Numerical Analysis and Scientific Computing Commons^™

Rpostgis: Linking R With A Postgis Spatial Database, David Bucklin, Mathieu Basille

The R Journal

With the proliferation of sensors and the ease of data collection from online sources, large datasets have become the norm in many scientific disciplines, and efficient data storage, management, and retrival is imperative for large research projects. Relational databases provide a solution, but in order to be useful, must be able to be linked to analysis and visualization tools, such as R. Here, we present a package intended to facilitate integration of R with the open-source database software PostgreSQL, with a focus on its spatial extension, PostGIS. The package rpostgis (version 1.4.1) provides methods for spatial data handling (vector and …

R Package Imputetestbench To Compare Imputation Methods For Univariate Time Series, Marcus W. Beck, Neeraj Bokde, Gualberto Asencio-Cortés, Kishore Kulat

The R Journal

Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are …

Advanced Bayesian Multilevel Modeling With The R Package Brms, Paul-Christian Bürkner

The R Journal

The brms package allows R users to easily specify a wide range of Bayesian single-level and multilevel models which are fit with the probabilistic programming language Stan behind the scenes. Several response distributions are supported, of which all parameters (e.g., location, scale, and shape) can be predicted. Non-linear relationships may be specified using non-linear predictor terms or semi-parametric approaches such as splines or Gaussian processes. Multivariate models can be fit as well. To make all of these modeling options possible in a multilevel framework, brms provides an intuitive and powerful formula syntax, which extends the well known formula syntax of …

Welfare, Inequality And Poverty Analysis With Rtip: An Approach Based On Stochastic Dominance, Angel Berihuete, Carmen D. Ramos, Miguel A. Sordo

The R Journal

Disparities in economic welfare, inequality and poverty across and within countries are of great interest to sociologists, economists, researchers, social organizations and political scientists. Information about these topics is commonly based on surveys. We present a package called rtip that implements techniques based on stochastic dominance to make unambiguous comparisons, in terms of welfare, poverty and inequality, among income distributions. Besides providing point estimates and confidence intervals for the most commonly used indicators of these characteristics, the package rtip estimates the usual Lorenz curve, the generalized Lorenz curve, the TIP (Three I’s of Poverty) curve and allows to test statistically …

Support Vector Machines For Survival Analysis With R, Césaire J.K. Fouodo, Inke R. König, Claus Weihs, Andreas Ziegler, Marvin N. Wright

The R Journal

This article introduces the R package survivalsvm, implementing support vector machines for survival analysis. Three approaches are available in the package: The regression approach takes censoring into account when formulating the inequality constraints of the support vector problem. In the ranking approach, the inequality constraints set the objective to maximize the concordance index for comparable pairs of observations. The hybrid approach combines the regression and ranking constraints in a single model. We describe survival support vector machines and their implementation, provide examples and compare the prediction performance with the Cox proportional hazards model, random survival forests and gradient boosting using …

Mglm: An R Package For Multivariate Categorical Data Analysis, Juhyun Kim, Yiwen Zhang, Joshua Day, Hua Zhou

The R Journal

Data with multiple responses is ubiquitous in modern applications. However, few tools are available for regression analysis of multivariate counts. The most popular multinomial-logit model has a very restrictive mean-variance structure, limiting its applicability to many data sets. This article introduces an R package MGLM, short for multivariate response generalized linear models, that expands the current tools for regression analysis of polytomous data. Distribution fitting, random number generation, regression, and sparse regression are treated in a unifying framework. The algorithm, usage, and implementation details are discussed.

Cchs: An R Package For Stratified Case-Cohort Studies, Edmund Jones

The R Journal

The cchs package contains a function, also called cchs, for analyzing data from a stratified case-cohort study, as used in epidemiology. For data from this type of study, cchs calculates Estimator III of Borgan et al. (2000), which is a score-unbiased estimator for the regression coefficients in the Cox proportional hazards model. From the user’s point of view, the function is similar to coxph (in the survival package) and other widely used model-fitting functions. Convenient software has not previously been available for Estimator III since it is complicated to calculate. SAS and S-Plus code-fragments for the calculation have been published, …

Small Area Disease Risk Estimation And Visualization Using R, Paula Moraga

The R Journal

Small area disease risk estimation is essential for disease prevention and control. In this paper, we demonstrate how R can be used to obtain disease risk estimates and quantify risk factors using areal data. We explain how to define disease risk models and how to perform Bayesian inference using the INLA package. We also show how to make interactive maps of estimates using the leaflet package to better understand the disease spatial patterns and communicate the results. We show an example of lung cancer risk in Pennsylvania, United States, in year 2002, and demonstrate that R represents an excellent tool …

Semiparametric Generalized Linear Models With The Gldrm Package, Michael J. Wurm, Paul J. Rathouz

The R Journal

This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood. The new algorithm incorporates several computational stability and efficiency improvements over the algorithm originally proposed. In particular, the new algorithm performs well for either small or large support for the nonparametric response distribution. The …

Ratingscalereduction Package: Stepwise Rating Scale Item Reduction Without Predictability Loss, Waldemar W. Koczkodaj, Feng Li, Alicja Wolny–Dominiak

The R Journal

This study presents an innovative method for reducing the number of rating scale items without predictability loss. The “area under the receiver operator curve” method (AUC ROC) is used for the stepwise method of reducing items of a rating scale. RatingScaleReduction R package contains the presented implementation. Differential evolution (a metaheuristic for optimization) was applied to one of the analyzed datasets to illustrate that the presented stepwise method can be used with other classifiers to reduce the number of rating scale items (variables). The targeted areas of application are decision making, data mining, machine learning, and psychometrics.

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.7 was released on 1 May, 2018. It is compatible with R 3.5.1 and consists of 1560 software packages, 342 experiment data packages, and 919 up-to-date annotation packages. The release announcement includes descriptions of 98 new software packages and updated NEWS files for many additional packages. Start using Bioconductor by installing the most recent version of R and evaluating the commands

A System For An Accountable Data Analysis Process In R, Jonathan Gelfond, Martin Goros, Brian Hernandez, Alex Bokov

The R Journal

Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the ’verifiable …

Mmpf: Monte-Carlo Methods For Prediction Functions, Zachary M. Jones

The R Journal

Machine learning methods can often learn high-dimensional functions which generalize well but are not human interpretable. The mmpf package marginalizes prediction functions using Monte-Carlo methods, allowing users to investigate the behavior of these learned functions, as on a lower dimensional subset of input features: partial dependence and variations thereof. This makes machine learning methods more useful in situations where accurate prediction is not the only goal, such as in the social sciences where linear models are commonly used because of their interpretability.

Many methods for estimating prediction functions produce estimated functions which are not directly human-interpretable because of their complexity: …

Generalized Additive Model Multiple Imputation By Chained Equations With Package Imputerobust, Daniel Salfran, Martin Spiess

The R Journal

Data analysis, common to all empirical sciences, often requires complete data sets. Unfortunately, real world data collection will usually result in data values not being observed. We present a package for robust multiple imputation (the ImputeRobust package) that allows the use of generalized additive models for location, scale, and shape in the context of chained equations. The paper describes the basics of the imputation technique which builds on a semi-parametric regression model (GAMLSS) and the algorithms and functions provided with the corresponding package. Furthermore, some illustrative examples are provided.

Fhdi: An R Package For Fractional Hot Deck Imputation, Jongho Im, In Ho Cho, Jae Kwang Kim

The R Journal

Fractional hot deck imputation (FHDI), proposed by Kalton and Kish (1984) and investigated by Kim and Fuller (2004), is a tool for handling item nonresponse in survey sampling. In FHDI, each missing item is filled with multiple observed values yielding a single completed data set for subsequent analyses. An R package FHDI is developed to perform FHDI and also the fully efficient fractional imputation (FEFI) method of (Fuller and Kim, 2005) to impute multivariate missing data with arbitrary missing patterns. FHDI substitutes missing items with a few observed values jointly obtained from a set of donors whereas the FEFI uses …

The R Journal (July 2018) 10(1): Complete Issue, The R Foundation

The R Journal

Editorial, John Verzani

Contributed Research Articles

A System for an Accountable Data Analysis Process in R, Jonathan Gelfond, Martin Goros, Brian Hernandez and Alex Bokov

RealVAMS: An R Package for Fitting a Multivariate Value-added Model (VAM), Jennifer Broatch, Jennifer Green, and Andrew Karl

InfoTrad: An R Package for Estimating the Probability of Informed Trading, Duygu Çelik and Murat Tiniç

RatingScaleReduction Package: Stepwise Rating Scale Item Reduction without Predictability Loss, Waldemar W. Koczkodaj, Feng Li, and Alicja Wolny-Dominiak

mmpf: Monte-Carlo Methods for Prediction Functions, Zachary M. Jones

Generalized Additive Model Multiple Imputation by Chained Equations with Package ImputeRobust, Daniel Salfran and …

Deeptravel: A Neural Network Based Travel Time Estimation Model With Auxiliary Supervision, Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment or designed heuristically in a non-learning-based way. The former is not able to capture many cross-segment complex factors while the latter fails to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well …

A Survey Of Matrix Completion Methods For Recommendation Systems, Andy Ramlatchan, Mengyun Yang, Quan Liu, Min Li, Jianxin Wang, Yaohang Li

Computer Science Faculty Publications

In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative filtering. The collaborative filtering problem can be viewed as predicting the favorability of a user with respect to new items of commodities. When a rating matrix is constructed with users as rows, items as columns, and entries as ratings, the collaborative filtering problem can then be modeled as a matrix completion problem by filling out the unknown elements in the rating matrix. This article …

Online Deep Learning: Learning Deep Neural Networks On The Fly, Doyen Sahoo, Hong Quang Pham, Jing Lu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch setting, requiring the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream. We aim to address an open challenge of “Online Deep Learning” (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is more challenging as the optimization objective is non-convex, and regular DNN with …

Online Active Learning With Expert Advice, Shuji Hao, Peiying Hu, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an …