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lag function

122 papers and 1 presentation; 99 titles.

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Conference × Year × Title × Authors Extras Pages
SESUG 2024 Advanced DATA Step Look-Back and Look-Ahead Techniques Joshua M. Horstman source 5
SESUG 2024 SAS Programming Methods to Identify Episodes of Hospital Readmissions Jennifer Cano,
Cainnear K. Hogan,
Taylor N. Whittington,
Julien Weinstein,
Scott J. Denstaedt,
Sarah M. Seelye,
Hallie C. Prescott		 source 10
WUSS 2024 A Survey of Some of the Most Useful SAS Functions [presentation] Ronald P. Cody source 61
WUSS 2024 Advanced DATA Step Look-Back and Look-Ahead Techniques Joshua M. Horstman source 5
WUSS 2024 Count, Lag, Retain! Let's go through it one more time! Steven C. Black source 10
SESUG 2023 Forecasting Goals for Student Success Metrics in Accountability Performance Reporting using SAS Visual Analytics Xiaoying Liu,
Hennadii Balashov		 source 9
WUSS 2022 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
SESUG 2020 Data Step versus Everybody: Approaching Problems as a Beginning Coder Brian K. Varney source 13
SESUG 2020 The Efficient Alternative to LAG Function without LAG's Pitfalls Alexander M. Feigin source 8
SESUG 2019 Data Step versus Everybody: Approaching Problems as a Beginning Coder Brian K. Varney source 12
SGF 2019 FETCH()ing Use Cases for the Data Access Functions Chad Mukherjee source 11
SGF 2019 The Bayesians are Coming! The Bayesians are Coming! The Bayesians Are Coming to Time Series! Aric LaBarr source 13
WUSS 2018 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
PharmaSUG 2018 SAS Macros of Performing Look-Ahead and Look-Back Reads Yanhong Liu source 10
SGF 2018 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
SGF 2018 Parallel Programming with the DATA Step: Next Steps David Bultman,
Jason Secosky		 source 16
SGF 2018 SAS Arrays and Macros Make Processing Claims with Multiple Conditions Easier Shavonne J. Standifer source 10
SESUG 2017 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed. Mark Keintz source 15
SESUG 2017 Tales from the Help Desk: Solutions to Common DATA Step Tasks Bruce F. Gilsen source 19
PharmaSUG 2017 Mentoring and Oversight of Programmers across Cultures and Time Zones Chad W. Melson source 7
SGF 2017 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
SGF 2017 Hash Objects: When LAGging Behind Just Doesn't Work Andrea Wainwright-Zimmerman source 5
SGF 2017 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP, 2nd ed. Mark Keintz source 15
SGF 2017 Profitability and Actuarial Overview of Health Insurance on SAS Visual Analytics Janice Leal source 9
SGF 2017 Tales from the Help Desk: Solutions to Common DATA Step Tasks Bruce F. Gilsen source 19
SESUG 2016 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP Mark Keintz source 15
MWSUG 2016 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP Mark Keintz 15
MWSUG 2016 Mentoring and Oversight of Programmers across Cultures and Time Zones Chad W. Melson 6
PharmaSUG 2016 Remember to always check your simple SAS function code! Yingqiu Yvette Liu source 8
SGF 2016 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
SGF 2016 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP Mark Keintz source 15
SESUG 2015 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
WUSS 2015 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP Mark Keintz source 15
PharmaSUG CN 2015 SAS Longitudinal Data Techniques - From Change from Baseline to Change from Previous Visits Chao Wang 5
SGF 2015 Examination of Three SAS Tools for Solving a Complicated Data Summary Problem Carry W. Croghan source 12
SGF 2015 Health Services Research Using Electronic Health Record Data: A Grad Student How-To Paper Ashley Collinsworth,
Elisa L. Priest		 source 9
SGF 2015 Incorporating External Economic Scenarios into Your CCAR Stress Testing Routines Kenneth Sanford,
Christian Macaro		 source 10
SGF 2015 Leads and Lags: Static and Dynamic Queues in the SAS DATA STEP Mark Keintz source
zip		 14
SGF 2015 Some of the Most Useful SAS Functions Ronald P. Cody source 16
SGF 2015 Your Database Can Do SAS Too! Harry Droogendyk source 18
MWSUG 2014 Creating Code writing algorithms for producing n-lagged variables Matthew Bates 9
MWSUG 2014 SAS: Data Manipulation Tools Audrey Yeo 12
WUSS 2014 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
WUSS 2014 LEADS AND LAGS: HANDLING QUEUES IN THE SAS DATA STEP Mark Keintz source 12
SGF 2014 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
SGF 2014 SAS Solutions to Identifying Hospital Readmissions Weifeng Fan,
Maryam Sarfarazi		 source 8
WUSS 2013 Constructing Begin-End Date Intervals from Event or As-of Dates in a SAS DATA Step Thomas E. Billings source 8
WUSS 2013 Dynamic Regression in ARIMA Modeling Ross Bettinger source 11
NESUG 2013 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody 16
NESUG 2013 Leads and Lags in SAS Mark Keintz 11
NESUG 2013 Marketing and Granger Causality: Going Beyond Correlation Jonathan Prantner 11
PharmaSUG 2013 Let's get SASsy Amie Bissonett source 6
NESUG 2012 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody 16
NESUG 2012 Generalized Additive Models in Marketing Mix Modeling Revisited Patralekha Bhattacharya 9
SGF 2012 A Survey of Some of the Most Useful SAS Functions Ronald P. Cody source 16
PHUSE EU 2011 Learning to love the SAS LAG function Herman Ament,
Paul Vervuren		 source
presentation source		 6
NESUG 2011 A Comparison of PDLREG and GAM Procedures in Measuring Dynamic Effects Patralekha Bhattacharya 11
SGF 2011 LAG Function Combined with Conditional Functions: Useful for Identifying Differences in Like Data Andrew Hummel source 8
SGF 2011 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody source 7
NESUG 2010 Please Don't Lag Behind LAG ! Anjan Matlapudi,
James D. Knapp		 12
NESUG 2010 Using Generalized Additive Models in Marketing Mix Modeling Patralekha Bhattacharya 8
NESUG 2010 Using Google to Solve SAS Problems Michael Todd 13
WUSS 2010 LOCF-Different Approaches, Same Results Using LAG Function, RETAIN Statement, and ARRAY Facility Iuliana Barbalau source 5
WUSS 2010 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody source 7
WUSS 2010 Use RETAIN Statement and LAG Function to Indentify Consecutively Repeated Conditions Lan Xiao source 5
SESUG 2010 LAG Function Combined with Conditional Functions - Useful in Identifying Differences in Like Data Andrew Hummel source 7
PharmaSUG 2010 Looking for a Function Called LEAD? Couldn't Find? Here You Go - Multiple Ways to Simulate It Niraj J. Pandya 5
SCSUG 2009 Working with Longitudinal Data Ronald P. Cody 4
PHUSE EU 2009 reventing the "Bad Hire" - Behavioural Interview Techniques for SAS Programming Candidates Pamela J. Giese source 7
NESUG 2009 Techniques to Perform the Lead Function Through Data Manipulation Kruti Pandya,
Niraj J. Pandya		 6
SGF 2009 Creating Common Information Structures Using Lists Stored in SAS Data Step Hash Objects Shawn Edney source 19
SGF 2009 LAG - the Very Powerful and Easily Misused SAS Function Yunchao (Susan) Tian source 5
SGF 2009 Simulating Time Series Analysis Using SAS - Part I The Augmented Dickey-Fuller (ADF) Test Ismail E. Mohamed source 7
SESUG 2008 Creating Common Information Structures Using Lists Stored in SAS Data Step Hash Objects Shawn Edney source 18
SESUG 2008 PDA Data Collection: Data Collection issues... SAS Data Step to the Rescue! Mira Shapiro source 6
NESUG 2008 Time Series Analysis Using SAS - Part I - The Augmented Dickey Fuller (ADF) Test Ismail E. Mohamed 6
SGF 2008 Easy Rolling Statistics with PROC EXPAND Premal P. Vora source 6
NESUG 2007 Comparing Values Across Observations: Be Careful Prafulla Girase 4
NESUG 2007 Conditional Lags Don't Have to Be Treacherous Howard L. Schreier 10
NESUG 2007 PROC EXPAND to the Rescue! Premal Vora 5
SESUG 2007 Smart SAS Tricks Monal Kohli,
Smith Hanley		 source 7
SGF 2007 Calculating Duration via the Lagged Values of Variables Pon Su source 5
SGF 2007 Using SAS to Process Repeated Measures Data - A Hands-On Approach Terry Gareleck,
Cyndie B. Fain		 source 12
MWSUG 2006 A Picture is Worth a Thousand Data Points: Using SAS Arrays, LAG Functions and ODS to Create a Flexible Survey Interviewer Tracking Chart Joe Murphy 9
SESUG 2006 Detection and Quantification of Asymmetric Peaks Carry W. Croghan,
Ronald W. Williams		 source 13
WUSS 2006 Calculating Duration via the Lagged Values of Variables Pon Su source 4
NESUG 2006 LAG - the Very Powerful and Easily Misused SAS Function Yunchao (Susan) Tian 5
SUGI 2006 Using SAS to Process Repeated Measures Data Terry Fain,
Cyndie B. Gareleck		 source 11
NESUG 2005 "LAG With a WHERE" and Other DATA Step Stories Neil H. Howard 14
PharmaSUG 2005 Analyzing Clinical Drug Utilization Jianming He,
Brian Griffin		 10
PharmaSUG 2005 Retaining, Lagging, Leading, and Interleaving Data Toby Dunn,
Chang Y. Chung		 8
SCSUG 2004 Retaining, Lagging, Leading, and Interleaving Data Toby Dunn 14
SESUG 2004 Case Studies in Time Series David A. Dickey source 10
PharmaSUG 2004 "LAG With a WHERE" and Other DATA Step Stories Neil H. Howard 14
PharmaSUG 2004 Common Error: Wrong Result Varsha C. Shah 5
SUGI 2004 A SAS Macro to Count Consecutive Days Pingping Zhang source 5
SUGI 2004 Tales From the Help Desk: Solutions for Simple SAS Mistakes Bruce F. Gilsen source 9
NESUG 2003 Deja-vu all Over Again: Common Mistakes by new SAS Users Bruce F. Gilsen 6
NESUG 2002 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody 6
SESUG 2002 The Lag-o-Matic: An Improved Method for Selecting the Lag Structure of Multiple Predictor Variables in the Absence of Theory David C. Sharp,
Stephen M. Finnigan		 source 5
SUGI 2002 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody source 4
NESUG 2000 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody 6
SCSUG 1999 Longitudinal Data Techniques Ronald P. Cody 6
NESUG 1999 Longitudinal Data Techniques: Looking Across Observations Ronald P. Cody 6
MWSUG 1999 A Fast Format Macro - How to Quickly Create a Format by Specifying the Endpoints Christine A. Smiley 4
SUGI 1999 A Fast Format Macro How to Quickly Create a Format by Listing the Endpoints Christine A. Smiley source 4
SUGI 1999 Betcha Didn't Know Richard D. Langston source 4
SEUGI 1998 Autocorrelation as an artificial reponses detector: an IML application Efraim Darom source 6
NESUG 1997 The Power of the SAS Date Functions: Creating a Utilization History for Residents of Nursing Homes Sandra T. Rothwell 7
MWSUG 1997 A Macro for Efficient and Flexible Data Smoothing John Cucka 7
SUGI 1997 A Macro for Efficient and Flexible Data Smoothing John Cucka source 4
SUGI 1997 The Power of the SAS Date Functions: Creating a Utilization History for Residents of Nursing Homes Sandra T. Rothwell,
Mary Ann Bush,
Ilene Gottfried,
Dawn Scott		 source 7
SESUG 1995 LAGging Questions Debbie Hoppe,
John Alden		 2
SEUGI 1995 Wouldn't it be Good if we Understood what Drives our Claims Kevin Williams source 9
NESUG 1993 An Introduction to SAS Functions Ronald P. Cody 6
SUGI 1993 Facilitation of Retrieval of Brain Image Measurements with the SAS System Gail Lerner,
Nancy Y. Li,
Martin Redmond,
Houwei Wu,
Jose Maria J. Alvir,
Robert Bilder,
Manzar Ashtari		 source 4
SESUG 1993 How to "RETAIN" Your Sanity: Some Annotated Examples Ronald P. Cody,
Ray Pass		 6
NESUG 1992 How to "RETAIN" Your Sanity: Some Annotated Examples Ronald P. Cody,
Ray Pass		 8
SUGI 1992 Using SAS/GRAPH Software for Three-Dimensional Illustrations of VL Amino Acid Diversity7 .. Perry Watts,
Samuel Litwin		 source 6
NESUG 1991 An Introduction to SAS Functions Ronald P. Cody 10
MWSUG 1991 An Introduction to SAS Functions Ronald P. Cody 10
SUGI 1991 An Introduction to SAS Functions Ronald P. Cody source 6
SUGI 1990 Estimation of Area Under Concentration - Time Curves (AUC) Using the LAG Function and the Trapezoidal Rule R. A. Rogers,
W. L. Tippitt		 source 1

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