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A Visual Guide to Stata Graphics

A Visual Guide to Stata Graphics

Whether you are new to Stata graphics or a seasoned veteran this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 1 200 illustrated examples and quick-reference tabs this book quickly guides you to the information you need for creating and customizing high-quality graphs for any type of statistical data. Each graph is displayed in full color with simple and clear instructions that illustrate how to create and customize graphs using Stata commands. Whether you use this book as a learning tool or a quick reference you will have the power of Stata graphics at your fingertips. | A Visual Guide to Stata Graphics

GBP 74.99
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Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation. The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming. The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson DFP BFGS and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator. In later chapters you will learn how to take advantage of Mata Stata's matrix programming language. For ease of programming and potential speed improvements you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata. In the final chapter the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit linear regression Weibull regression the Cox proportional hazards model random-effects regression and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model a model that is not available otherwise in Stata. The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book users will be able to write their own estimation commands-commands that look and behave just like the official estimation commands in Stata. Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use you need this book.

GBP 59.99
1

Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures the book illustrates linear models with continuous predictors (modeled linearly using polynomials and piecewise) interactions of continuous predictors categorical predictors interactions of categorical predictors and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models models where time is a continuous predictor models with time as a categorical predictor nonlinear models (such as logistic or ordinal logistic regression) and models involving complex survey data. The examples illustrate the use of the margins marginsplot contrast and pwcompare commands. This new edition reflects new and enhanced features added to Stata most importantly the ability to label statistical output using value labels associated with factor variables. As a result output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally this second edition illustrates other new features such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata this book is for you.

GBP 59.99
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