Group items tagged

In an experiment study, various treatments are applied to test subjects and the response data is gathered for analysis. A critical tool for carrying out the analysis is the Analysis of Variance (ANOVA).

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. Among other things it has an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either directly at the computer or on hardcopy, and a well developed, simple and effective programming language (called ‘S’) which includes conditionals, loops, user defined recursive functions and input and output facilities. (Indeed most of the system supplied functions are themselves written in the S language.)

Data ExplorationDecision Treesk-means ClusteringHierarchical ClusteringOutlier DetectionTime Series AnalysisTime Series Clustering and ClassificationAssociation RulesText MiningSocial Network AnalysisParallel ComputingOther Examples

Jim Guszcza (Predictive Analytics lead at Deloitte Consulting and Assistant Professor at UW-Madison) who gave a great webinar presentation yesterday

R is used for exploratory data analysis and modeling, with a live examples of fitting a mixute distribution to bimodal claims data, and calculating loss reserves using Poisson regression.

With data analysis showing up in domains as varied as baseball, evidence-based medicine, predicting recidivism and child support lapses, judging wine quality, credit scoring, supermarket scanner data analysis, and “genius” recommendation engines, “business analytics” is part of the zeitgeist. This is a good moment for actuaries to remember that their discipline is arguably the first – and a quarter of a millennium old – example of business analytics at work.

This session will include an overview of the R statistical computing environment as well as a sequence of brief case studies of actuarial analyses in R. Case studies will include examples from loss distribution analysis, ratemaking, loss reserving, and predictive modeling.

It's incredibly diverse. It ranges from pharmaceuticals for drug developments, biostatistics, gene analysis, genetic analysis into finance. It's very heavily used in hedge funds, quantitative finance risk, credit analysis, fraud detection, understanding customer churn, understanding customer behavior, pricing optimization forecasting. We have customers who are entertainment or gaming companies using it to understand buying patterns and effectiveness of different media campaigns. So it ranges anywhere from life sciences to finance to Internet to telecom.

What makes R particularly powerful is that statisticians and statistically minded people around the world have contributed packages to the R Group and maintain a very active news group offering suggestions and help. The growing collection of packages and the ease with which they interact with each other and the core R is perhaps the greatest advantage of R

Using R in 12 simple steps for personality research (These steps are not meant to limit what can be done with R, but merely to describe how to do the analysis for the most basic of research projects and to give a first experience with R). Install R on your computer or go to a machine that has it. Download the psych package as well as other recommended packages from CRAN using the install.packages function, or using the package installer in the GUI. To get packages recommended for a particular research field, use the ctv package to install a particular task view. Note, these first two steps need to be done only once! Activate the psych package or other desired packages using e.g., library(psych). This needs to be done every time you start R. Or, it is possible to modify the startup parameters for R so that certain libraries are loaded automatically. Enter your data using a text editor and save as a text file (perhaps comma delimited if using a spreadsheet program such as Excel or OpenOffice) Read the data file or copy and paste from the clipboard (using, e.g., read.clipboard). Find basic descriptive statistics (e.g., means, standard deviations, minimum and maxima) using describe. Prepare a simple descriptive graph (e.g, a box plot) of your variables. Find the correlation matrix to give an overview of relationships (if the number is not too great, a scatter plot matrix or SPLOM plot is very useful, this can be done with pairs.panels. If you have an experimental variable, do the appropriate multiple regression using stan- dardized or at least zero centered scores. If you want to do a factor analysis or principal components analysis, use the factanal or fa and principal function To score items and create a scale and find various reliability estimates, use score.items and perhaps omega. Graph the results.

Project Goals The broad goals of the Bioconductor project are: To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software. To further scientific understanding by producing high-quality documentation and reproducible research. To train researchers on computational and statistical methods for the analysis of genomic data

Windows and MacOS

R is available as Free Software

R is a language and environment for statistical computing and graphics.

developed at Bell Laboratories

R provides an Open Source route to participation in that activity.

classification and clustering of time series data are not readily supported by existing R functions or packages.

R Library support; visualization Steep learning curve Yes Finance; Statistics

Matlab Elegant matrix support; visualization Expensive; incomplete statistics support No Engineering

Chemometrics: Li

Medical Image Analysis:

harmacokinetics

Here at Revolution Analytics, we’d like to thank all of the data scientists who participated in our first R in Business competition and congratulate the winners. Each of the applications selected demonstrates how predictive analytics with R goes well beyond traditional business intelligence to empower business decision-makers to evaluate critical success factors within important business processes early and often. We see from the winning entries that a wide variety of industries and processes – from marketing, heavy manufacturing, clinical trial design, and IT project management all share a need for ongoing predictive analysis. Revolution Analytics delivers the only R-based enterprise analytics framework, and by examining undiscovered applications of the R language, Revolution Analytics can further refine R tools to meet all organizations’ deep analysis needs.

Learn more about the winning entries in the slide show below:

R es utilizado en google y faceboo