5,306 research outputs found
The pls Package: Principal Component and Partial Least Squares Regression in R
No full textThe pls package implements principal component regression (PCR) and partial least squares regression (PLSR) in R (R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network (CRAN), licensed under the GNU General Public License (GPL). The user interface is modelled after the traditional formula interface, as exemplified by lm. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a exible crossvalidation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.
CCA: An R Package to Extend Canonical Correlation Analysis
No full textCanonical correlations analysis (CCA) is an exploratory statistical method to highlight correlations between two data sets acquired on the same experimental units. The cancor() function in R (R Development Core Team 2007) performs the core of computations but further work was required to provide the user with additional tools to facilitate the interpretation of the results. We implemented an R package, CCA, freely available from the Comprehensive R Archive Network (CRAN, http://CRAN.R-project.org/), to develop numerical and graphical outputs and to enable the user to handle missing values. The CCA package also includes a regularized version of CCA to deal with data sets with more variables than units. Illustrations are given through the analysis of a data set coming from a nutrigenomic study in the mouse.
Team Vision in Product Development: how knowledge strategy matters?
No full textIn today´s more complex multinational and technologically sophisticated environment, the group has re-emerged in importance as the project team. Work teams are important to organizations in general, but are especially critical in product development because they span many functional areas including engineering, marketing, manufacturing, finance, etc, and new product teams must frequently be composed of individuals from different backgrounds and perspectives. In these circumstances, this paper addresses the contingency role that knowledge strategy plays in explaining the relationship between team vision and product development performance. After studying the team vision on 78 new product deveProduct development , Team vision, Knowledge strategy
Covariate Augmented Dickey-Fuller Tests with R
No full textThis paper describes CADFtest, a R (R Development Core Team 2008) package for testing for the presence of a unit root in a time series using the Covariate Augmented Dickey-Fuller (CADF) test proposed in Hansen (1995). The procedures presented here are user friendly, allow fully automatic model specification, and allow computation of the asymptotic p-values of the test.unit root, stationary covariates, asymptotic p-values, R.
State-of-the-Art in Parallel Computing with R
No full textR is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly useful for general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems four different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix
State of the Art in Parallel Computing with R
No full textR is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.
Learning Bayesian Networks with the bnlearn R Package
No full textbnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010).
Rcpp: Seamless R and C++ Integration
No full textThe Rcpp package simplifies integrating C++ code with R. It provides a consistent C++ class hierarchy that maps various types of R objects (vectors, matrices, functions, environments, . . . ) to dedicated C++ classes. Object interchange between R and C++ is managed by simple, flexible and extensible concepts which include broad support for C++ Standard Template Library idioms. C++ code can both be compiled, linked and loaded on the fly, or added via packages. Flexible error and exception code handling is provided. Rcpp substantially lowers the barrier for programmers wanting to combine C++ code with R.
An Open Source Approach for Modern Teaching Methods: The Interactive TGUI System
No full textIn order to facilitate teaching complex topics in an interactive way, the authors developed a computer-assisted teaching system, a graphical user interface named TGUI (Teaching Graphical User Interface). TGUI was introduced at the beginning of 2009 in the Austrian Journal of Statistics (Dinges and Templ 2009) as being an effective instrument to train and teach staff on mathematical and statistical topics. While the fundamental principles were retained, the current TGUI system has been undergone a complete redesign. The ultimate goal behind the reimplementation was to share the advantages of TGUI and provide teachers and people who need to hold training courses with a strong tool that can enrich their lectures with interactive features. The idea was to go a step beyond the current modular blended-learning systems (see, e.g., Da Rin 2003) or the related teaching techniques of classroom-voting (see, e.g., Cline 2006). In this paper the authors have attempted to exemplify basic idea and concept of TGUI by means of statistics seminars held at Statistics Austria. The powerful open source software R (R Development Core Team 2010a) is the backend for TGUI, which can therefore be used to process even complex statistical contents. However, with specifically created contents the interactive TGUI system can be used to support a wide range of courses and topics. The open source R packages TGUICore and TGUITeaching are freely available from the Comprehensive R Archive Network at http://CRAN.R-project.org/.
Variation in Experience and Team Familiarity: Addressing the Knowledge Acquisition-Application Problem
No full textPrior work in organizational learning has failed to find a consistent effect of variation in experience on performance. While some studies find a positive relationship between these two variables, others find no effect or even a negative relationship. In this paper, we suggest that the differences in prior findings may be due to the failure to separate the processes of knowledge acquisition and knowledge application. While variation in experience may permit the acquisition of valuable knowledge, additional mechanisms may be necessary to enable the subsequent application of that knowledge in a team setting. We hypothesize that team familiarity - prior experience working with team members - may be such a mechanism. We use detailed project- and individual-level data from an Indian software services firm to examine the effects of team familiarity and variation in market experience on multiple measures of performance for over 1,100 software development projects Consistent with prior work, we find mixed results for the effect of variation in experience on performance. We do, however, see evidence of a moderating effect of team familiarity on the relationship between these two variables. Our paper identifies one mechanism for uniting knowledge acquisition and knowledge application and provides insight into how the management of experience accumulation affects the development of organizational capabilities.Experience, Knowledge, Software, Team Familiarity, Variation
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