bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance evaluation of vegetable base oils relative to mineral base oils in the lubrication of cold forming processes of 2024 aluminum alloy
Authors: Mashhadi Keshtiban Peyman , Sheydaei Govarchin Ghaleh Saeid , Alimirzaloo Vali
Published: November 2018

Due to lower environmental pollution and also higher biodegradability of vegetable base oils relative to mineral base oils, they can be a good alternative for mineral base types in the metal forming industry. The aim of this study was to evaluate the lubrication efficiency of vegetable base oils relative to mineral base oils (SAE 10 W oil) and, if possible, to replace them with mineral base oils in forging. For the extraction of shear friction coefficient, the friction calibration curves have been used by implementing ring compression tests. In order to evaluate the performance of vegetable base oils and do comparison with mineral base oils, surface roughness, friction coefficient and pressing forces have been extracted for both conditions. Experimental works were carried out using a hydraulic press putting pressure on ring-shaped specimens of 2024 aluminum alloy at room temperature. The results of the experimental studies showed that the vegetable base oils had a satisfactory lubrication performance relative to the mineral base oil (SAE 10 W oil). It was inferred that vegetable base lubricants especially walnut oil, leads to 22% reduction of friction coefficient and also it yields to much lower forming forces compared with mineral base lubricants. Moreover, using vegetable base oils leads to more perseverance of the initial surfaces quality about 26% and 37% relative to the mineral base lubricant and the dry status (no lubrication), respectively.
Economic impact of language tourism on mature sun and sand destinations: The case of Alicante (Spain)
Authors: Barra Pilar , Marco Bartolomé , Cachero Cristina
Published: November 2018

The objective of this research is to measure the economic impact of language tourism in a medium-sized Spanish city, Alicante, which has been traditionally considered a sun and sand destination on the Mediterranean coast. This analysis aims to highlight the importance of developing a tourism diversification strategy and the desirability of promoting alternative forms of tourism. This investigation demonstrates the significant economic impact that language tourism has on a consolidated tourism destination as Alicante, which is higher, in relative terms, than the impact generated by conventional tourism. Based on the findings, the implications of the study are also discussed.
Tourism–growth nexus and spatial spillovers: Evidence from Greece
Authors: Eleftheriou Konstantinos , Sambracos Evangelos
Published: November 2018

In this research note, we reconsider the tourism–growth nexus by accounting for spillover effects between regional tourism development and regional economic growth. For this purpose, we utilize spatial panel econometric techniques to measure the above effects in 49 Greek prefectures during the period 2010–2014. Our findings indicate strong short-run and long-run spillover effects, suggesting that policymakers should consider regional tourism development as a key factor for boosting national economic growth.
Gender, love, and sex: Using duoethnography to research gender and sexuality minority experiences of transgender relationships
Authors: Vincent Ben , Erikainen Sonja
Published: November 2018

This article uses a duoethnographic approach to explore the intersection of lesbian and queer sexualities and transgender identities in intimate relationships. By comparing experiences of gender and sexual identity negotiation within transgender relationships, the authors document how sexual identity borders are traversed, and how gender is negotiated and interrogated in and through these relationships. We argue that our differential experiences of ‘queer’ as an identity, our relationship challenges and how we express/relate to gender are heavily shaped by feminist politics, and how social interactions are gendered.
LGBT activism in Estonia: Identities, enactment and perceptions of LGBT people
Authors: Tiidenberg Katrin , Allaste Airi-Alina
Published: November 2018

This article explores how Estonian LGBT activists make sense of their own activism. We analyze the activists’ perceptions of their activism, their identities and how those identities are deployed for action. All of these are, in turn, situated in how activists understand the broader Estonian LGBT community, and Estonian society’s historico-politically complex relationship with activism as such. The article is theoretically grounded within the new social movement theories and theories of emergent LGBT and activist identities. The analyzed material consists of interviews, observations, documents and meeting notes gathered via ethnographic fieldwork with Estonian LGBT activists in 2012–2013. Pragmatic and iterative qualitative analysis revealed that the activists studied resist the activist identity, and perceive there to be a weak collective identity among the broader Estonian LGBT population. However, the lobbying for the Registered Partnership Law (passed in 2014) brought a shift in LGBT activists’ ways of enacting their identities and their perception of the possibility of LGBT activism in Estonia.
Approaching media industries comparatively: A case study of streaming
Authors: Herbert Daniel , Lotz Amanda D. , Marshall Lee
Published: November 2018

Although ‘streaming’ media has become increasingly common across multiple media industries, significant differences underpin the industrial practices that allow this behavior and explain discrepant experiences of internet distribution across industries. This article uses collaborative comparative media industry analysis to investigate the commonalities and variations among streaming in the US music, film, and television industries to assess the viability of theorizing the cultural implications of streaming as a consistent phenomenon across media industries. The article explores the consistencies and divergences of streaming among consumer experience, business practices, and textual implications to compare how established uses, production practices, and media content have been affected by internet distribution. Such detailed industry comparison is a novel approach, and the article also considers the methodological value of rigorous collaboration among scholars expert in different media industries. The analysis is based on industry data and practices obtained through trade press, industry reports, and interviews with media workers consistent with a critical media industries approach.
Game-based student response system: Revisiting its potentials and criticalities in large-size classes
Authors: Ranieri Maria , Raffaghelli Juliana E , Bruni Isabella
Published: November 2018

Instructor–student interaction and regular feedback are seen as teaching strategies designed to support effective learning. These days, there is increasing reliance on technology to support this in the classroom; one such technology is a student response system or its more recent development of this, that is, a game-based one. This study investigates the potential of using game-based student response systems for formative assessment through the analysis of learning outcomes and students’ perceptions of the gamified experience in terms of engagement and pleasantness. A quasi-experiment was conducted within an undergraduate course on educational technologies, involving about 400 students. Learning improvements were measured through a pre- and posttest, and students’ perceptions were elicited through a questionnaire. Data analysis showed a high level of student satisfaction and an overall improvement of learning outcomes due to the use of game-based student response systems. However, differences in terms of effectiveness were found according to the nature of the topics dealt with in the course, that is, whether or not they were used in the development of theoretical knowledge rather than the use of that knowledge in a more practical context.
Mapping brand similarities: Comparing consumer online comments versus survey data
Authors: Vriens Marco , Chen Song , Vidden Chad
Published: November 2018

Online consumer behavior has become a valuable and viable source of consumer insights. Consumer comments in online forums, or discussion groups, have proven useful as a source to extract brand similarity data from. Apart from the cost and speed advantages, such data can be captured easily over different time periods. Both online consumer-generated data (CGD) and surveys have their pros and cons. To date, little is known as to how these two data sources compare in terms of brand insights. In this study, we discuss the results from analyzing survey and consumer-generated online data pertaining to the U.S. skincare market. Our study included 57 brands, and we used multidimensional scaling (MDS), t-stochastic neighbor embedding (t-SNE; an alternative to MDS), hierarchical clustering, and additive similarity trees (an extension of hierarchical clustering) to analyze the data. We show that the outcomes vary between CGD and surveys. As an additional insight, we show that, rather than the spatial scaling methods, additive trees result in a much better fit of brand similarity data in cases where we have many brands.
Defending academic freedom: Arts and Humanities research as constrained writing
Authors: Francis Mary Anne
Published: November 2018

This article notes that while there is a large literature lamenting increasing assaults on academic freedom, there is little literature to address ways in which it might be preserved. Sampling that writing, it finds some concern with protecting academic freedom in extreme scenarios, via discrete programmes, and generalised dissidence, but no discussion of determinate action applicable to all Arts and Humanities research. Defining academic freedom via the UK’s legal framework and elaboration in Judith Butler’s writing, the article inventorises significant assaults in recent times, noting the roles of government and the market in such. Following the literature review, it proposes a new, interventionist tactic for preserving academic freedom, suggesting that undue constraints should be annotated when research is written up, and that this space should also be used to suggest constructive alternatives. This strategy is demonstrated as the article acknowledges some of the constraints on its own production and suggests redress.
The influence of numerical parameters in the finite-volume method on the Wigley hull resistance
Authors: Andrun Martina , Blagojević Branko , Bašić Josip
Published: November 2018

The equations discretization errors are often overlooked compared to the spatial discretization errors. This article presents the results of the influence of various equations discretization schemes in computational fluid dynamics on the prediction of the ship resistance and wave elevation on the hull. For the analysis, steady flow around a model of the Wigley hull is numerically predicted by employing a finite-volume method solver based on Reynolds-averaged Navier–Stokes equations and the volume-of-fluid method. Six momentum discretization schemes, three multi-fluid discretization schemes and three gradient schemes, are used in the analysis. The results show that the choice of discretization schemes has a significant influence on the results of wave elevation and resistance force in the case of the flow around the Wigley hull. In conclusion, second-order discretization schemes should be used for the resistance evaluation, in order to properly capture the non-linear effects.
Optical Surface Scanning for Patient Positioning in Radiation Therapy: A Prospective Analysis of 1902 Fractions
Authors: Carl G , Reitz D , Schönecker S , Pazos M , Freislederer P , Reiner M , Alongi F , Niyazi M , Ganswindt U , Belka C , Corradini S
Published: November 2018

Purpose/Objective:

Reproducible patient positioning remains one of the major challenges in modern radiation therapy. Recently, optical surface scanners have been introduced into clinical practice in addition to well-established positioning systems, such as room laser and skin marks. The aim of this prospective study was to evaluate setup errors of the optical surface scanner Catalyst HD (C-RAD AB) in different anatomic regions.

Material/Methods:

Between October 2016 and June 2017 a total of 1902 treatment sessions in 110 patients were evaluated. The workflow of this study included conventional setup procedures using laser-based positioning with skin marks and an additional registration of the 3-dimensional (3D) deviations detected by the Catalyst system. The deviations of the surface-based method were then compared to the corrections of cone beam computed tomography alignment which was considered as gold standard. A practical Catalyst setup error was calculated between the translational deviations of the surface scanner and the laser positioning. Two one-sided t tests for equivalence were used for statistical analysis.

Results:

Data analysis revealed total deviations of 0.09 mm ± 2.03 mm for the lateral axis, 0.07 mm ± 3.21 mm for the longitudinal axis, and 0.44 mm ± 3.08 mm vertical axis for the Catalyst system, compared to −0.06 ± 3.54 mm lateral, 0.53 ± 3.47 mm longitudinal, and 0.19 ± 3.49 mm vertical for the laser positioning compared to cone beam computed tomography. The lowest positional deviations were found in the cranial region, and larger deviations occurred in the thoracic and abdominal sites. A statistical comparison using 2 one-sided t tests showed a general concordance of the 2 methods ( P ≤ 0.036), excluding the vertical direction of the abdominal region ( P = 0.198).

Conclusion:

The optical surface scanner Catalyst HD is a reliable and feasible patient positioning system without any additional radiation exposure. From the head to the thoracic and abdominal region, a decrease in accuracy was observed within a comparable range for Catalyst and laser-assisted positioning.
Relationship Continuity/Discontinuity—A Framework for Investigating the Role of Relationships in the Experience of Living With Dementia
Authors: Riley Gerard A.
Published: November 2018

A recent paper (Riley, Evans and Oyebode, 2018) reported that a sense of discontinuity in carers’ experience of their spousal relationship with a person with dementia is associated with a greater sense of burden and fewer rewards from the caregiving role. This commentary sets the paper in context by providing a brief overview of research relating to relationship continuity/discontinuity. Factors that may explain why some carers experience continuity but others discontinuity are reviewed. So, too, is the potential impact of continuity/discontinuity on how the carer copes with the challenges of dementia. Possible future research is discussed.
Giant Lymphatic Malformations in Lower Extremity in an Adult: A Case Report
Authors: Seo Bommie Florence , Kang Kyo Joon , Choi Hyuk Joon , Seo Kyung Jin , Jung Sung-No , Shin Jongweon
Published: November 2018

Lymphatic malformations are benign vascular malformations rarely seen in adults. There have been only around 100 cases reported in the literature, most of them located in the head and neck area. The etiology is poorly understood but trauma, infection, or tumors have been suggested as possible causes. We present an unusual case of lymphatic malformations in the lower extremity of a 56-year-old male patient after minor trauma. The masses were asymptomatic but progressively enlarged for a period of 2 months, developing into a mass with a 21-cm-long axis in computed tomography images. Total surgical resection was performed and there were no complications or recurrence during the 1-year follow-up period. We present this rare case with a review of relevant previous literatures.
Review of Generalized Linear Models and Extensions by Hardin and Hilbe
Authors: Newson Roger
Published: November 2018

The new book Hardin and Hilbe (2001) is reviewed.
Residual Diagnostics for Cross-section Time Series Regression Models
Authors: Baum Christopher F.
Published: November 2018

These routines support the diagnosis of groupwise heteroskedasticity and cross-sectional correlation in the context of a regression model fit to pooled cross-section time series (xt) data.
Sort a List of Items
Authors: Royston Patrick
Published: November 2018

The command listsort for sorting the contents of a set of local macros is introduced and illustrated.
FIML Estimation of an Endogenous Switching Model for Count Data
Authors: Miranda Alfonso
Published: November 2018

This paper presents code for fitting a fiml endogenous switching poisson count model for cross-sectional data in stata 7: the espoisson command. the poisson process depends on an unobserved heterogeneity term, ξ; a set of explanatory variables, x; and an endogenous dummy, d. the endogenous dummy depends on an unobserved random term, ν. correlation between ξ and ν is allowed. if a model with exogenous d is fitted instead, correlation between ξ and ν will result in simultaneous equation bias. the endogenous switching model corrects this problem. after describing the underlying econometric theory behind the command, an example is discussed.
From the Help Desk: Kaplan–Meier Plots with Stsatrisk
Authors: Linhart Jean Marie , Pitblado Jeffrey S. , Hassell James
Published: November 2018

Stsatrisk is a wrapper for sts graph that adds a table to a survival plot with at-risk information, making it easy to create graphs that follow the list of recommendations given by Pocock et al. (2002) for Kaplan–Meier plots. We use stsatrisk to create plots in the desired format with the desired information.
Flexible Parametric Alternatives to the Cox Model: Update
Authors: Royston Patrick
Published: November 2018

Royston (2001)and Royston and Parmar (2002) introduced flexible parametric models for survival analysis, implemented in Stata through the ado-file stpm (Royston 2001). In the present article, stpm is updated to Stata 8.1 and has been shown to work correctly with Stata 8.2. To increase the reliability of the estimation procedure, the basis functions of the splines used to approximate the baseline distribution function have been orthogonalized.
Speaking Stata: How to Move Step By: Step
Authors: Cox Nicholas J.
Published: November 2018

The by varlist: construct is reviewed, showing how it can be used to tackle a variety of problems with group structure. These range from simply doing some calculations for each of several groups of observations to doing more advanced manipulations making use of the fact that with this construct, subscripts and the built-ins _n and _N are all interpreted within groups. A fairly complete tutorial on numerical evaluation of true and false conditions is included.
Bootstrapping a Conditional Moments Test for Normality after Tobit Estimation
Authors: Drukker David M.
Published: November 2018

Categorical and limited dependent variable models are routinely estimated via maximum likelihood. It is well-known that the ML estimates of the parameters are inconsistent if the distribution or the skedastic component is misspecified. When conditional moment tests were first developed by Newey (1985) and Tauchen (1985), they appeared to offer a wide range of easy-to-compute specification tests for categorical and limited dependent variable models estimated by maximum likelihood. However, subsequent studies found that using the asymptotic critical values produced severe size distortions. This paper presents simulation evidence that the standard conditional moment test for normality after tobit estimation has essentially no size distortion and reasonable power when the critical values are obtained via a parametric bootstrap.
strbee: Randomization-based Efficacy Estimator
Authors: White Ian R. , Walker Sarah , Babiker Abdel
Published: November 2018

strbee analyzes a two-group clinical trial with a survival outcome, in which some subjects may “crossover” to receive the treatment of the other arm. Adjustment for treatment crossover is done by a randomization-respecting method that preserves the intention-to-treat p-value.
From the Help Desk: It's all about the Sampling
Authors: McDowell Allen , Pitblado Jeff
Published: November 2018

Effective estimation and inference, when the data are collected using complex survey designs, requires estimators that fully account for the sampling design. This article explores, by means of Monte Carlo simulations of the power of simple hypothesis tests, the consequences of parameter estimation and inference when naive estimators are employed with survey data.
Least Likely Observations in Regression Models for Categorical Outcomes
Authors: Freese Jeremy
Published: November 2018

This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given the values of the independent variables and the parameter estimates and, like observations with large residuals in ordinary least squares regression, may warrant individual inspection. Use of the program is illustrated with examples using binary and ordered logistic regression.
From the Help Desk: Comparing Areas under Receiver Operating Characteristic Curves from Two or more Probit or Logit Models
Authors: Cleves Mario A.
Published: November 2018

Occasionally, there is a need to compare the predictive accuracy of several fitted logit (logistic) or probit models by comparing the areas under the corresponding receiver operating characteristic (ROC) curves. Although Stata currently does not have a ready routine for comparing two or more ROC areas generated from these models, this article describes how these comparisons can be performed using Stata's roccomp command.
Programmable GLM: Two User-defined Links
Authors: Guan Weihua , Gutierrez Roberto G.
Published: November 2018

With the release of Stata 7, the glm command for fitting generalized linear models underwent a substantial overhaul. Stata 7 glm contains an expanded array of variance estimators, regression diagnostics, and other enhancements. The overhaul took place to coincide with the release of Hardin and Hilbe (2001). With the new glm came a modular design that enables users to program customized link functions, variance functions, and weight functions to be used if Newey–West covariance estimates are desired. Because cases requiring customized link functions are the more prevalent in the literature, only those are considered here. We give two examples where a nonstandard link function is required: the relative survival model of Hakulinen and Tenkanen (1987)and a logistic model that accounts for natural response as described in Collett (2003). The relative ease (over previous versions of Stata) with which these alternate links can be programmed into glm is demonstrated.
Review of an Introduction to Survival Analysis Using Stata
Authors: Hosmer David W.
Published: November 2018

The new book by Cleves et al. (2002) is reviewed.
Intra-class Correlation in Random-effects Models for Binary Data
Authors: Rodríguez Germán , Elo Irma
Published: November 2018

We review the concept of intra-class correlation in random-effects models for binary outcomes as estimated by Stata's xtprobit, xtlogit, and xtclog. We consider the usual measures of correlation based on a latent variable formulation of these models and note corrections to the last two procedures. We also discuss alternative measures of association based on manifest variables or actual outcomes and introduce a new command xtrho for computing these measures for all three types of models.
From the Help Desk: Bootstrapped Standard Errors
Authors: Guan Weihua
Published: November 2018

Bootstrapping is a nonparametric approach for evaluating the dis-tribution of a statistic based on random resampling. This article illustrates the bootstrap as an alternative method for estimating the standard errors when the theoretical calculation is complicated or not available in the current software.
Exploring the Use of Variable Bandwidth Kernel Density Estimators
Authors: Salgado-Ugarte Isaías H. , Pérez-Hernández Marco A.
Published: November 2018

Variable bandwidth kernel density estimators increase the window width at low densities and decrease it where data concentrate. This represents an improvement over the fixed bandwidth kernel density estimators. In this article, we explore the use of one implementation of a variable kernel estimator in conjunction with several rules and procedures for bandwidth selection applied to several real datasets. The considered examples permit us to state that when working with tens or a few hundreds of data observations, least-squares cross-validation bandwidth rarely produces useful estimates; with thousands of observations, this problem can be surpassed. Optimal bandwidth and biased cross-validation (BCV), in general, oversmooth multimodal densities. The Sheather–Jones plug-in rule pro-duced bandwidths that behave slightly better in this respect. The Silverman test is considered as a very sophisticated and safe procedure to estimate the number of modes in univariate distributions; however, similar results could be obtained with the Sheather–Jones rule, but at a much lower computational cost. As expected, the variable bandwidth kernel density estimates showed fewer modes than those chosen by the Silverman test, especially those distributions in which multimodality was caused by several noisy minor modes. More research on the subject is needed.
From the Help Desk: Hurdle Models
Authors: McDowell Allen
Published: November 2018

This article demonstrates that, although there is no command in Stata for fitting hurdle models, the parameters of a hurdle model can be estimated in Stata rather easily using a combination of existing commands. We also include a likelihood evaluator to be used with Stata's ml facilities to illustrate how to fit a hurdle model using ml's cluster(), svy, and constraints() options.
Review of Generalized Estimating Equations by Hardin and Hilbe
Authors: Stillman Steven
Published: November 2018

The new book by Hardin and Hilbe (2003) is reviewed.
Confidence Intervals and p-values for Delivery to the End User
Authors: Newson Roger
Published: November 2018

Statisticians make their living producing confidence intervals and p-values. However, those in the Stata log are not ready for delivery to the end user, who usually wants to see statistical output either as a plot or as a table. This article describes a suite of programs used to convert Stata results to one or other of these forms. The eclplot package creates plots of estimates with confidence intervals, and the listtex package outputs a Stata dataset in the form of table rows that can be inserted into a plain TEX, LATEX, HTML, or word processor table. To create a Stata dataset that can be output in these ways, we can use the parmest, dsconcat, and lincomest packages to create datasets with one observation per estimated parameter; the sencode, tostring, ingap, and reshape packages to process these datasets into a form ready to be output; and the descsave and factext packages to reconstruct, in the output dataset, categorical predictor variables represented by dummy variables in regression models.
Submenu and Dialogs for Meta-analysis Commands
Authors: Steichen Thomas J.
Published: November 2018

The metadialog package provides Stata dialog boxes for the publicly available meta-analysis commands. It includes the commands needed to create a Meta-Analysis submenu on the StataCorp-defined User menu.
Funnel Plots in Meta-analysis
Authors: Sterne Jonathan A. C. , Harbord Roger M.
Published: November 2018

Funnel plots are a visual tool for investigating publication and other bias in meta-analysis. They are simple scatterplots of the treatment effects estimated from individual studies (horizontal axis) against a measure of study size (vertical axis). The name “funnel plot” is based on the precision in the estimation of the underlying treatment effect increasing as the sample size of component studies increases. Therefore, in the absence of bias, results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies. Publication bias (the association of publication probability with the statistical significance of study results) may lead to asymmetrical funnel plots. It is, however, important to realize that publication bias is only one of a number of possible causes of funnel-plot asymmetry-funnel plots should be seen as a generic means of examining small study effects (the tendency for the smaller studies in a meta-analysis to show larger treatment effects) rather than a tool to diagnose specific types of bias. This article introduces the metafunnel command, which produces funnel plots in Stata. In accordance with published recommendations, standard error is used as the measure of study size. Treatment effects expressed as ratio measures (for example risk ratios or odds ratios) may be plotted on a log scale.
Sample Size and Power Calculations using the Noncentral t-distribution
Authors: Harrison David A. , Brady Anthony R.
Published: November 2018

The standard formulas for sample size and power calculation, as implemented in the command sampsi, make use of a normal approximation to the t-distribution. When the sample sizes are small, this approximation is poor, resulting in overestimating power (or underestimating sample size). One particular situation in which this is likely to be important is the field of cluster randomized trials. Although the total number of individuals in a cluster randomized trial may be large, the number of clusters will often be small. We present a simulation study from the design of a cluster randomized crossover trial that motivated this work and a command to perform more accurate sample size and power calculations based on the noncentral t-distribution.
Review of Statistics with Stata (Updated for Version 8) by Hamilton
Authors: Williams Richard
Published: November 2018

The new book by Hamilton (2004) is reviewed.
Compliance-adjusted Intervention Effects in Survival Data
Authors: Kim Lois G. , White Ian R.
Published: November 2018

Survival data are most frequently analyzed by the intention-to-treat principle. However, presenting a compliance-adjusted analysis alongside the primary analysis can provide an insight into the effect of the treatment for those individuals actually complying with their randomized intervention. There are a number of methods for this type of analysis. Loeys and Goetghebeur (2003) use proportional hazards techniques to provide an estimate of the treatment effect for compliers when compliance is measured on an all-or-nothing scale. This methodology is here made available through a new Stata command, stcomply.
Analysis of Matched Cohort Data
Authors: Cummings Peter , McKnight Barbara
Published: November 2018

Matching is occasionally used in cohort studies; examples include studies of twins and some studies of traffic crashes. Analysis of matched cohort data is not discussed in many textbooks or articles and is not mentioned in the Stata manuals. Risk ratios can be estimated using matched-pair cohort data with Stata's mcc command. We describe a new command, csmatch, which can produce these risk ratios and is often more convenient. We briefly review flexible regression methods that can estimate risk ratios in matched cohort data: conditional Poisson regression and some versions of Cox regression.
From the Help Desk: Some Bootstrapping Techniques
Authors: Poi Brian P.
Published: November 2018

Bootstrapping techniques have become increasingly popular in applied econometrics and other areas. This article presents several methods and shows how to implement them using Stata's bootstrap command.
Review of Statistical Evaluation of Measurement Errors by Dunn
Authors: Cox Nicholas J.
Published: November 2018

The new edition of the book by Dunn (2004) is reviewed.
Further Processing of Estimation Results: Basic Programming with Matrices
Authors: Watson Ian
Published: November 2018

Rather than process estimation results in other applications, such as spreadsheets, this article shows how easy it is to process them inside Stata by undertaking some basic programming with matrices. Spreadsheet visualization helps define the task, but the steps are all core Stata: macros, loops, and matrices. The programming challenge is only a modest one for novices, while the benefits of converting do-files into ado programs can be considerable.
Multilingual Datasets
Authors: Weesie Jeroen
Published: November 2018

This insert describes a new command mlanguage that facilitates the creation and maintenance of “comprehensive” multilingual datasets. These are datasets with many variables, many of which are value labeled, with labels in different languages, all contained within the dataset. The tools make it easy to add labels in a new language by translating an existing set of labels, to switch between the sets of labels, to verify the integrity of such labels, and to assist in keeping the labels complete.
Data Inspection using Biplots
Authors: Kohler Ulrich , Luniak Magdalena
Published: November 2018

Biplots display interunit distances, as well as variances and correlations of variables of large datasets. They can be used as a tool to reveal clustering, multicollinearity, and multivariate outliers, and to guide the interpretation of principal component analyses (PCA). This article describes the uses of biplots and its implementation in Stata.
Speaking Stata: Density Probability Plots
Authors: Cox Nicholas J.
Published: November 2018

Density probability plots show two guesses at the density function of a continuous variable, given a data sample. The first guess is the density function of a specified distribution (e.g., normal, exponential, gamma, etc.) with appropriate parameter values plugged in. The second guess is the same density function evaluated at quantiles corresponding to plotting positions associated with the sample's order statistics. If the specified distribution fits well, the two guesses will be close. Such plots, suggested by Jones and Daly in 1995, are explained and discussed with examples from simulated and real data. Comparisons are made with histograms, kernel density estimation, and quantile–quantile plots.
Estimation of Marginal Effects using Margeff
Authors: Bartus Tamás
Published: November 2018

This article describes the user-written program margeff, which enables the fast estimation of (average) marginal effects. Besides describing the program, this article offers a new discussion of some problems that are related to computation of marginal effects. I will argue that (1) marginal effects computed at means are not good approximations of average marginal effects, computed as means of marginal effects evaluated at each observations, if some of the parameter estimates are large; (2) both average marginal effects and marginal effects computed at means might produce wrong estimates for dummies that are part of a set of indicator variables indicating different categories of a single underlying variable; and (3) the use of marginal effects computed at means is preferred if some of the regressors are mathematical transformations of other regressors.
Boosted Regression (Boosting): An Introductory Tutorial and a Stata Plugin
Authors: Schonlau Matthias
Published: November 2018

Boosting, or boosted regression, is a recent data-mining technique that has shown considerable success in predictive accuracy. This article gives an overview of boosting and introduces a new Stata command, boost, that implements the boosting algorithm described in Hastie, Tibshirani, and Friedman (2001, 322). The plugin is illustrated with a Gaussian and a logistic regression example. In the Gaussian regression example, the R2 value computed on a test dataset is R2 = 21.3% for linear regression and R2 = 93.8% for boosting. In the logistic regression example, stepwise logistic regression correctly classifies 54.1% of the observations in a test dataset versus 76.0% for boosted logistic regression. Currently, boost accommodates Gaussian (normal), logistic, and Poisson boosted regression. boost is implemented as a Windows C++ plugin.
Using Density-distribution Sunflower Plots to Explore Bivariate Relationships in Dense Data
Authors: Dupont William D. , Plummer W. Dale
Published: November 2018

Density-distribution sunflower plots are used to display high-density bivariate data. They are useful for data where a conventional scatterplot is difficult to read due to overstriking of the plot symbol. The x–y plane is subdivided into a lattice of small, regular, hexagonal bins. These bins are divided into low-, medium-, and high-density groups. In low-density bins, the individual observations are plotted as in a conventional scatterplot. Medium- and high-density bins contain light and dark sunflowers, respectively. In a light sunflower, each petal represents one observation. In a dark sunflower, each petal represents a specific number of observations. The user can control the sizes and colors of the sunflowers. By selecting appropriate colors and sizes for the light and dark sunflowers, plots can be obtained that give both the overall sense of the data-density distribution, as well as the number of data points in any given region.

Sunflower plots are also contrasted with contour plots of bivariate kernel-density estimates. The appearance of these plots is markedly affected by the choice of smoothing parameters and the spacing of points at which the probability density function is evaluated. Sunflower plots can be helpful in guiding the selection of these parameters and in distinguishing between chance and systematic variation in the distribution of bivariate data.
Stings in the Tails: Detecting and Dealing with Censored Data
Authors: Conroy Ronán M.
Published: November 2018

Variables often show evidence of clustering at extreme values and of graininess, that is, of a limited number of distinct values. Scores on two subscales of a quality-of-life measure, traditionally analyzed with OLS regression or ANOVA models, provide examples. Ignoring or failing to detect such features of the data will result in poor estimates of effect size.
Depending on Conditions: A Tutorial on the Cond() Function
Authors: Kantor David , Cox Nicholas J.
Published: November 2018

This is a tutorial on the cond() function, giving explanations and examples and assessing its advantages and limitations.
Review of Statistics for Epidemiology by Jewell
Authors: Bellocco Rino
Published: November 2018

The new book by Jewell (2004) is reviewed.
Confidence Intervals for Predicted Outcomes in Regression Models for Categorical Outcomes
Authors: Xu Jun , Long J. Scott
Published: November 2018

We discuss methods for computing confidence intervals for predictions and discrete changes in predictions for regression models for categorical outcomes. The methods include endpoint transformation, the delta method, and bootstrapping. We also describe an update to prvalue and prgen from the SPost package, which adds the ability to compute confidence intervals. The article provides several examples that illustrate the application of these methods.
Speaking Stata: Smoothing in Various Directions
Authors: Cox Nicholas J.
Published: November 2018

Identifying patterns in bivariate data on a scatterplot remains a basic statistical problem, with special flavor when both variables are on the same footing. Ideas of double, diagonal, and polar smoothing inspired by Cleveland and McGill's 1984 paper in the Journal of the American Statistical Association are revisited with various examples from environmental datasets. Double smoothing means smoothing both y given x and x given y. Diagonal smoothing means smoothing based on the sum and difference of y and x that treats the two variables symmetrically, possibly under standardization. Polar smoothing is based on the transformation from Cartesian to polar coordinates followed by smoothing and then reverse transformation; here the smoothing is implemented by regression on a series of sine and cosine terms. These methods thus offer exploratory tools for determining the broad structure of bivariate data.
Generalized Ordered Logit/Partial Proportional Odds Models for Ordinal Dependent Variables
Authors: Williams Richard
Published: November 2018

This article describes the gologit2 program for generalized ordered logit models. gologit2 is inspired by Vincent Fu's gologit routine (Stata Technical Bulletin Reprints 8: 160–164) and is backward compatible with it but offers several additional powerful options. A major strength of gologit2 is that it can fit three special cases of the generalized model: the proportional odds/parallel-lines model, the partial proportional odds model, and the logistic regression model. Hence, gologit2 can fit models that are less restrictive than the parallel-lines models fitted by ologit (whose assumptions are often violated) but more parsimonious and interpretable than those fitted by a nonordinal method, such as multinomial logistic regression (i.e., mlogit). Other key advantages of gologit2 include support for linear constraints, survey data estimation, and the computation of estimated probabilities via the predict command.
Review of Multilevel and Longitudinal Modeling Using Stata by Rabe-Hesketh and Skrondal
Authors: Wolfe Rory
Published: November 2018

This article reviews Multilevel and Longitudinal Modeling Using Stata, by Rabe-Hesketh and Skrondal.
Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation
Authors: Cappellari Lorenzo , Jenkins Stephen P.
Published: November 2018

We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: mdraws for deriving draws from the standard uniform density using either Halton or pseudorandom sequences, and an egen function, mvnp(), for calculating the probabilities themselves. Several illustrations show how the programs may be used for maximum simulated likelihood estimation.
Estimation of Multinomial Logit Models with Unobserved Heterogeneity using Maximum Simulated Likelihood
Authors: Haan Peter , Uhlendorff Arne
Published: November 2018

In this paper, we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold. First, we describe the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine with the Stata program gllamm, which solves integration by using Gauss–Hermite quadrature or adaptive quadrature. For the analysis, we draw on multilevel data about schooling. Our empirical findings show that the estimation techniques lead to approximately the same estimation results. The advantage of simulation over Gauss–Hermite quadrature is a marked reduction in computational time for integrals with higher dimensions. Adaptive quadrature leads to more stable results relative to the other integration methods. However, simulation is more time efficient. We find that maximum simulated likelihood leads to estimation results with reasonable accuracy in roughly half the time required when using adaptive quadrature.
Jackknife Instrumental Variables Estimation in Stata
Authors: Poi Brian P.
Published: November 2018

The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the endogenous variables and when many instruments are used. In this article, I discuss jackknife instrumental variables estimation as an alternative to 2sls. Monte Carlo simulations comparing the jackknife instrument variables estimators to 2sls and limited information maximum likelihood (liml) show that two of the four variants perform remarkably well even when 2sls does not. In a weak-instrument experiment, the two best performing jackknife estimators also outperform liml.
Difference-Based Semiparametric Estimation of Partial Linear Regression Models
Authors: Lokshin Michael
Published: November 2018

This article describes the plreg command, which implements the difference-based algorithm for fitting partial linear regression models.
Importing Federal Reserve Economic Data
Authors: Drukker David M.
Published: November 2018

This note describes freduse, which imports datasets from the Federal Reserve economic data (FRED®) repository.
Mata Matters: Interactive use
Authors: Gould William
Published: November 2018

Mata is Stata's matrix language. In the Mata Matters column, we show how Mata can be used interactively to solve problems and as a programming language to add new features to Stata. In this quarter's column, we look at interactive use of Mata.
Speaking Stata: Graphs for all Seasons
Authors: Cox Nicholas J.
Published: November 2018

Time series showing seasonality—marked variation with time of year—are of interest to many scientists, including climatologists, other environmental scientists, epidemiologists, and economists. The usual graphs plotting response variables against time, or even time of year, are not always the most effective at showing the fine structure of seasonality. I survey various modifications of the usual graphs and other kinds of graphs with a range of examples. Although I introduce here two new Stata commands, cycleplot and sliceplot, I emphasize exploiting standard functions, data management commands, and graph options to get the graphs desired.
Speaking Stata: In Praise of Trigonometric Predictors
Authors: Cox Nicholas J.
Published: November 2018

Using sine and cosine terms as predictors in modeling periodic time series and other kinds of periodic responses is a long-established technique, but it is often overlooked in many courses or textbooks. Such trigonometric regression is straightforward in Stata through applications of existing commands. I give various examples using classic periodic datasets on the motion of the asteroid Pallas and the daily rhythm of birth numbers. I make a brief connection to polynomial-trigonometric regression.
Improved Generalized Estimating Equation Analysis via xtqls for Quasi–Least Squares in Stata
Authors: Shults Justine , Ratcliffe Sarah J. , Leonard Mary
Published: November 2018

Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (gee) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of qls that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of qls: (1) qls allows some correlation structures that have not yet been implemented in the framework of gee, (2) qls can be applied as an alternative to gee if the gee estimate is infeasible, and (3) qls uses the same estimating equation for estimation of β as gee; as a result, qls can involve programs already available for gee. In particular, xtqls calls the Stata program xtgee within an iterative approach that alternates between updating estimates of the correlation parameter α and then using xtgee to solve the gee for β at the current estimate of α. The benefit of this approach is that after xtqls, all the usual postregression estimation commands are readily available to the user.