Exploring Habitat Selection by Wildlife with adehabitat

Knowledge of the environmental features affecting habitat selection by animals is important for designing wildlife management and conservation policies. The package adehabitat for the R software is designed to provide a computing environment for the analysis and modelling of such relationships. This paper focuses on the preliminary steps of data exploration and analysis, performed prior to a more formal modelling of habitat selection. In this context, I illustrate the use of a factorial analysis, the K-select analysis. This method is a factorial decomposition of marginality, one measure of habitat selection. This method was chosen to present the package because it illustrates clearly many of its features (home range estimation, spatial analyses, graphical possibilities, etc.). I strongly stress the powerful capabilities of factorial methods for data analysis, using as an example the analysis of habitat selection by the wild boar (Sus scrofa L.) in a Mediterranean environment.

Exploring Habitat Selection by Wildlife with adehabitat

Knowledge of the environmental features affecting habitat selection by animals is important for designing wildlife management and conservation policies. The package adehabitat for the R software is designed to provide a computing environment for the analysis and modelling of such relationships. This paper focuses on the preliminary steps of data exploration and analysis, performed prior to a more formal modelling of habitat selection. In this context, I illustrate the use of a factorial analysis, the K-select analysis. This method is a factorial decomposition of marginality, one measure of habitat selection. This method was chosen to present the package because it illustrates clearly many of its features (home range estimation, spatial analyses, graphical possibilities, etc.). I strongly stress the powerful capabilities of factorial methods for data analysis, using as an example the analysis of habitat selection by the wild boar (Sus scrofa L.) in a Mediterranean environment

Optimal utilization of historical data sets for the construction of software cost prediction models

The accurate prediction of software development cost at early stage of development life-cycle may have a vital economic impact and provide fundamental information for management decision making. However, it is not well understood in practice how to optimally utilize historical software project data for the construction of cost predictions. This is because the analysis of historical data sets for software cost estimation leads to many practical difficulties. In addition, there has been little research done to prove the benefits. To overcome these limitations, this research proposes a preliminary data analysis framework, which is an extension of Maxwell's study. The proposed framework is based on a set of statistical analysis methods such as correlation analysis, stepwise ANOVA, univariate analysis, etc. and provides a formal basis for the erection of cost prediction models from his¬torical data sets. The proposed framework is empirically evaluated against commonly used prediction methods, namely Ordinary Least-Square Regression (OLS), Robust Regression (RR), Classification and Regression Trees (CART), K-Nearest Neighbour (KNN), and is also applied to both heterogeneous and homogeneous data sets. Formal statistical significance testing was performed for the comparisons. The results from the comparative evaluation suggest that the proposed preliminary data analysis framework is capable to construct more accurate prediction models for all selected prediction techniques. The framework processed predictor variables are statistic significant, at 95% confidence level for both parametric techniques (OLS and RR) and one non-parametric technique (CART). Both the heterogeneous data set and homogenous data set benefit from the application of the proposed framework for improving project effort prediction accuracy. The homogeneous data set is more effective after being processed by the framework. Overall, the evaluation results demonstrate that the proposed framework has an excellent applicability. Further research could focus on two main purposes: First, improve the applicability by integrating missing data techniques such as listwise deletion (LD), mean imputation (MI), etc., for handling missing values in historical data sets. Second, apply benchmarking to enable comparisons, i.e. allowing companies to compare themselves with respect to their productivity or quality

nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

Nonparametric kernel density and local polynomial regression estimators are very popular in statistics, economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ingredient entering some other estimation or inference procedure. This article describes the main methodological and numerical features of the software package nprobust, which offers an array of estimation and inference procedures for nonparametric kernel-based density and local polynomial regression methods, implemented in both the R and Stata statistical platforms. The package includes not only classical bandwidth selection, estimation, and inference methods (Wand and Jones 1995; Fan and Gijbels 1996), but also other recent developments in the statistics and econometrics literatures such as robust bias-corrected inference and coverage error optimal bandwidth selection (Calonico, Cattaneo, and Farrell 2018, 2019a). Furthermore, this article also proposes a simple way of estimating optimal bandwidths in practice that always delivers the optimal mean square error convergence rate regardless of the specific evaluation point, that is, no matter whether it is implemented at a boundary or interior point. Numerical performance is illustrated using an empirical application and simulated data, where a detailed numerical comparison with other R packages is given

nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference

Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ingredient entering some other estimation or inference procedure. This article describes the main methodological and numerical features of the software package nprobust, which offers an array of estimation and inference procedures for nonparametric kernel-based density and local polynomial regression methods, implemented in both the R and Stata statistical platforms. The package includes not only classical bandwidth selection, estimation, and inference methods (Wand and Jones, 1995; Fan and Gijbels, 1996), but also other recent developments in the statistics and econometrics literatures such as robust bias-corrected inference and coverage error optimal bandwidth selection (Calonico, Cattaneo and Farrell, 2018, 2019). Furthermore, this article also proposes a simple way of estimating optimal bandwidths in practice that always delivers the optimal mean square error convergence rate regardless of the specific evaluation point, that is, no matter whether it is implemented at a boundary or interior point. Numerical performance is illustrated using an empirical application and simulated data, where a detailed numerical comparison with other R packages is given

Preliminary Results in a Multi-site Empirical Study on Cross-organizational ERP Size and Effort Estimation

This paper reports on initial findings in an empirical study carried out with representatives of two ERP vendors, six ERP adopting organizations, four ERP implementation consulting companies, and two ERP research and advisory services firms. Our study’s goal was to gain understanding of the state-of-the practice in size and effort estimation of cross-organizational ERP projects. Based on key size and effort estimation challenges identified in a previously published literature survey, we explored some difficulties, fallacies and pitfalls these organizations face. We focused on collecting empirical evidence from the participating ERP market players to assess specific facts about the state-of-the-art ERP size and effort estimation practices. Our study adopted a qualitative research method based on an asynchronous online focus group

Three-dimensional image reconstruction in J-PET using Filtered Back Projection method

We present a method and preliminary results of the image reconstruction in the Jagiellonian PET tomograph. Using GATE (Geant4 Application for Tomographic Emission), interactions of the 511 keV photons with a cylindrical detector were generated. Pairs of such photons, flying back-to-back, originate from e+e- annihilations inside a 1-mm spherical source. Spatial and temporal coordinates of hits were smeared using experimental resolutions of the detector. We incorporated the algorithm of the 3D Filtered Back Projection, implemented in the STIR and TomoPy software packages, which differ in approximation methods. Consistent results for the Point Spread Functions of ~5/7,mm and ~9/20, mm were obtained, using STIR, for transverse and longitudinal directions, respectively, with no time of flight information included.Comment: Presented at the 2nd Jagiellonian Symposium on Fundamental and Applied Subatomic Physics, Krak\'ow, Poland, June 4-9, 2017. To be published in Acta Phys. Pol.

Integrate the GM(1,1) and Verhulst models to predict software stage effort

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Software effort prediction clearly plays a crucial role in software project management. In keeping with more dynamic approaches to software development, it is not sufficient to only predict the whole-project effort at an early stage. Rather, the project manager must also dynamically predict the effort of different stages or activities during the software development process. This can assist the project manager to reestimate effort and adjust the project plan, thus avoiding effort or schedule overruns. This paper presents a method for software physical time stage-effort prediction based on grey models GM(1,1) and Verhulst. This method establishes models dynamically according to particular types of stage-effort sequences, and can adapt to particular development methodologies automatically by using a novel grey feedback mechanism. We evaluate the proposed method with a large-scale real-world software engineering dataset, and compare it with the linear regression method and the Kalman filter method, revealing that accuracy has been improved by at least 28% and 50%, respectively. The results indicate that the method can be effective and has considerable potential. We believe that stage predictions could be a useful complement to whole-project effort prediction methods.National Natural Science Foundation of China and the Hi-Tech Research and Development Program of Chin