Computer Sciences Commons^™

How To Gauge Accuracy Of Processing Big Data: Teaching Machine Learning Techniques To Gauge Their Own Accuracy, Vladik Kreinovich, Thongchai Dumrongpokaphan, Hung T. Nguyen, Olga Kosheleva

Departmental Technical Reports (CS)

When the amount of data is reasonably small, we can usually fit this data to a simple model and use the traditional statistical methods both to estimate the parameters of this model and to gauge this model's accuracy. For big data, it is often no longer possible to fit them by a simple model. Thus, we need to use generic machine learning techniques to find the corresponding model. The current machine learning techniques estimate the values of the corresponding parameters, but they usually do not gauge the accuracy of the corresponding general non-linear model. In this paper, we show how …

How Better Are Predictive Models: Analysis On The Practically Important Example Of Robust Interval Uncertainty, Vladik Kreinovich, Hung T. Nguyen, Songsak Sriboonchitta, Olga Kosheleva

Departmental Technical Reports (CS)

One of the main applications of science and engineering is to predict future value of different quantities of interest. In the traditional statistical approach, we first use observations to estimate the parameters of an appropriate model, and then use the resulting estimates to make predictions. Recently, a relatively new predictive approach has been actively promoted, the approach where we make predictions directly from observations. It is known that in general, while the predictive approach requires more computations, it leads to more accurate predictions. In this paper, on the practically important example of robust interval uncertainty, we analyze how more accurate …

Why Student Distributions? Why Matern's Covariance Model? A Symmetry-Based Explanation, Steffen Schön, Gaël Kermarrec, Boris Kargoll, Ingo Neumann, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we show that empirical successes of Student distribution and of Matern's covariance models can be indirectly explained by a natural requirement of scale invariance -- that fundamental laws should not depend on the choice of physical units. Namely, while neither the Student distributions nor Matern's covariance models are themselves scale-invariant, they are the only one which can be obtained by applying a scale-invariant combination function to scale-invariant functions.

Entropy As A Measure Of Average Loss Of Privacy, Luc Longpre, Vladik Kreinovich, Thongchai Dumrongpokaphan

Departmental Technical Reports (CS)

Privacy means that not everything about a person is known, that we need to ask additional questions to get the full information about the person. It therefore seems to reasonable to gauge the degree of privacy in each situation by the average number of binary ("yes"-"no") questions that we need to ask to determine the full information -- which is exactly Shannon's entropy. The problem with this idea is that it is possible, by asking two binary questions -- and thus, strictly speaking, getting only two bits of information -- to sometimes learn a large amount of information. In this …

Performing A Practical Paging Attack On The Lte Network, Nathan H. Yee

Computer Science and Software Engineering

No abstract provided.

Senior Project: Calendar, Jason L. Chin

Computer Science and Software Engineering

This calendar application is meant to help individuals with busy schedules. Those who must balance their time between working on multiple simultaneous projects would categorize key users. In the application, users will be able to participate in multiple projects at any one time. When a user is in a project, they will be able to create tasks, add tasks, be assigned to tasks, and add other users to the project. A key feature in this application is that each user is provided a personal project. In their personal project, any task assigned to the user from any project, will be …

Roborodentia Robot (Duct Tape Craze), Tarrant J. Starck

Computer Science and Software Engineering

Roborodentia is an annual autonomous robotics competition held at Cal Poly in April. In 2017, Roborodentia was a head-to-head double elimination tournament with the winner being the robot that moves more rings onto the scoring pegs. For this year’s competition, I designed, built, programmed, and tested a robot.

Hosting Data Packages Via Drat: A Case Study With Hurricane Exposure Data, G Brooke Anderson, Dirk Eddelbuettel

The R Journal

Data-only packages offer a way to provide extended functionality for other R users. However, such packages can be large enough to exceed the package size limit (5 megabytes) for the Comprehensive R Archive Network (CRAN). As an alternative, large data packages can be posted to additional repostiories beyond CRAN itself in a way that allows smaller code packages on CRAN to access and use the data. The drat package facilitates creation and use of such alternative repositories and makes it particularly simple to host them via GitHub. CRAN packages can draw on packages posted to drat repositories through the use …

Milr: Multiple-Instance Logistic Regression With Lasso Penalty, Ping-Yang Chen, Ching-Chuan Chen, Chun-Hao Yang, Sheng-Mao Chang, Kuo-Jung Lee

The R Journal

The purpose of the milr package is to analyze multiple-instance data. Ordinary multiple instance data consists of many independent bags, and each bag is composed of several instances. The statuses of bags and instances are binary. Moreover, the statuses of instances are not observed, whereas the statuses of bags are observed. The functions in this package are applicable for analyzing multiple-instance data, simulating data via logistic regression, and selecting important covariates in the regression model. To this end, maximum likelihood estimation with an expectation-maximization algorithm is implemented for model estimation, and a lasso penalty added to the likelihood function is …

Flan: An R Package For Inference On Mutation Models, Adrien Mazoyer, Rémy Drouilhet, Stéphane Despréaux, Bernard Ycart

The R Journal

This paper describes flan, a package providing tools for fluctuation analysis of mutant cell counts. It includes functions dedicated to the distribution of final numbers of mutant cells. Parametric estimation and hypothesis testing are also implemented, enabling inference on different sorts of data with several possible methods. An overview of the subject is proposed. The general form of mutation models is described, including the classical models as particular cases. Estimating from a model, when the data have been generated by another, induces different possible biases, which are identified and discussed. The three estimation methods available in the package are …

Psf: Introduction To R Package For Pattern Sequence Based Forecasting Algorithm, Neeraj Bokde, Gualberto Asencio-Cortés, Francisco Martínez-Álvarez, Kishore Kulat

The R Journal

This paper introduces the R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF …

Market Area Analysis For Retail And Service Locations With Mci, Thomas Wieland

The R Journal

In retail location analysis, marketing research and spatial planning, the market areas of stores and/or locations are a frequent subject. Market area analyses consist of empirical observations and modeling via theoretical and/or econometric models such as the Huff Model or the Multiplicative Competitive Interaction Model. The authors’ package MCI implements the steps of market area analysis into R with a focus on fitting the models and data preparation and processing.

Update Of The Nlme Package To Allow A Fixed Standard Deviation Of The Residual Error, Simon H. Heisterkamp, Engelbertus Van Willigen, Paul-Matthias Diderichsen, John Maringwa

The R Journal

The use of linear and non-linear mixed models in the life sciences and pharmacometrics is common practice. Estimation of the parameters of models not involving a system of differential equations is often done by the R or S-Plus software with the nonlinear mixed effects nlme package. The estimated residual error may be used for diagnosis of the fitted model, but not whether the model correctly describes the relation between response and included variables including the true covariance structure. The latter is only true if the residual error is known in advance. Therefore, it maybe necessary or more appropriate to fix …

Autoimage: Multiple Heat Maps For Projected Coordinates, Joshua P. French

The R Journal

Heat maps are commonly used to display the spatial distribution of a response observed on a two-dimensional grid. The autoimage package provides convenient functions for constructing multiple heat maps in unified, seamless way, particularly when working with projected coordinates. The autoimage package natively supports: 1. automatic inclusion of a color scale with the plotted image, 2. construction of heat maps for responses observed on regular or irregular grids, as well as non-gridded data, 3. construction of a matrix of heat maps with a common color scale, 4. construction of a matrix of heat maps with individual color scales, 5. projecting …

Imputets: Time Series Missing Value Imputation In R, Steffen Moritz, Thomas Bartz-Beielstein

The R Journal

The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact, that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a …

On Some Extensions To Ga Package: Hybrid Optimisation, Parallelisation And Islands Evolution, Luca Scrucca

The R Journal

Genetic algorithms are stochastic iterative algorithms in which a population of individuals evolve by emulating the process of biological evolution and natural selection. The R package GA provides a collection of general purpose functions for optimisation using genetic algorithms. This paper describes some enhancements recently introduced in version 3 of the package. In particular, hybrid GAs have been implemented by including the option to perform local searches during the evolution. This allows to combine the power of genetic algorithms with the speed of a local optimiser. Another major improvement is the provision of facilities for parallel computing. Parallelisation has been …

Mdplot: Visualise Molecular Dynamics, Christian Margreitter, Chris Oostenbrink

The R Journal

The MDplot package provides plotting functions to allow for automated visualisation of molecular dynamics simulation output. It is especially useful in cases where the plot generation is rather tedious due to complex file formats or when a large number of plots are generated. The graphs that are supported range from those which are standard, such as RMSD/RMSF (root-mean-square deviation and root-mean-square fluctuation, respectively) to less standard, such as thermodynamic integration analysis and hydrogen bond monitoring over time. All told, they address many commonly used analyses. In this article, we set out the MDplot package’s functions, give examples of the function …

Working With Daily Climate Model Output Data In R And The Futureheatwaves Package, G Brooke Anderson, Colin Eason, Elizabeth A. Barnes

The R Journal

Research on climate change impacts can require extensive processing of climate model output, especially when using ensemble techniques to incorporate output from multiple climate models and multiple simulations of each model. This processing can be particularly extensive when identifying and characterizing multi-day extreme events like heat waves and frost day spells, as these must be processed from model output with daily time steps. Further, climate model output is in a format and follows standards that may be unfamiliar to most R users. Here, we provide an overview of working with daily climate model output data in R. We then present …

Network Visualization With Ggplot2, Sam Tyner, François Briatte, Heike Hofmann

The R Journal

This paper explores three different approaches to visualize networks by building on the grammar of graphics framework implemented in the ggplot2 package. The goal of each approach is to provide the user with the ability to apply the flexibility of ggplot2 to the visualization of network data, including through the mapping of network attributes to specific plot aesthetics. By incorporating networks in the ggplot2 framework, these approaches (1) allow users to enhance networks with additional information on edges and nodes, (2) give access to the strengths of ggplot2, such as layers and facets, and (3) convert network data objects …

Iotools: High-Performance I/O Tools For R, Taylor Arnold, Michael J. Kane, Simon Urbanek

The R Journal

The iotools package provides a set of tools for input and output intensive data processing in R. The functions chunk.apply and read.chunk are supplied to allow for iteratively loading contiguous blocks of data into memory as raw vectors. These raw vectors can then be efficiently converted into matrices and data frames with the iotools functions mstrsplit and dstrsplit. These functions minimize copying of data and avoid the use of intermediate strings in order to drastically improve performance. Finally, we also provide read.csv.raw to allow users to read an entire dataset into memory with the same efficient parsing code. In this …

Dgaselid: An R Package For Selecting A Variable Number Of Features In High Dimensional Data, Nicolae Teodor Melita, Stefan Holban

The R Journal

The dGAselID package proposes an original approach to feature selection in high dimensional data. The method is built upon a diploid genetic algorithm. The genotype to phenotype mapping is modeled after the Incomplete Dominance Inheritance, over passing the necessity to define a dominance scheme. The fitness evaluation is done by user selectable supervised classifiers, from a broad range of options. Cross validation options are also accessible. A new approach to crossover, inspired from the random assortment of chromosomes during meiosis is included. Several mutation operators, inspired from genetics, are also proposed. The package is fully compatible with the data formats …

Orthopanels: An R Package For Estimating A Dynamic Panel Model With Fixed Effects Using The Orthogonal Reparameterization Approach, Mark Pickup, Paul Gustafson, Davor Cubranic, Geoffrey Evans

The R Journal

This article describes the R package OrthoPanels, which includes the function opm(). This function implements the orthogonal reparameterization approach recommended by Lancaster(2002) to estimate dynamic panel models with fixed effects(and optionally: wave specific intercepts). This article provides a statistical description of the orthogonal reparameterization approach, a demonstration of the package using real-world data, and simulations comparing the estimator to the known-to-be-biased OLSestimator and the commonly used GMM estimator.

Editorial, Roger Bivand

The R Journal

This new issue, Volume 9, Issue 1, of the R Journal contains 33 contributed research articles, like the second issue of 2016. Most of the articles present R packages, and cover a very wide range of uses of R. Our journal continues to be critically dependent on its readers, authors, reviewers and editors. Annual submission numbers have grown markedly, but the rate of growth is less than that of the number of CRAN packages. Table 1 shows the outcomes of submitted contributed articles by year of submission. The proportion of submissions reaching publication has been roughly half since 2012.

Minval: An R Package For Minimal Validation Of Stoichiometric Reactions, Daniel Osorio, Janneth González, Andrés Pinzón

The R Journal

A genome-scale metabolic reconstruction is a compilation of all stoichiometric reactions that can describe the entire cellular metabolism of an organism, and they have become an indispensable tool for our understanding of biological phenomena, covering fields that range from systems biology to bioengineering. Interrogation of metabolic reconstructions are generally carried through Flux Balance Analysis, an optimization method in which the biological sense of the optimal solution is highly sensitive to thermodynamic unbalance caused by the presence of stoichiometric reactions whose compounds are not produced or consumed in any other reaction (orphan metabolites) and by mass unbalance. The minval package was …

The Mosaic Package: Helping Students To Think With Data Using R, Randall Pruim, Daniel T. Kaplan, Nicholas J. Horton

The R Journal

The mosaic package provides a simplified and systematic introduction to the core functional ity related to descriptive statistics, visualization, modeling, and simulation-based inference required in first and second courses in statistics. This introduction to the package describes some of the guiding principles behind the design of the package and provides illustrative examples of several of the most important functions it implements. These can be combined to help students “think with data" using R in their early course work, starting with simple, yet powerful, declarative commands.

Aliner: An R Package For Optimizing Feature-Weighted Alignments And Linguistic Distances, Sean S. Downey, Guowei Sun, Peter Norquest

The R Journal

Linguistic distance measurements are commonly used in anthropology and biology when quantitative and statistical comparisons between words are needed. This is common, for example, when analyzing linguistic and genetic data. Such comparisons can provide insight into historical population patterns and evolutionary processes. However, the most commonly used linguistic distances are derived from edit distances, which do not weight phonetic features that may, for example, represent smaller-scale patterns in linguistic evolution. Thus, computational methods for calculating feature-weighted linguistic distances are needed for linguistic, biological, and evolutionary applications; additionally, the linguistic distances presented here are generic and may have broader applications in …

Multilabel Classification With R Package Mlr, Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl

The R Journal

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of random ForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.

Weighted Effect Coding For Observational Data With Wec, Rense Nieuwenhuis, Manfred Te Grotenhuis, Ben Pelzer

The R Journal

Weighted effect coding refers to a specific coding matrix to include factor variables in generalised linear regression models. With weighted effect coding, the effect for each category represents the deviation of that category from the weighted mean (which corresponds to the sample mean). This technique has particularly attractive properties when analysing observational data, that commonly are unbalanced. The wec package is introduced, that provides functions to apply weighted effect coding to factor variables, and to interactions between (a.) a factor variable and a continuous variable and between (b.) two factor variables.

Pdp: An R Package For Constructing Partial Dependence Plots, Brandon M. Greenwell

The R Journal

Complex nonparametric models—like neural networks, random forests, and support vector machines—are more common than ever in predictive analytics, especially when dealing with large observational databases that don’t adhere to the strict assumptions imposed by traditional statistical techniques (e.g., multiple linear regression which assumes linearity, homoscedasticity, and normality). Unfortunately, it can be challenging to understand the results of such models and explain them to management. Partial dependence plots offer a simple solution. Partial dependence plots are low dimensional graphical renderings of the prediction function so that the relationship between the outcome and predictors of interest can be more easily understood. These …

R Foundation News, Torsten Hothorn

The R Journal

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