• A complete guide to Stata’s graph command and Graph Editor
• Exhaustive examples of customizing graphs using both command options and the Graph Editor
• Visual indexing of features—just look for a picture that matches what you want to do

New in this edition are treatments of contour plots, margins plots, and font handling. Mitchell dedicates a new subsection to contour plots—showing you how to control the number of levels, the colors used, and how to produce effective legends. Over 30 graphs are used to demonstrate what you can accomplish with the new marginsplot command—graphs of estimated means and marginal means (with confidence intervals), interaction graphs, comparisons of groups, and more. Mitchell also adds a section showing you how to get bold text, italic text, subscripts, superscripts, and Greek letters into your titles, axis, labels, and other text.

The book retains its visual style, presenting the reader with a color-coded, visual table of contents that runs along the right edge of every page and shows readers exactly where they are in the book. You can see the color-coded chapter tabs without opening the book, providing quick visual access to each chapter.

The heart of each chapter is a series of entries that are typically formatted three to a page. Each entry shows a graph command (with the emphasized portion of the command highlighted in red), the resulting graph, a description of what is being done, the dataset and scheme used, and a section showing how to produce the result by using the Graph Editor. Because every feature, option, and edit is demonstrated with a graph or screen capture, you can often flip through a section of the book to find exactly the effect you are seeking.

The first chapter discusses how to use the book, the types of Stata graphs, how to use schemes to control the overall appearance of graphs, and how to use options to make specific modifications. It also outlines a process for building graphs using the graph command.

The second chapter is a complete overview of the Graph Editor. It includes over 120 color graphics and screen captures to show exactly how things are done and exactly how they look on the graph. With pictures and words, Mitchell shows you how to change the color, size, or placement of any titles, markers, annotations, or other objects on your graph by using just a few mouse clicks. More subtly, he shows you how to change things such as the number of ticks and labels on your axes, the number of columns in your legends, the label on an individual point, and more. He even shows you how to convert, for example, a scatterplot to a line plot and how to rotate or pivot bar charts. Mitchell also covers advanced topics such as how to draw lines and arrows on graphs so that they continue to reference your objects of interest even if you resize the graph, combine it with other graphs, or change the scale or range of the axes. In short, he exposes all the Graph Editor’s tools, from the simplest to the most powerful. Mitchell does not stop there; almost every example in the book shows you how to accomplish the desired graph or effect not only by using a command or command-line option but also by using the Graph Editor. Just look for the editor icon symbol to learn how to produce the displayed result with the Editor.

Of the Graph Editor, the author writes,

[...] You need to use the Graph Editor for only a short amount of time to see what a smart and powerful tool it is. Whereas commands offer the power of repeatability, the Graph Editor provides a nimble interface that permits you to tangibly modify graphs like a potter directly handling clay.

Mitchell advisedly spends the most time in his next chapter, which is about twoway graphs such as scatterplots, line plots, area plots, bar plots, range plots, contour plots, regression fits, and smooths. Mitchell shows how to create each of these types of graphs and how to use options (and the Graph Editor) to control how the graph looks. He also introduces graphing across groups of data; and options for adding and controlling titles, notes, legends, and so forth. Beyond the basics, he shows how to easily overlay plots to obtain graphs such as regression fits with error contours and observed data scatters, local polynomial smooths with scatters of their underlying data, stock market-style graphs of open and closed values with quantities traded as a bar chart at the bottom, histograms with density smooths, and more. Because Stata’s graph command will let you customize any aspect of the graph, Mitchell spends ample time showing you the most valuable options for obtaining the look you want. After reading this chapter, you will have a thorough grasp of how to create graphs in Stata. Or, if you are in a hurry to discover one special option, you can skim the chapter until you see the effect you want, then glance at the command to see what is highlighted in red.

In the succeeding five chapters, Mitchell covers scatterplot matrices, bar graphs, box plots, dot plots, and pie charts. As with twoway graphs, he shows you how to create each of these graphs and how to adjust every aspect of the graph to your taste (or to a publisher’s required form).

In chapters 9 and 10, Mitchell undertakes an in-depth presentation of the options that are available across almost all graph types—options that add and change the look of titles, notes, and such; control the number of ticks on axes; control the content and appearance of the numbers and labels on axes; control legends; add and change the look of annotations; graph over subgroups; change the look of markers and their labels; apply schemes to control the look of the graph; change the look of graph regions; size graphs and their elements; and more. Again, he also shows how to make these changes both with options and in the Graph Editor.

To complete the graphical journey, Mitchell discusses and demonstrates the 12 styles that unite and control the appearance of the myriad number of graph objects. These styles are angles, colors, clock positions, compass directions, connecting points, line patterns, line widths, margins, marker sizes, orientations, marker symbols, and text sizes.

That completes the main body of the Visual Guide, but don’t skip the appendix. There, Mitchell first gives a quick overview of the dozens of statistical graph commands that are not strictly the subject of the book. Even so, these commands use the graph command as an engine to draw their graphs, and therefore almost all that Mitchell has discussed applies to them. To make this clear, he shows explicitly how to apply common options and common Graph Editor tools to statistical graphs. Then, Mitchell takes on a tour of the new marginsplot command. After that, he addresses combining graphs—showing you how to create complex and multipart images from previously created graphs.

In a crucial section entitled “Putting it all together”, Mitchell shows us how to do just that. We learn more about overlaying twoway plots, and we learn how to combine data management and graphics to create plots such as bar charts of rates with capped confidence intervals, scatterplots with range-finder confidence intervals in both dimensions, and population pyramids.

Mitchell then warns us about mistakes that can be made when typing graph commands and how to correct them. In the appendix, he even show us how to create our own scheme files. Scheme files allow you to control every aspect of how your graphs look without having to specify options. They are the answer to department or journal standards or if you just want all your graphs to have a common appearance that is not one of the schemes shipped with Stata. As with the rest of the book, this section includes cross-references to the Stata Graphics Reference Manual to provide more depth on the subject. Finally, Mitchell reviews all datasets, schemes, and other online supplements available for the book.

The third edition of A Visual Guide to Stata Graphics is a complete guide to Stata’s graph command and the associated Graph Editor. Whether you want to tame the Stata graph command, quickly find out how to produce a graphical effect, master the Stata Graph Editor, or learn approaches that can be used to construct custom graphs, this is the book to read.

Auteur : Michael N. Mitchell
Editeur : Stata Press
ISBN : 978-1-59718-106-8 | 499 pages
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Auteur : Christopher F. Baum
Editeur : Stata Press
ISBN : 978-1-59718-013-9 / 341 pages
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Auteur : Svend Juul & Morten Frydenberg
Editeur : Stata Press
ISBN : 978-1-59718-077-1 / 340 Pages
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Baum steps the reader through the three levels of Stata programming. First up are do-files. Though often thought of as simple batch files, do-files support both loops and conditional execution, and hence can be used for automation as well as reproducibility. While giving examples of do-file programming, Baum introduces useful but often-overlooked Stata constructions.

Next come ado-files, which are used to extend Stata by creating new commands that share the syntax and behavior of official commands. Baum gives an example of how to write a simple additional command for Stata, complete with documentation and certification. After writing the simple command, users can then learn how to write their own custom estimation commands by using both Stata’s built-in numerical maximum-likelihood estimation routine, ml, and its built-in nonlinear least-squares routines, nl and nlsur.

Finishing up the book are two chapters on programming in Mata, which is Stata’s matrix programming language. Mata programs are integrated into ado-files to build a custom estimation routine that is optimized for speed and numerical stability. While stepping through these structures, Baum weaves in the details that are needed to become an expert at Stata programming, so readers will also learn more about Stata itself while learning the tools for programming.

Baum approaches each topic by first explaining the background and need for the topic, then looking at the basic usage and examples, and finally examining use within larger, more applied “cookbook” examples. Many of his examples come from questions posed on the Statalist listserver, so they address complexities of interest to a broad range of Stata users. The programming examples cover an array of topics, illustrate some of Stata’s built-in tools (such as the resampling techniques of bootstrapping and jackknifing), and offer solutions to tricky data-management questions.

The breadth and depth of this book make it a necessity for anyone interested in programming in Stata.

Auteur : Christopher F.Baum
Editeur : Stata Press
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Michael N. Mitchell’s Data Management Using Stata comprehensively covers data-management tasks, from those a beginning statistician would need to those hard-to-verbalize tasks that can confound an experienced user. Mitchell does this all in simple language with illustrative examples.

The book is modular in structure, with modules based on data-management tasks rather than on clusters of commands. This format is helpful because it allows readers to find and read just what they need to solve a problem at hand. To complement this format, the book is in a style that will teach even sporadic readers good habits in data management, even if the reader chooses to read chapters out of order.

Throughout the book, Mitchell subtly emphasizes the absolute necessity of reproducibility and an audit trail. Instead of stressing programming esoterica, Mitchell reinforces simple habits and points out the time-savings gained by being careful. Mitchell’s experience in UCLA’s Academic Technology Services clearly drives much of his advice.

Mitchell includes advice for those who would like to learn to write their own data-management Stata commands. Even experienced users will learn new tricks and new ways to approach data-management problems.

This is a great book - thoroughly recommended for anyone interested in data management using Stata.

Auteur : Michael N. Mitchell
Editeur : Stata Press
ISBN : 978-1-59718-076-4 | 387 pages
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Researchers wishing to fit regression models to survival data have long faced the difficult task of choosing between the Cox model and a parametric survival model such as Weibull. Cox models can be fit using Stata’s stcox command, and parametric models are fit using streg, which offers five parametric forms in addition to Weibull. While the Cox model makes minimal assumptions about the form of the baseline hazard function, prediction of hazards and other related functions for a given set of covariates is hindered by this lack of assumptions; the resulting estimated curves are not smooth and do not possess information about what occurs between the observed failure times. Parametric models offer nice, smooth predictions by assuming a functional form of the hazard, but often the assumed form is too structured for use with real data, especially if there exist significant changes in the shape of the hazard over time.

This text is concerned with obtaining a compromise between Cox and parametric models that retains the desired features of both types of models. The book is aimed at researchers who are familiar with the basic concepts of survival analysis and with the stcox and streg commands in Stata. As such, it is an excellent complement to An Introduction to Survival Analysis Using Stata by Cleves et al. (2010).

This book is written for Stata 12 but is fully compatible with Stata 11 as well.

Much of the text is dedicated to estimation with Royston–Parmar models using the stpm2 command, which is maintained by the authors and available from the Statistical Software Components (SSC) archive at http://www.repec.org. Royston–Parmar models are highly flexible alternatives to the exponential, Weibull, loglogistic, and lognormal models (fit using streg) that allow extension from proportional hazards to proportional odds and to scaled probit models. Additional flexibility is obtained by the use of restricted cubic spline functions as alternatives to the linear functions of log time used in standard models. The authors demonstrate fitting these models and graphing predicted hazards, cumulative hazards, and survival functions with real data from breast cancer and prostate cancer studies.

After some introductory material on the motivation behind flexible parametric models and on working with survival data in Stata, the authors proceed by demonstrating that Cox models may instead be expressed as Poisson models by splitting the time scale at the observed failures. The Poisson-model expression allows extension by changing how the time scale is split and by introducing restricted cubic splines and fractional polynomials. Royston–Parmar models are then introduced, followed by material on model building and diagnostics for these models. Considerable attention is then given to time-dependent effects, how these may be modeled, and how to interpret the graphs of the predicted functions the models produce. This material is followed by a chapter on relative survival models such as those used for population-based cancer studies. This chapter is very thorough, relates well to the previous material, and is an ideal introduction for those new to the concepts of relative survival and excess mortality. The final chapter is devoted to advanced topics such as determining the number needed to treat (NNT), handling multiple-event data, and analyzing competing risks.

Auteur : Patrick Royston & Paul C. Lambert
Editeur : Stata Press
ISBN : 978-1-59718-079-5 | 347 pages
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The revised edition has been updated to reflect the new features available in Stata 11 that are germane to microeconomists. Instead of using mfx and the user-written margeff commands, the revised edition uses the new margins command, emphasizing both marginal effects at the means and average marginal effects. Factor variables, which allow you to specify indicator variables and interaction effects, replace the xi command. The new gmm command for generalized method of moments and nonlinear instrumental-variables estimation is presented, along with several examples. Finally, the chapter on maximum likelihood estimation incorporates the enhancements made to ml in Stata 11.

Early in the book, Cameron and Trivedi introduce simulation methods and then use them to illustrate features of the estimators and tests described in the rest of the book. While simulation methods are important tools for econometricians, they are not covered in standard textbooks. By introducing simulation methods, the authors arm students and researchers with techniques they can use in future work. Cameron and Trivedi address each topic with an in-depth Stata example, and they reference their 2005 textbook, Microeconometrics: Methods and Applications, where appropriate.

The authors also show how to use Stata’s programming features to implement methods for which Stata does not have a specific command. Although the book is not specifically about Stata programming, it does show how to solve many programming problems. These techniques are essential in applied microeconometrics because there will always be new, specialized methods beyond what has already been incorporated into a software package.

Cameron and Trivedi’s choice of topics perfectly reflects the current practice of modern microeconometrics. After introducing the reader to Stata, the authors introduce linear regression, simulation, and generalized least-squares methods. The section on cross-sectional techniques is thorough, with up-to-date treatments of instrumental-variables methods for linear models and of quantile-regression methods.

The next section of the book covers estimators for the parameters of linear panel-data models. The authors’ choice of topics is unique: after addressing the standard random-effects and fixed-effects methods, the authors also describe mixed linear models—a method used in many areas outside of econometrics.

Cameron and Trivedi not only address methods for nonlinear regression models but also show how to code new nonlinear estimators in Stata. In addition to detailing nonlinear methods, which are omitted from most econometrics textbooks, this section shows researchers and students how to easily implement new nonlinear estimators.

The authors next describe inference using analytical and bootstrap approximations to the distribution of test statistics. This section highlights Stata’s power to easily obtain bootstrap approximations, and it also introduces the basic elements of statistical inference.

Cameron and Trivedi then include an extensive section about methods for different nonlinear models. They begin by detailing methods for binary dependent variables. This section is followed by sections about multinomial models, tobit and selection models, count-data models, and nonlinear panel-data models. Two appendices about Stata programming complete the book.

The unique combination of topics, intuitive introductions to methods, and detailed illustrations of Stata examples make Microeconometrics Using Stata an invaluable, hands-on addition to the library of anyone who uses microeconometric methods.

Auteur : A. Colin Cameron and Pravin K. Trivedi
Editeur : Stata Press
ISBN-13 : 978-1-59718-073-3 | 706 pages
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The second edition has much to offer for readers of the first edition, reading more like a sequel than an update. The text has almost doubled in length from the original, coming in at 562 pages. This second edition incorporates three new chapters: a chapter on standard linear regression, a chapter on discrete-time survival analysis, and a chapter on longitudinal and panel data containing an expanded discussion of random-coefficient and growth-curve models. The authors have updated this edition for Stata 10, expanding on discussions in the original edition and adding new in-text examples and end-of-chapter exercises. In particular, the authors have thoroughly covered the new Stata commands xtmelogit and xtmepoisson.

Multilevel and Longitudinal Modeling Using Stata, Second Edition, by Sophia Rabe-Hesketh and Anders Skrondal, looks specifically at Stata’s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are “mixed” because they allow fixed and random effects, and they are “generalized” because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.

Auteur : Sophia Rabe-Hesketh and Anders Skrondal
Editeur : Stata Press
ISBN : 978-1-59718-040-5 | 562 pages
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Seventy-six Stata Tips , 2nd Edition(English)

Since 2003, the Stata Journal has included Stata Tips on special issues in data analysis with Stata. Now Seventy-six Stata Tips, 2nd Edition compiles these useful guides into a compact tome for ease of reference. In keeping with the Stata spirit, Tips are from Stata users and StataCorp employees alike and will serve as guideposts for both new and experienced users. Seventy-six Stata Tips includes the first 33 tips of the series, previously published in the book thirty-three Stata Tips.

Auteur : H. Joseph Newton and N. J. Cox
Editeur : Stata Press
ISBN : 978-1-59718-071-9 | 177 pages
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The Stata Survival Manual (English)

The Stata Survival Manual, by David Pevalin and Karen Robson, is a nicely written introduction to practical use of Stata 10. The style is friendly and flows well, and the authors do not assume prior knowledge of Stata or statistical sophistication from the reader. Both Stata and statistical usage are explained throughout.

The text steps through the basics of using Stata, starting with basic usage of Stata and working through common data-management techniques for table and graph creation, analysis, and presentation of results. Special focus is given to working with categorical variables and building scales from instruments. The analysis sections detail how to fit interactions and explain them to nonstatistical audiences using graphs. Each chapter begins with a presentation of new tools in Stata and simple examples of their use. The tools are then applied in a “Demonstration Exercise” to an example that runs throughout the book. Thus the reader can learn new tools in a simple setting and see their use in an analysis on a real-life dataset from start to finish.

At several points in the book, especially in the chapters working with data management, the authors point out differences between Stata and SPSS for those making the transition from SPSS to Stata. While the authors focus on using do-files for reproducibility, they also show how to use the menus and dialog boxes for those accustomed to working in this fashion.

Auteur : David Pevalin and Karen Robson
Editeur : McGraw-Hill
ISBN : 978-0-335-22388-6 | 373 pages
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Comment from the Stata technical group

The Workflow of Data Analysis Using Stata, by J. Scott Long, is an essential productivity tool for data analysts. Aimed at anyone who analyzes data, this book presents an effective strategy for designing and doing data-analytic projects.

In this book, Long presents lessons gained from his experience with numerous academic publications, as a coauthor of the immensely popular Regression Models for Categorical Dependent Variables Using Stata, and as a coauthor of the SPOST routines, which are downloaded over 20,000 times a year.

A workflow of data analysis is a process for managing all aspects of data analysis. Planning, documenting, and organizing your work; cleaning the data; creating, renaming, and verifying variables; performing and presenting statistical analyses; producing replicable results; and archiving what you have done are all integral parts of your workflow.

Long shows how to design and implement efficient workflows for both one-person projects and team projects. Long guides you toward streamlining your workflow, because a good workflow is essential for replicating your work, and replication is essential for good science.

An efficient workflow reduces the time you spend doing data management and lets you produce datasets that are easier to analyze. When you methodically clean your data and carefully choose names and effective labels for your variables, the time you spend doing statistical and graphical analyses will be more productive and more enjoyable.

After introducing workflows and explaining how a better workflow can make it easier to work with data, Long describes planning, organizing, and documenting your work. He then introduces how to write and debug Stata do-files and how to use local and global macros. Long presents conventions that greatly simplify data analysis—conventions for naming, labeling, documenting, and verifying variables. He also covers cleaning, analyzing, and protecting your data.

While describing effective workflows, Long also introduces the concepts of basic data management using Stata and writing Stata do-files. Using real-world examples, Stata commands, and Stata scripts, Long illustrates effective techniques for managing your data and analyses. If you analyze data, this book is recommended for you.

Comments from readers

You have written the book that I had planned to write someday. But I’m glad I didn’t—your book is much better. Congratulations, this was greatly needed.

Prof. Bill Gardner
The Ohio State University

I will post the announcement of Workflow on my door with the following note: “I’m glad to help anybody who followed at least 25% of the advice Long provides—and brings me their do-files!”

Prof. Alan C. Acock
Oregon State University

I just wanted to send you a thank you for taking the time to write this book. I feel a little like an obsessed fan because I read it for several hours last night, bought 3 copies for my new research team and am presenting our new organization scheme tomorrow. It turns out that we have just finished a first flurry of data collection and hiring and I’ve been scratching my head about how to systematize some aspects. It is a perfect time to superimpose a structure. I’ve used aspects of your plan in my own work (hence my eagerness to adopt) but having this coherent volume is a wonderful and practical resource. I learned a lot from reading this. Thank you!

Elizabeth Gifford, Ph.D.
Research Scientist
Duke University

I just received a knock at my door with my new copy of The Workflow of Data Analysis Using Stata. I immediately ripped off the packaging and began perusing it. Just before the knock, I was attempting to write a program to get Stata to save the r(mean) and r(sd) for two variables following a summarize command to be saved for a ttesti command. After looking at your book for about two minutes, I stumbled upon pages 91–92, where it gave me all the information I need. … I have only had the book about 10 minutes and already it has made my life easier. Thanks much, and I am already looking forward to reading the rest of the book!

Claire M. Kamp Dush, Ph.D.
The Ohio State University

I am a Spanish professor of public economics who is at present enjoying a study-research leave at Melbourne University (Australia). Because of that I have had the time to read your book from cover to cover. I just want to thank you for the incredible work you have done! A book such as this one is a must for anyone trying to make an academic career. Definitely, I will recommend it to my graduate students as soon as I go back to Spain. If I had the chance to reach this book twenty years ago I would have been much more efficient doing my work. Never is it too late! Thanks!

Prof. Jose Felix Sanz-Sanz
Dept. of Applied Economics
Universidad Complutense de Madrid

Auteur : J. Scott Long
Editeur : Stata Press
ISBN : 978-1-59718-047-4 | 379 pages
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• expository papers that link the use of Stata commands or programs to associated principles, such as those that will serve as tutorials for users first encountering a new field of statistics or a major new technique
• papers that go "beyond the Stata manual" in explaining key features or uses of Stata that are of interest to intermediate or advanced users of Stata
• papers that discuss new commands or Stata programs of interest either to a wide spectrum of users (e.g., in data management or graphics) or to some large segment of Stata users (e.g., in survey statistics, survival analysis, panel analysis, or limited dependent variable modeling)
• papers analyzing the statistical properties of new or existing estimators and tests in Stata
• papers that could be of interest or usefulness to researchers, especially in fields that are of practical importance but are not often included in texts or other journals, such as the use of Stata in managing datasets, especially large datasets, with advice from hard-won experience
• papers of interest to those who teach, including Stata with topics such as extended examples of techniques and interpretation of results, simulations of statistical concepts, and overviews of subject areas.

Auteur : H. Joseph Newton and Nicholas J. Cox
Editeur : Stata Press | 327 pages
(Abonnement de 3 ans)
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IMPORTANT ! Merci de nous indiquer votre adresse email lors de votre commande pour la livraison de l'abonnement (voie électronique)

Includes

• Getting Started with Stata manuals (all 3 platforms)
• User's Guide
• Base Reference Manual (4 volumes)
• Data-Management Reference Manual
• Graphics Reference Manual
• Longitudianl-Data/Panel-Data Reference Manual
• Mata Reference Manual (2 volumes)
• Multiple-Imputation Reference Manual
• Multivariate Statistics Refrence Manual
• Programming Reference Manual
• Structural Equation Modeling Reference Manual
• Survey Data Reference Manual
• Survival Analysis and Epidemiological Tables Reference Manual
• Time-Series Reference Manual
• Quick Reference and Index
  

Auteur/Editeur : Stata Press | 9 471 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-082-5 | 140 pages (Mac)
ISBN : 978-1-59718-084-9 | 144 pages (Windows)
ISBN : 978-1-59718-083-2 | 148 pages (Unix)
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Table of Contents for Windows
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-095-5 | 396 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-098-6 | 2 379 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-080-1 | 636 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-081-8 | 663 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-096-2 | 570 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-086-3 | 923 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-089-4 | 365 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-090-0 | 687 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-091-7 | 486 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-097-9 | 300 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-093-1 | 202 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-092-4 | 554 pages
Prix : 48 €HT
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-094-8 | 717 pages
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Auteur/Editeur : Stata Press
ISBN : 978-1-59718-085-6 | 190 pages
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