%PROC_R: A SAS Macro that Enables Native R Programming in the Base SAS Environment

In this paper, we describe %PROC_R, a SAS macro that enables native R language to be embedded in and executed along with a SAS program in the base SAS environment under Windows OS. This macro executes a user-defined R code in batch mode by calling the unnamed pipe method within base SAS. The R textual and graphical output can be routed to the SAS output window and result viewer, respectively. Also, this macro automatically converts data between SAS datasets and R data frames such that the data and results from each statistical environment can be utilized by the other environment. The objective of this work is to leverage the strength of the R programming language within the SAS environment in a systematic manner. Moreover, this macro helps statistical programmers to learn a new statistical language while staying in a familiar environment

Statistical Software (R, SAS, SPSS, and Minitab) for Blind Students and Practitioners

Abstracts not available for SoftwareReview

Equivalence Testing the Easy Way

The purpose of the article is to demonstrate an equivalence testing software application written under SAS^1 Institute software designed for use by pharmaceutical and other medical research professionals. Besides making the entire equivalence testing procedure easier and more efficient, the application “EquivEasy” offers three main advantages over similar software: a) testing for 3 x 3 in addition to 2 x 2 crossover designs, b) familiar SAS user environment, and c) export flexibility (MS Word, PDF, HTML). Two case studies are presented with report results provided in tabular and graphical form

A systematic comparison of different approaches of unsupervised extraction of text from scholary figures

Different approaches have been proposed in the past to address the challenge of extracting text from scholarly figures. However, so far a comparative evaluation of the different approaches has not been conducted. Based on an extensive study, we compare the 7 most relevant approaches described in the literature as well as 25 systematic combinations of methods for extracting text from scholarly figures. To this end, we define a generic pipeline, consisting of six individual steps. We map the existing approaches to this pipeline and re-implement their methods for each pipeline step. The method-wise re-implementation allows to freely combine the different possible methods for each pipeline step. Overall, we have evaluated 32 different pipeline configurations and systematically compared the different methods and approaches. We evaluate the pipeline configurations over four datasets of scholarly figures of different origin and characteristics. The quality of the extraction results is assessed using F-measure and Levenshtein distance. In addition, we measure the runtime performance. The experimental results show that there is an approach that overall shows the best text extraction quality on all datasets. Regarding runtime, we observe huge differences from very fast approaches to those running for several weeks

Country risk analysis: an application of logistic regression and neural networks

A research report submitted to the Faculty of Science, School of Statistics and Actuarial Science in partial fulfilment of the requirements for the degree of Master of Science, University of the Witwatersrand. Johannesburg, 08 June 2017. Mathematical Statistics degree, 2017Country risk evaluation is a crucial exercise when determining the ability of countries to repay their debts. The global environment is volatile and is filled with macro-economic, financial and political factors that may affect a country’s commercial environment, resulting in its inability to service its debt. This re search report compares the ability of conventional neural network models and traditional panel logistic regression models in assessing country risk. The mod els are developed using a set of economic, financial and political risk factors obtained from the World Bank for the years 1996 to 2013 for 214 economies. These variables are used to assess the debt servicing capacity of the economies as this has a direct impact on the return on investments for financial institu tions, investors, policy makers as well as researchers. The models developed may act as early warning systems to reduce exposure to country risk. Keywords: Country risk, Debt rescheduling, Panel logit model, Neural net work modelsXL201

Knowledge visualization: From theory to practice

Visualizations have been known as efficient tools that can help users analyze com- plex data. However, understanding the displayed data and finding underlying knowl- edge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different ar- eas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work be- gins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user under- stand the complex data through visual analytical reasoning and discovery

Characterization of Gene Products Associated with Reduced Aflatoxin Accumulation in Maize (Zea Mays)

The fungus Aspergillus flavus L. is an agricultural threat, particularly for maize (Zea mays L.). Its invasive growth and contamination before and after harvest can lead to the loss of the commodity and cause life threating consequences to humans and livestock that consume a contaminated product. The differential expression profile of two resistant maize inbred lines, Mp313E and Mp719, and two susceptible maize inbred lines, B73 and Va35, were evaluated in the mRNA and protein levels. The experimental designed used allowed to observe the responses of these maize lines to A. flavus inoculation and to the stress caused by wounding on kernels. Candidate genes were selected from a prior published GWAS and pathway analysis for the expression analysis at the mRNA level. Seventeen candidate genes were selected, and gene expression analysis via RT-qPCR was performed for nine of them. Two of characterized candidate genes that showed an upregulation above 2olds in the resistant lines. These genes are involved in oxidation responses and had an associated positive allele effect, e.g. contribute to aflatoxin accumulation. The results indicate that their role is not necessarily to make the plant more susceptible to the accumulation of aflatoxin but rather to alleviate the oxidative stress caused by the fungus. Susceptible lines, in general, did not show any difference in the expression of the selected candidate genes. At the protein level, an in-gel analysis identified a variety of stress-related proteins that were upregulated in response to A. flavus inoculation and to wounding stress in the resistant lines. A list of genes associated to the proteins identified was compiled for further characterizations and possible use as molecular markers in marker-assisted selection of commercial maize lines resistant to A. flavus

CONSULTATION EXPERIENCE IN PUBLIC HEALTH

The following portfolio is an overview of my consulting experience in public health. My experiences include making frequency tables, producing summary statistics, conducting Chi- Square and T-Test, and reproducing a demographics table. Working with investigators, I have conducted a power analysis for a pilot study, assisted with a study design consultation, used the Kappa coefficient to test for inter-rater reliability, presented survey results with answer distributions, and produced summary statistics for an investigator’s research poster. Working in the Applied Statistics Laboratory and with investigators has taught me the process of consulting. It begins by meeting with an investigator and determining their research needs. This can be many things, including study design, data management, and data analysis. Some investigators come with data and a study design already in place, while others will come to the Applied Statistics Laboratory for each step of the project, from the beginning to end. After determining an investigator’s needs and at what point they are in their research, members of the Applied Statistics Laboratory are assigned tasks to assist investigators with their research. In addition to data management and study design, data analysis is often conducted so investigators can receive a report to use for their research results. Being a part of the Applied Statistics Laboratory and assisting with consultations gave me the opportunity to improve my programming skills. As seen in this portfolio, I wrote numerous SAS programs and utilized various procedures and statements, including higher-level programming with SAS Macros. I also had the opportunity to obtain real-world practice conducting data management and data analysis. An important part of working with investigators was learning how to interpret statistical output, which is found in the “Critical Thinking” portions of each project in this portfolio. In the lab I also had the opportunity to follow up with investigators and produce reports which displays data in a form that is useful to the investigator, such as tables and graphs. The final page of my portfolio includes a poster made by an investigator I worked with. She utilized tables and graphs which I produced in her results section. I enjoyed my consulting experience in public health and plan to use many of the skills I learned in consulting in the future. I expect to use the numerous SAS skills I learned at my future job, along with what I have learned about study design, data management, and data analysis

Image Segmentation and Classification using Small Data: An Application on Friendly Statements - A Computer Vision and Document Intelligence Project

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceInsurance companies face significant challenges in managing numerous physical documents containing critical information, resulting in considerable time and cost expenditures. Although Deep Learning models offer a promising solution, their implementation costs and data privacy concerns restrict widespread adoption, especially when dealing with confidential documents. This internship report presents a novel approach to address these challenges by developing a lightweight computer vision solution for accurately detecting and processing checkboxes from Portuguese friendly statements. The key objective was to demonstrate the feasibility of achieving high accuracy without relying on advanced Deep Learning techniques. By leveraging a small set of examples, we successfully extracted checkbox information while mitigating the high computational requirements associated with traditional Deep Learning models. The results highlight the practicality and cost-effectiveness of our approach, offering insurance companies a viable solution to streamline document management, enhance data security, and improve overall efficiency. This research contributes to the computer vision field by providing valuable insights into alternative methodologies that can be adopted to overcome the limitations of Deep Learning, facilitating broader accessibility and utilization among insurance providers