R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated development environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself.Keywords: GUI, integrated development environment, plugin, R. Background and motivationIn mid 1993 Ihaka and Gentleman published initial efforts on the computing language and programming environment R on the s-news mailing list. Ambitions for this project were to 2 RKWard: A Comprehensive GUI and IDE for R develop an S-like language without inheriting memory and performance issues. The source code of R was finally released in 1995, and since 1997 development has evolved under the umbrella of the R Development Core Team (R Development Core Team 2001, 2012aIhaka 1998). R does not include an advanced cross-platform graphical user interface (GUI) as known from other statistical software packages. However, R includes tools for building GUIs mainly based on Tcl/Tk (Dalgaard 2001(Dalgaard , 2002. Meanwhile a plethora of R GUIs have emerged (see Grosjean 2010, for a comprehensive list). In 2005 John Fox released version 1.0 of R Commander (Fox 2005, package Rcmdr), which can be considered a milestone in R GUI development; it was the first GUI implementation that was able to make statistical tests, plots and data manipulation easily accessible for R novices. John Fox stated that Rcmdr's target was to provide functionality for basic-statistical courses, though the features have increased over time beyond this (Fox 2005(Fox , 2007. In November 2002 Thomas Friedrichsmeier started the RKWard open-source software project with the goal to create a GUI for R based on KDE (KDE e.V. 2012) and Qt (Nokia Corporation 2012) technologies 1 .The scope of RKWard is deliberately broad, targeting both R novices and experts. For the first group, the aim is to allow any person with knowledge on statistical procedures to start using RKWard for their everyday work without having to learn anything about t...
The development of diagnostics to check the fit of a proposed Markov random field (MRF) to data is a very important problem in spatial statistics. In this article, the consequences of fitting a given MRF to spatial data are visualized using diagnostic plots. The Gaussian MRF known as the conditional autoregressive model is featured. Various types of departures of the data from the fitted MRF model are calculated, allowing locally influential observations to be highlighted using the MRF-Neighborhoods plot. Through a global summary statistic and the Model-Comparison plot, we compare MRF models that differ both in terms of the neighborhood structure and the parameterization of spatial dependence.
When crossing international gateways, commercial vehicles engage in multiple activities—such as approaching on congested roadways, paying tolls, undergoing customs inspections, and waiting in queues—that increase trip times and trip-time variability. Documenting the times incurred in these multiple activities is difficult because the activities are spatially dispersed, temporally variant, and affected by institutional and operational complexities resulting from the multiple organizations operating infrastructure in two countries. An approach is presented to capture the times required to complete various activities at international border crossings, using trucks from a large fleet that regularly crosses international gateways. This approach takes advantage of the telematics systems already in use by the truck fleet and is not dependent on installation of roadside equipment. Geofences are specified at strategic locations that delineate the beginnings and ends of activities of interest and send information electronically to the vehicles' onboard data units. The activity times for an individual truck crossing can be determined from data automatically recorded when trucks enter and exit the specified geofences. The trucks serve as activity time probes, and the activity times of multiple trucks can be aggregated to form activity time distributions. To demonstrate the capabilities and potential of the approach, empirical data from a large fleet of trucks traversing two of the busiest U.S. border crossings are used to produce results on median crossing times, variability in trip times, time-of-day patterns, and excess times (i.e., delays) generated from customs inspections and the queuing that results from the inspections.
Several anomalies in the foundations of ridge regression from the perspective of constrained least-square (LS) problems were pointed out in Jensen & Ramirez. Some of these so-called anomalies, attributed to the non-monotonic behaviour of the norm of unconstrained ridge estimators and the consequent lack of sufficiency of Lagrange's principle, are shown to be incorrect. It is noted in this paper that, for a fixed Y, norms of unconstrained ridge estimators corresponding to the given basis are indeed strictly monotone. Furthermore, the conditions for sufficiency of Lagrange's principle are valid for a suitable range of the constraint parameter. The discrepancy arose in the context of one data set due to confusion between estimates of the parameter vector, β , corresponding to different parametrization (choice of bases) and/or constraint norms. In order to avoid such confusion, it is suggested that the parameter β correspondi ng to each basis be labelled appropriately. Copyright (c) 2010 The Authors. Journal compilation (c) 2010 International Statistical Institute.
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