1,185,748 research outputs found
Ego-networks analysis with R (IV) : multilevel analysis
Get PDFEste texto es el cuarto y último de la serie que constituye un taller sobre análisis de ego-redes (y/o redes personales) con R. El texto está acompañado por ejemplos de datos y los scripts de lenguaje R necesarios para realizar las actividades propuestas.This text is the fourth and last in the series that constitutes a workshop on ego-network (and/or personal network) analysis with R. The text is accompanied by example data and the R language scripts needed to perform the proposed activities
Análisis de redes egocéntricas con R (III) : Egoredes múltiples
Get PDFEste texto es el tercero de una serie de cuatro que conjuntamente constituyen un taller sobre análisis de ego-redes (y/o redes personales) con R. El texto está acompañado por ficheros de datos y los scripts de lenguaje R necesarios para realizar las actividades propuestas.This text is the third in a series of four that together constitute a workshop on ego-network (and/or personal network) analysis with R. The text is accompanied by data files and the R language scripts needed to perform the proposed activities
The NoiseFiltersR Package: Label Noise Preprocessing in R
Get PDFIn Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms used for the implementation of the label noise filters are appropriately documented and referenced. They can be called in a R-user-friendly manner, and their results are unified by means of the "filter" class, which also benefits from adapted print and summary methods.Univ Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, SpainUniv Sao Paulo, Inst Ciencias Matemat & Comp, Trabalhador Sao Carlense Av 400, BR-13560970 Sao Carlos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilWeb of ScienceSpanish Research ProjectAndalusian Research PlanBrazilian grant-CeMEAI-FAPESPFAPESPSpanish Research Project: TIN2014-57251-PAndalusian Research Plan: P11-TIC-7765CeMEAI-FAPESP: 2013/07375-0FAPESP: 2012/22608-8FAPESP: 2011/14602-
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
Get PDFBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Illustrations of serial mediation using PROCESS, Mplus and R
Get PDFThere has been an increased interest among researchers in the behavourial and social sciences for mediation models. This interest is well deserved: mediation can explain via intermediate variables the relationship between an independent variable and a dependent variable. Many software programs are now available to perform such analysis. However, there is a lack of articles to guide users to perform more complex models. The purpose of the current manuscript is to provide a tutorial on serial mediation analysis using software requiring less programming skills like SPSS (PROCESS), and Mplus to more advanced software such as R. In this manuscript, we first introduce the simple mediation analysis. Second, we explain the different parameters and effects of a serial mediation analysis with two mediators. Third, we show how to generate data using R. Fourth, we explain the input and output of PROCESS, Mplus, and R. Finally, a practical example is performed with Mplus
Numerical Ecology with R
No full textInternational audienceNumerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site (http://www.springer.com/978-1-4419-7975-9). This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment
asteRisk - Integration and Analysis of Satellite Positional Data in R
No full textOver the past few years, the amount of artificial satellites orbiting Earth has grown fast,
with close to a thousand new launches per year. Reliable calculation of the evolution of the satellites’
position over time is required in order to efficiently plan the launch and operation of such satellites,
as well as to avoid collisions that could lead to considerable losses and generation of harmful space
debris. Here, we present asteRisk, the first R package for analysis of the trajectory of satellites. The
package provides native implementations of different methods to calculate the orbit of satellites, as
well as tools for importing standard file formats typically used to store satellite position data and to
convert satellite coordinates between different frames of reference. Such functionalities provide the
foundation for integrating orbital data and astrodynamics analysis in R
Learning from R&D outsourcing vs. learning by R&D outsourcing
Get PDFWe analyze how research and development (R&D) outsourcing influences product innovation. We propose a separation between learning from R&D outsourcing, whereby the firm improves its ability to innovate by using outsourced R&D directly in new products, from learning by R&D outsourcing, whereby the firm indirectly uses outsourced R&D by integrating it with internal R&D to create new products. Building on the knowledge-based view, we argue that learning from R&D outsourcing is likely to have an inverse U-shaped relationship with product innovation, because the initial benefits of using outsourced component R&D knowledge to innovate products is eventually outweighed by the hollowing out of the firm's ability to innovate. In contrast, we propose that learning by R&D outsourcing is likely to have a U-shaped relationship with product innovation, because the initial challenges of integrating internal and external R&D are eventually overcome, resulting in more innovations. Finally, we distinguish between domestic and foreign R&D outsourcing and propose a liability of foreignness in R&D outsourcing as it has a lower impact on new products than domestic R&D outsourcing. The empirical analysis shows that outsourced R&D has an inverted U-shaped relationship with the number of new products, while the interaction between outsourced R&D and internal R&D has a U-shaped relationship with the number of new products. It also shows that domestic outsourced R&D has a higher positive impact on the number of new products than foreign outsourced R&D.Ministry of Economy and Competitiveness of Spain (Grant: ECO2015-67296-R, MINECO/FEDER, UE), the Community of Madrid and the European Social Fund (S2015/HUM-3417 and INNCOMCON-CM) for their support of this project. Finally, for financial support, Un thanks the D'Amore-McKim School of Business of Northeastern University for the Strategic Summer Research Award, and Rodriguez thanks the Ramon Areces Foundation CISP15A3196
An Interview with Cass R. Sunstein: Author of The World According to Star Wars
No full textThe guest editors of special issue 12, Jason W. Ellis and Sean Scanlan, interview Cass R. Sunstein, the Robert Walmsley University Professor at Harvard, where he is founder and director of the Program on Behavioral Economics and Public Policy. He is the author of many books, including the bestseller Nudge: Improving Decisions about Health, Wealth, and Happiness (with Richard H. Thaler). His 2016 book The World According to Star Wars attempts to understand the Star Wars universe in ten chapters through the lenses of Sunstein’s academic interests, namely: culture, sociology, psychology, behavioral science, and political science. The book is both personal and theoretical, practical and academic. It takes accurate measure of the genesis of the movies, the movies themselves, and briefly, but trenchantly, it examines concepts such as reputational cascades and speculates on what Star Wars can teach viewers about constitutional disputes
Using SQL for data consolidation in R
Get PDFWorking with multiple data sources implies data cleaning and consolidation prior to analysis. R has become popular among social scientists (Kelley, 2007; Clark, 2014), who are advised to screen data in a “favorite spreadsheet program” (Muenchen, 2011:21), before importing it to R. This way, users avoid typing in the R console and are supported by a graphical user interface. Even for experienced R users, querying/ retrieving data from multiple large sources takes a lot of computing power, which is better handled by SQL language (Table 2; KeyCentrix, 2015).Sociedad Argentina de Informática e Investigación Operativ
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