Numerical Analysis and Scientific Computing Commons^™

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 …

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 …

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 …

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 …

Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.

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 …

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 …

Detecting Personal Intake Of Medicine From Twitter, Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang

Research Collection School Of Computing and Information Systems

Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier …

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 7 months, 1178 new packages were added to the CRAN package repository. 18 packages were unarchived, 493 archived and none removed. The following shows the growth of the number of active packages in the CRAN package repository:

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.0 patched

CHANGES IN R 3.5.0

R Foundation News, Torsten Hothorn

The R Journal

Donations and members

Donations

Supporting benefactors

Supporting institutions

Supporting members

Conference Report: Erum 2018, Gergely Daróczi

The R Journal

The European R Users Meeting (eRum) is an international conference that aims at bringing together users of the R language living in Europe– in the years when the useR! conference is hosted outside of the continent.

The first eRum conference was held in 2016 in Poznan, Poland with around 250 attendees and 20 sessions spanning over 3 days, including more than 80 speakers. Around that time, we also held a smaller conference in Budapest: the first satRday event happened with 25 speakers and almost 200 attendees from 19 countries in 2016.

The eRum 2018 conference is heritage of these two …

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 …

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, …

Approximating The Sum Of Independent Non-Identical Binomial Random Variables, Boxiang Liu, Thomas Quertermous

The R Journal

The distribution of the sum of independent non-identical binomial random variables is frequently encountered in areas such as genomics, healthcare, and operations research. Analytical solutions for the density and distribution are usually cumbersome to find and difficult to compute. Several methods have been developed to approximate the distribution, among which is the saddlepoint approximation. However, implementation of the saddlepoint approximation is non-trivial. In this paper, we implement the saddlepoint approximation in the sinib package and provide two examples to illustrate its usage. One example uses simulated data while the other uses real-world healthcare data. The sinib package addresses the gap …

Nonparametric Independence Tests And K-Sample Tests For Large Sample Sizes Using Package Hhg, Barak Brill, Yair Heller, Ruth Heller

The R Journal

Nonparametric tests of independence and k-sample tests are ubiquitous in modern applications, but they are typically computationally expensive. We present a family of nonparametric tests that are computationally efficient and powerful for detecting any type of dependence between a pair of univariate random variables. The computational complexity of the suggested tests is sub-quadratic in sample size, allowing calculation of test statistics for millions of observations. We survey both algorithms and the HHG package in which they are implemented, with usage examples showing the implementation of the proposed tests for both the independence case and the k-sample problem. The tests are …

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 …