Evaluation of a statistical programming environment for analytical chemistry

1996 ◽  
Vol 15 (9) ◽  
pp. 458-462
Author(s):  
Max Feinberg
Keyword(s):  
Analytical Chemistry ◽  
Programming Environment ◽  
Statistical Programming

scholarly journals Non-spatial and Spatial Linear Regression

2021 ◽  
Author(s):  
Wouter Steenbeek ◽  
Stijn Ruiter
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Spatial Dependence ◽  
Linear Regression Analysis ◽  
Predictor Variable ◽  
Programming Environment ◽  
Statistical Programming ◽  
Multiple Predictor ◽  
Applied Researcher ◽  
Broad Overview

This chapter gives an introduction to the workhorse of quantitative statistical analysis, linear regression analysis, assuming minimal background knowledge of the reader. We give a broad overview of linear regression analysis using one predictor variable and then turn to regression with multiple predictor variables and key assumptions, which segues into regression analysis of areal units that include spatial dependence. Throughout we use the statistical programming environment R, and we try to summarize the most important challenges that an applied researcher will face. As this is just an introduction to the topic, we provide references to sources that are highly recommended for any researcher who aims to understand or apply (spatial) linear regression analysis.

How to use this book.

2021 ◽  
pp. 1-2
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Statistical Analysis ◽  
Open Source ◽  
Programming Environment ◽  
Biology Students ◽  
Book Title ◽  
Analysis Tools ◽  
Simple Step ◽  
Statistical Programming

Abstract This book takes a simple step-by-step approach to give a good grounding in the use of R for undergraduate/beginning postgraduate biology students. R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. This chapter provides some steps on how to use the book, from setting up the computer to running the code as you go along. The chapter structure is also introduced.

scholarly journals - Solving the probability regression models by using R programming environment - Case Study For patients Epilepsy in Basra : حل نماذج الانحدار الاحتمالية باستخدام بيئة برمجة R - دراسة حالة لمرضى الصرع –

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Nada Badr Jarah
Keyword(s):  
Regression Models ◽  
Age Factors ◽  
Scientific Progress ◽  
Regression Function ◽  
Economic Effects ◽  
Programming Environment ◽  
Factors Affecting ◽  
Statistical Programming ◽  
R Programming ◽  
Patients With Epilepsy

The technology for dealing with accurate calculations progresses very quickly . and be a major impetus for scientific progress. and the perfect mastery of R in higher levels is necessary for many applications . The R statistical programming environment provides an ideal stage to conduct the study because of its powerful programming capability , graphics and a comprehensive set of statistical functions , it contains more than 11164 packages . The aim is to study the phenomenon of epilepsy and to identify the most important factors affecting patients with epilepsy in the province of Basra. And the cause of this disease of social and economic effects on the patient and his family. and also this study is to solve the probability regression models represented by probit, tobit and logit ,as well as identifying the variables of the study (sex and age) that affect or increase the incidence epilepsy for the data of 2296 patients in Basra. The study design , is the represent by estimating the three probable regression models by probit, tobit and logit for the effect of sex and age factors on the risk of developing epilepsy ,and the tools used is to make statistic program ,then we find the R programming language was give the good and correct answer, Then the statistical analysis using R language in which the written orders are implemented directly without the need to build a complete program to implement the programming statements using the regression function for each probability regression models (Probit, Tobit and Logit) and the results showed that the regression of the probabilistic functions is that both the age factor and the sex factor have a significant effect in the case of the disease in terms of being inpatient or outpatient. This is confirmed by the Wold test. and Also the conclusion shows that functions represented the best representation, which was confirmed by the coefficient of selection as it reached 0.96.

scholarly journals rMyCoPortal - an R package to interface with the Mycology Collections Portal

2019 ◽  
Vol 7 ◽  
Author(s):  
Franz-Sebastian Krah ◽  
Scott Bates ◽  
Andrew Miller
Keyword(s):  
Open Source ◽  
Basic Research ◽  
R Package ◽  
Programming Environment ◽  
Web Interface ◽  
Biogeographical Distribution ◽  
Online Databases ◽  
Statistical Programming ◽  
Research Questions ◽  
Large Effort

The understanding of the biodiversity and biogeographical distribution of fungi is still limited. The small number of online databases and the large effort required to access existing data have prevented their use in research articles. The Mycology Collections Portal was established in 2012 to help alleviate these issues and currently serves data online for over 4.3 million fungal records. However, the current process for accessing the data through the web interface is manual, therefore slow, and precludes the extensive use of the existing datasets. Here we introduce the software package rMyCoPortal, which allows users rapid, automated access to the data. rMyCoPortal makes data readily available for further computations and analyses in the open source statistical programming environment R. We will demonstrate the core functions of the package, and how rMyCoPortal can be employed to obtain fungal data that can be used to address basic research questions. rMyCoPortal is a free and open-source R package, available via GitHub.

scholarly journals queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data

2019 ◽  
Vol 35 (17) ◽  
pp. 3203-3205
Author(s):  
Y Rivault ◽  
O Dameron ◽  
N Le Meur
Keyword(s):  
Linked Data ◽  
Medical Knowledge ◽  
Limb Ischemia ◽  
R Package ◽  
Public Health Research ◽  
Health Data ◽  
Programming Environment ◽  
Medical Database ◽  
Biomedical Knowledge ◽  
Statistical Programming

AbstractSummaryIn public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and pharmacological knowledge stored in formats compliant with the Linked Data paradigm (e.g. OWL ontologies and RDF datasets) into the R statistical programming environment. We show how it allowed us to identify all the drugs prescribed for critical limb ischemia (CLI) and also to detect one contraindicated prescription for one patient by linking a medical database of 1003 CLI patients to ontologies.Availability and implementationqueryMed is readily usable for medical data mappings and enrichment. Sources, R vignettes and test data are available on GitHub (https://github.com/yannrivault/queryMed) and are archived on Zenodo (https://doi.org/10.5281/zenodo.1323481).

scholarly journals GazeR: A Package for Processing Gaze Position and Pupil Size Data

2019 ◽  
Author(s):  
Jason Geller ◽  
Matthew Winn ◽  
Tristan Mahr ◽  
Daniel Mirman
Keyword(s):  
Eye Tracking ◽  
Pupil Size ◽  
Programming Environment ◽  
Eye Tracker ◽  
Vision Science ◽  
Position Data ◽  
Statistical Programming ◽  
Data Files ◽  
Areas Of Interest ◽  
Gaze Position

Eye-tracking is widely used throughout the scientific community, from vision science and psycholinguistics, to marketing and human-computer interaction. Surprisingly, there is little consistency and transparency in preprocessing steps, making replicability difficult. To increase replicability and transparency, a package in R (a free and widely used statistical programming environment) called gazeR was created to read in and preprocess two types of data from the SR EyeLink eye tracker: gaze position and pupil size. For gaze position data, gazeR has functions for: reading in raw eye-tracking data, formatting it for analysis, converting from gaze coordinates to areas of interest, and binning and aggregating data. For data from pupillometry studies, the gazeR package has functions for: reading in and merging multiple raw pupil data files, removing observations with too much missing data, eliminating artifacts, blink identification and interpolation, subtractive baseline correction, and binning and aggregating data. The package is open-source and freely available for download and installation: https://github.com/dmirman/gazer. We provide step

Analytical Chemistry in Archaeology

2007 ◽  
Author(s):  
A. M. Pollard ◽  
C. M Batt ◽  
B. Stern ◽  
S. M. M. Young
Keyword(s):  
Analytical Chemistry
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