscure and from simple to complex and they utilize SAS Language functions, System Options, SASHELP views, DICTIONARY tables, and OS Environmental Variables. This presentation will take a survey of a number of these techniques for finding and determining path and location information. This information can be important for all levels of programmers, and you should be aware of these techniques even if you think that you will never need them. | proc sql, dictionary tables, macro variable, macro definition, macro put, options statement | source | 8 |
| 76 | The SAS User Group Community Activity | Donald E. Kros | Internal SAS User groups are an effective, inexpensive way to receive practical information and technical content by meeting with peers who share the goal of getting more out of the SAS platforms, products, technologies, and resources available. Our User Group has grown from 5 to 400 active users in 3 years. Typically a user group is run by volunteers, independent of enterprise management, who meet on a regular basis to discuss and share information on a variety of technical topics and user topics. How we did it and what can you take away from our experience? | source | 9 | |
| 77 | The Systems Development Life Cycle (SDLC) as a Standard: Beyond the Documentation | Dianne Louise Rhodes | Has your company adopted the Systems Development Life Cycle (SDLC) as a standard for benchmarking progress on a project? Have they developed Word and other templates for documents created during SDLC? In three of my most recent positions, the stress was put on completing the documents according to schedule, rather than emphasizing the work. The work involved cataloguing requirements, analyzing them and developing a good design document, and thoroughly testing the resulting code. When I first started programming in SAS, I was lucky to get any users requirements at all; it was always "I'll know it when I see it." But with the emphasis on the documentation, and not on the analytical work behind them, the project still falls behind schedule because of missed requirements. If the requirements are not thoroughly complete when coding begins, it is likely to fail in the testing phase, especially if the independent test team gets a better, more complete version of the requirements than the development team. We discuss the work that is involved in detail for producing sound requirements, design, and testing protocols. Consider the retirement of legacy software as part of the SDLC. Some useful templates (in EXCEL, perhaps) to help non-programmers specify the reports they want. |
excel, data cleaning | source | 5 |
| 78 | Tips and Tricks Of Efficient SAS Programming for SDTM Data | Eric Qi, Fikret Karahoda |
Dataset sizes increased dramatically after the pharmaceutical industry adopted the SDTM data model. For example, SUPPQUAL, CF, and LB domains can easily reach several gigabytes (GB) in size. The need to process this data efficiently becomes more important even when using today's high-speed computer resources. In this paper, we will discuss a case to show how efficient programming plays an important role in handling large SAS datasets. | cpu time, where statement, cdisc | source | 6 |
| 79 | Traffic Lighting Your Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS | Vincent DelGobbo | "Traffic lighting" is the process of applying visual formatting to data. This paper explains how to use Base SAS9 software to create multi-sheet Microsoft EXCEL workbooks (for EXCEL versions 2002 and later), and then traffic light the values that lie outside a range. You will learn step-by-step techniques for quickly and easily creating attractive, multi-sheet EXCEL workbooks that contain your SAS output using the ExcelXP ODS tagset. The techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is also discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented. |
excel, xls, date variable, proc report, output delivery system, sas output, proc template, proc step, sas base, sysdate9, proc print, dataset, by group processing, format statement, ods xml, sample, clinical trial | source | 28 |
| 80 | Two Guys On Hash | Paul M. Dorfman | The SAS hash object is no longer new, yet its use is still not widespread. Where it is used, often the strengths and capabilities of the Hash object are underutilized. In this workshop we will quickly step through some introductory steps to ensure everyone is 'on the same page'; however, it does assume workshop attendee have some knowledge of the hash object - if not from practical experience, at least from attendance at an introductory workshop. Once we lay some introductory groundwork we will work through some more interesting and challenging examples of the Hash object in action. Take a deep breath (but don't inhale) as we start our journey into the world of Hash. | hash table, dataset, hash iterator, sas programmer, proc sql, sample, group by, sysrc, variable, proc step, memory usage, data merging, proc means | source | 33 |
| 81 | Using Dictionary Tables to Explore SAS Datasets | Phillip Julian | This paper presents a SAS dataset profiling tool that uses only the Base SAS language. The tool was inspired by my need to know much more about my data, without having data analysis tools like SAS Data Integration, Dataflux, or SAS JMP. The tool uses information from the SAS dictionary tables to answer questions about data structure, contents, classes, and attributes. The SAS program will find and analyze all SAS datasets at any level in a directory tree. Once SAS datasets are located, they are analyzed by a generic profiler program that summarizes all character values and computes a set of statistics for all the numberic variables, producing a "fingerprint" of the data. | dataset, dictionary tables, proc step, proc sql, excel | source | 21 |
| 82 | Using DICTIONARY Views to Eliminate Tedious Visual Review | Christine Davies | The SASHELP Dictionary views are a powerful tool that may be overlooked at times. As an example of their utility, they were used to overcome the challenge of inconsistent naming of datasets across years for a project. This is illustrated in the example code where the dictionary views in the SASHELP library were used to populate macro variables with dataset names, which were then used in conjunction with the FILEEXIST function to perform conditional processing on the dataset based on date last modified. | sample, proc sql, proc step, sample size, create table, macro variable, macro loop, dictionary tables | source | 7 |
| 83 | Using Linux Shell Commands, vi Editor, and Base SAS to Parse through Log Files and Gather Log-information | Fuad J. Foty | Reading log files can be fun but not when one has to examine several dozens or sometimes several thousands of those files and calculate real time and CPU time to see if the process is running according to plan. There are many ways to do that, the manual way or some smart automated way. By using the Linux shell commands such as tail, grep and cut, the vi editor, and base SAS, one can parse through log files and gather log-information such as cpu time, real time, and other important information that one needs to make decisions. | sas log, proc step, cpu time, macro parameter, %macro parameters, proc print, txt, data step, if-then-else | source | 17 |
| 84 | Using SAS PROC CLUSTER to Determine University Benchmarking Peers | Elayne Reiss, Sandra J. Archer, Robert Armacost, Ying Sun |
This paper will explore the steps taken by a large public research institution to develop a list of peer institutions for consistent use in future benchmarking studies. At the heart of this process is the use of two tools available in SAS/STAT - PROC CLUSTER and PROC FASTCLUS. Both of these procedures address clustering in differing ways, but this paper will demonstrate how we utilized the strengths of both procedures to strengthen our analysis. In describing the analytical process, we will focus on data selection and preparation concerns, the specifics of using these particular SAS procedures for our benchmarking application, and the ways this application of clustering fits into benchmarking activities as a whole. |
proc step, proc cluster, proc fastclus, cluster analysis, missing value, proc candisc, proc varclus, proc sgplot, sample | source | 10 |
| 85 | Using SAS Text Miner 4.1 to create a term list for patients with PTSD within the VA | Matthew R. Richardson | SAS Text Miner 4.1 was utilized to perform statistical text mining to supplement efforts to develop a clinical vocabulary for post-traumatic stress disorder (PTSD) in the VA. 405 veterans with PTSD were identified through administrative data within the Tampa VA and then combined with a comparison group of 392 veterans with no known PTSD symptoms or diagnosis. The patient notes from this cohort were captured to produce the dataset analyzed. Using all possible combinations of Frequency Weight and Term Weight 24 different models were run. 21 models produced usable results. In each model terms were ranked and scored based on global weights and then ranked and scored again based on the absolute value of the coefficient in a regression analysis utilizing stepwise regression. Scores for each model and each analysis were combined to conceive a master score with the highest scored term receiving the highest rank. 449 terms were identified within the regression analysis and 343 were identified in the global weight analysis for a total of 585 distinct terms. Future studies will focus on incorporating processes that identify terms that carry greater clinical relevance as well as incorporation of the improved techniques behind SAS Text Miner 4.2. | regression analysis, stepwise selection | source | 9 |
| 86 | Using SAS to Examine Missing Data in Psychometric Research | Abbas S. Tavakoli, Elizabeth Register |
Background: This study examined the effect of missing data in psychometric research. Missing data present a challenge to researchers. Incomplete data is common in most researches. There are many reasons for no response such as poor research designs, poor structured questions, and attrition in longitudinal studies. Method: A convenience sample of 428 community dwelling older adults participated in this study. Scale items were generated through a grounded theory study that was conducted to explore connectedness in older adults. The Register - Connectedness Scale for Older Adults is a 72-likert item self-administered paper and pencil survey. Single and multiple imputations were used to replace the missing data for 72 items. Means of each item were compared with and without imputation. Three factor analyses were run to develop Register-Connectedness Scales for older adults. These run included no imputation, single imputation, and multiple imputation for missing data in the study. | imputation, missing value, factor analysis, multiple imputation, output delivery system | source | 12 |
| 87 | Using SAS to Produce Report-Ready Summaries of Likert-Type Survey Data: PROC TABULATE, Output Delivery System, PROC TEMPLATE | Imelda C. Go | Beginners in SAS can use simple methods, such as the DATA STEP, PROC FREQ, and PROC MEANS, to compute the summary statistics for their survey data. However, the form in which default SAS output appears is typically not compatible with the appearance of formal reports. In the worst-case scenario, one can get the required statistics from the SAS output and type them manually into the report. Using Likert data as an example, this paper shows how, with data set manipulation, a programmer can use PROC TABULATE, the Output Delivery System, and PROC TEMPLATE to produce customized report-ready output in rich text format (RTF). It is possible to produce RTF output that can be included in formal reports with little or no revision. |
proc step, proc tabulate, proc template | source | 8 |
| 88 | Which Job Sent *THAT* Error Message - How to Generate a Lookup List From Your Metadata | Robert Janka | Imagine you are a newly hired SAS Platform Administrator and Data Integrator. It is trial by fire … jobs are failing! How would you quickly figure out which nightly jobs send which error messages? How would you match up those error texts to the appropriate jobs and create a lookup list that will help not only you, but your co-workers as well? The author found himself in just such a situation. He will present an example using DATA STEP metadata functions to automatically generate this lookup list. |
error message, business intelligence, proc report, sas base, proc step, macro definition | source | 8 |
| 89 | Which SASAUTOS Macros Are Available to My SAS Session? | Harry Droogendyk | The SAS installation process makes a number of SAS supplied macros available to your SAS session via the SASAUTOS option. In addition, useful macros created by users in your organization may be made available to SAS users via SASAUTOS or by way of the Compiled Stored Macro facility. But… how do I know which macros are available? And, if a macro has been defined in more than one location, which one will be utilized? This paper will explain the different methods of defining and storing macros, discuss how SAS searches for macros, demonstrate how to specify the appropriate options to allow access to compiled stored macros and define the SASAUTOS search order. The meaty stuff will highlight parts of %list_sasautos, a useful utility macro written by the author, that will identify all of the macros available to your SAS session. | stored macro, macro definition, macro catalog, catalog, catalog entry, permanent library, autocall macro, work library, macro loop, %macro parameters, %if-%then-%else | source | 15 |
| 90 | While You Were Sleeping, SAS Was Hard At Work | Andrea Wainwright-Zimmerman | Automating and scheduling SAS code to run over night has many advantages, but there are also many pitfalls to be aware of. This paper will discuss how to make sure you save the log, get e-mail messages at critical points, as well as other recommendations and considerations on how to make it all work. | exception handling, proc printto | source | 9 |
| 91 | Yes! SAS ExcelXP WILL NOT Create a Microsoft Excel Graph; But SAS Users Can Command Microsoft Excel to Automatically Create Graphs From SAS ExcelXP Output | William E. Benjamin Jr. | With everything becoming more "Security Aware", hearing that Microsoft EXCEL 2007 can ignore DDE is no surprise. The SAS ODS Tagset ExcelXP creates .xml output, and .xml output cannot contain graphs. Nowadays, no one wants EXCEL spreadsheets with any kind of macros in the workbook. Most default security settings will not open "Macro Infected" workbooks (without an authorized Digital Signature - just try to get one of those). So how can SAS Programmers, without "Admin" rights on your computer, get graphs into your EXCEL workbooks? One way is to build them in EXCEL yourself, one at a time… But, did you really start writing SAS code to build graphs one at a time, "In EXCEL"? This paper shows you how to create data using SAS, then command Microsoft EXCEL to read the data, create a graph or fully reformat a worksheet, build a .xls/.xlsx file (NOTE - no embedded macros can be saved in *.xlsx files); without putting an EXCEL macro into the EXCEL Workbook? And the programs will do it all while you watch, for multiple sheets in a workbook? Over, and Over, and Over again. This paper defines the pieces and parts of SAS code, and EXCEL code, that you can write, which will allow the creation of a fully integrated system to create and format macro free EXCEL 2007 workbooks, without downloading any external data or code from the "Forbidden Unsecure Internet"; using SAS Version 9 (SAS Version 8 if internet downloads are available) and EXCEL 2007. |
excel, xls, proc print | source | 12 |
| 92 | Your Friendly Neighborhood Webcrawler: A Guide to Crawling the Web with SAS | Jake Bartlett, Alicia Bieringer, James A. Cox |
The World Wide Web has a plethora of information; from stock quotes to movie reviews, market prices to trending topics, almost anything can be found at the click of a button. Many SAS users are interested in analyzing data found on the Web, but how do you get this data into the SAS environment? Various methods are available, such as designing your own Web crawler in SAS DATA STEP code or utilizing the %TMFILTER macro in SAS Text Miner. In this paper, we will review the general architecture of a Web crawler. We will discuss the methods of getting Web information into SAS, as well as examine experimental code from an experimental internal project called SAS Search Pipeline. We will also offer advice on how to easily customize a Web crawler to suit individual needs and how to import specific data into SAS Enterprise Miner. |
dataset, proc step, proc sql, data mining | source | 6 |
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.