R J.最新文献

{"title":"pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions","authors":"Markus Fritsch, Adrian Yu Pua Andrew, Joachim Schnurbus","doi":"10.32614/rj-2021-035","DOIUrl":"https://doi.org/10.32614/rj-2021-035","url":null,"abstract":"This paper introduces pdynmc , an R package that provides users sufficient flexibility and precise control over the estimation and inference in linear dynamic panel data models. The package primarily allows for the inclusion of nonlinear moment conditions and the use of iterated GMM; additionally, visualizations for data structure and estimation results are provided. The current implementation reflects recent developments in literature, uses sensible argument defaults, and aligns commercial and noncommercial estimation commands. Since the understanding of the model assumptions is vital for setting up plausible estimation routines, we provide a broad introduction of linear dynamic panel data models directed towards practitioners before concisely describing the functionality available in pdynmc regarding instrument type, covariate type, estimation methodology, and general configuration. We then demonstrate the functionality by revisiting the popular firm-level dataset of Arellano and Bond (1991). ,","PeriodicalId":20974,"journal":{"name":"R J.","volume":"16 1","pages":"218"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87189898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates","authors":"M. Rodríguez-Álvarez, Vanda Inácio","doi":"10.32614/rj-2021-066","DOIUrl":"https://doi.org/10.32614/rj-2021-066","url":null,"abstract":"This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive fraction. For the Bayesian methods, we provide tools for assessing model fit via posterior predictive checks, while the model choice can be carried out via several information criteria. Numerical and graphical outputs are provided for all methods. This is the only package implementing Bayesian procedures for ROC curves.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"9 1","pages":"525"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72975867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PASSED: Calculate Power and Sample Size for Two Sample Tests","authors":"Jinpu Li, R. Knigge, Kaiyi Chen, E. Leary","doi":"10.32614/rj-2021-094","DOIUrl":"https://doi.org/10.32614/rj-2021-094","url":null,"abstract":"Power and sample size estimation are critical aspects in study design to demonstrate minimized risk for subjects and to justify the allocation of time, money, and other resources. Researchers often work with response variables which take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"45 1","pages":"450"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75943160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reproducible Summary Tables with the gtsummary Package","authors":"D. Sjoberg, Karissa A Whiting, Michael Curry, J. Lavery, J. Larmarange","doi":"10.32614/rj-2021-053","DOIUrl":"https://doi.org/10.32614/rj-2021-053","url":null,"abstract":"The gtsummary package provides an elegant and flexible way to create publication-ready summary tables in R. A critical part of the work of statisticians, data scientists, and analysts is summarizing data sets and regression models in R and publishing or sharing polished summary tables. The gtsummary package was created to streamline these everyday analysis tasks by allowing users to easily create reproducible summaries of data sets, regression models, survey data, and survival data with a simple interface and very little code. The package follows a tidy framework, making it easy to integrate in standard data workflows, and offers many table customization features through function arguments, helper functions, and custom themes.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"16 1","pages":"570"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75422442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DChaos: An R Package for Chaotic Time Series Analysis","authors":"Julio E. Sandubete, L. Escot","doi":"10.32614/rj-2021-036","DOIUrl":"https://doi.org/10.32614/rj-2021-036","url":null,"abstract":"Chaos theory has been hailed as a revolution of thoughts and attracting ever-increasing attention of many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory is the detection of chaotic behavior from empirical time-series data. One of the main features of chaos is the well-known initial-value sensitivity property. Methods and techniques related to testing the hypothesis of chaos try to quantify the initial-value sensitive property estimating the so-called Lyapunov exponents. This paper describes the main estimation methods of the Lyapunov exponent from time series data. At the same time, we present the DChaos library. R users may compute the delayed-coordinate embedding vector from time series data, estimates the best-fitted neural net model from the delayed-coordinate embedding vectors, calculates analytically the partial derivatives from the chosen neural nets model. They can also obtain the neural net estimator of the Lyapunov exponent from the partial derivatives computed previously by two different procedures and four ways of subsampling by blocks. To sum up, the DChaos package allows the R users to test robustly the hypothesis of chaos in order to know if the data-generating process behind time series behaves chaotically or not. The package’s functionality is illustrated by examples.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"30 1","pages":"232"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88762793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares","authors":"Boyoung Kim, Yunju Im, Keun Yoo Jae","doi":"10.32614/rj-2021-026","DOIUrl":"https://doi.org/10.32614/rj-2021-026","url":null,"abstract":"Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many science fields such as chemomtrics, pattern recognition, genomic sequence analysis and so on. The so-called seedCCA is a newly developed R package, and it implements not only the standard and seeded CCA but also partial least squares. The package enables us to fit CCA to large-p and small-n data. The paper provides a complete guide. Also, the seeded CCA application results are compared with the regularized CCA in the existing R package. It is believed that the package along with the paper will contribute to highdimensional data analysis in various science field practitioners and that the statistical methodologies in multivariate analysis become more fruitful. Introduction Explanatory studies are important to identify patterns and special structure in data prior to develop a specific model. When a study between two sets of a p-dimensional random variables X (X ∈ Rp) and a r-dimensional random variable Y (Y ∈ Rr), are of primary interest, one of the popular explanatory statistical methods would be canonical correlation analysis (CCA; Hotelling (1936)). The main goal of CCA is the dimension reduction of two sets of variables by measuring an association between the two sets. For this, pairs of linear combinations of variables are constructed by maximizing the Pearson correlation. The CCA has successful application in many science fields such as chemomtrics, pattern recognition, genomic sequence analysis and so on. In Lee and Yoo (2014) it is shown that the CCA can be used as a dimension reduction tool for high-dimensional data, but also it is connected to least square estimator. Therefore, the CCA is not only explanatory and dimension reduction method but also can be utilized as alternative of least square estimation. If max(p, r) is bigger than or equal to the sample size, n, usual CCA application is not plausible due to no incapability of inverting sample covariance matrices. To overcome this, a regularized CCA is developed by Leurgans et al. (1993), whose idea was firstly suggested in Vinod (1976). In practice, the CCA package by González et al. (2008) can implement a version of the regularized CCA. To make the sample covariance matrices saying Σ̂x and Σ̂y, invertible, in González et al. (2008), they are replaced with Σ̂ λ1 x = Σ̂x + λ1Ip and Σ̂ λ2 y = Σ̂y + λ1Ir. The optimal values of λ1 and λ2 are chosen by maximizing a cross-validation score throughout the two-dimensional grid search. Although it is discussed that a relatively small grid of reasonable values for λ1 and λ2 can lesson intensive computing in González et al. (2008), it is still time-consuming as observed in later sections. Additionally, fast regularized CCA and robust CCA via projection-pursuit are recently developed in Cruz-Cano (2012) and Alfons et al. (2016), respectively. Another version of CCA to handle max(p, ","PeriodicalId":20974,"journal":{"name":"R J.","volume":"44 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91542743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple Imputation and Synthetic Data Generation with NPBayesImputeCat","authors":"Jingchen Hu, O. Akande, Quanli Wang","doi":"10.32614/rj-2021-080","DOIUrl":"https://doi.org/10.32614/rj-2021-080","url":null,"abstract":"In many contexts, missing data and disclosure control are ubiquitous and challenging issues. In particular, at statistical agencies, the respondent-level data they collect from surveys and censuses can suffer from high rates of missingness. Furthermore, agencies are obliged to protect respondents’ privacy when publishing the collected data for public use. The NPBayesImputeCat R package, introduced in this paper, provides routines to i) create multiple imputations for missing data and ii) create synthetic data for statistical disclosure control, for multivariate categorical data, with or without structural zeros. We describe the Dirichlet process mixture of products of the multinomial distributions model used in the package and illustrate various uses of the package using data samples from the American Community Survey (ACS). We also compare results of the missing data imputation to the mice R package and those of the synthetic data generation to the synthpop R package. Introduction and background Multiple imputation for missing data Missing data problems arise in many statistical analyses. To impute missing values, multiple imputation, first proposed by Rubin (1987), has been widely adopted. This approach estimates predictive models based on the observed data, fills in missing values with draws from the predictive models, and produces multiple imputed and completed datasets. Data analysts then apply standard statistical analyses (e.g., regression analysis) on each imputed dataset and use appropriate combining rules to obtain valid point estimates and variance estimates (Rubin, 1987). As a brief review of the multiple imputation combining rules for missing data, let q be the completed data estimator of some estimand of interest Q, and let u be the estimator of the variance of q. For l = 1, . . . , m, let q(l) and u(l) be the values of q and u in the lth completed dataset. The multiple imputation estimate of Q is equal to q̄m = ∑l=1 q (l)/m, and the estimated variance associated with q̄m is equal to Tm = (1 + 1/m)bm + ūm , where bm = ∑l=1(q (l) − q̄m)/(m − 1) and ūm = ∑l=1 u (l)/m. Inferences for Q are based on (q̄m − Q) ∼ tv(0, Tm), where tv is a t-distribution with v = (m − 1)(1 + ūm/[(1 + 1/m)bm]) degrees of freedom. Multiple imputation by chained equations (MICE, Buuren and Groothuis-Oudshoorn (2011)) remains the most popular method for generating multiple completed datasets after multiple imputation. Under MICE, one specifies univariate conditional models separately for each variable, usually using generalized linear models (GLMs) or classification and regression trees (CART Breiman et al. (1984); Burgette and Reiter (2010)), and then iteratively samples plausible predicted values from the sequence of conditional models . For implementing MICE in R, most analysts use the mice package. For an in-depth review of the MICE algorithm, see Buuren and Groothuis-Oudshoorn (2011). For more details and reviews, see Rubin (1996), Harel and Zhou (2007), R","PeriodicalId":20974,"journal":{"name":"R J.","volume":"24 1","pages":"25"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84673342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RobustBF: An R Package for Robust Solution to the Behrens-Fisher Problem","authors":"Gamze Güven, S. Acitas, Hatice Samkar, B. Şenoğlu","doi":"10.32614/rj-2021-107","DOIUrl":"https://doi.org/10.32614/rj-2021-107","url":null,"abstract":"Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates and powers of the proposed tests are also given and compared with Welch’s t-test based on LS estimators via an extensive Monte Carlo simulation study.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"57 1","pages":"642"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81339370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BayesSenMC: an R package for Bayesian Sensitivity Analysis of Misclassification","authors":"Jinhui Yang, Lifeng Lin, H. Chu","doi":"10.32614/rj-2021-097","DOIUrl":"https://doi.org/10.32614/rj-2021-097","url":null,"abstract":"In case–control studies, the odds ratio is commonly used to summarize the association between a binary exposure and a dichotomous outcome. However, exposure misclassification frequently appears in case–control studies due to inaccurate data reporting, which can produce bias in measures of association. In this article, we implement a Bayesian sensitivity analysis of misclassification to provide a full posterior inference on the corrected odds ratio under both non-differential and differential misclassification. We present an R (R Core Team, 2018) package BayesSenMC, which provides user-friendly functions for its implementation. The usage is illustrated by a real data analysis on the association between bipolar disorder and rheumatoid arthritis.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"15 1","pages":"123"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83607910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IndexNumber: An R Package for Measuring the Evolution of Magnitudes","authors":"A. Saavedra-Nieves, P. Saavedra-Nieves","doi":"10.32614/rj-2021-038","DOIUrl":"https://doi.org/10.32614/rj-2021-038","url":null,"abstract":"Index numbers are descriptive statistical measures useful in economic settings for comparing simple and complex magnitudes registered, usually in two time periods. Although this theory has a large history, it still plays an important role in modern today’s societies where big amounts of economic data are available and need to be analyzed. After a detailed revision on classical index numbers in literature, this paper is focused on the description of the R package IndexNumber with strong capabilities for calculating them. Two of the four real data sets contained in this library are used for illustrating the determination of the index numbers in this work. Graphical tools are also implemented in order to show the time evolution of considered magnitudes simplifying the interpretation of the results.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"31 1","pages":"253"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90080497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}