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Alpha Teleco

R Tutorial Series: Basic Polynomial Regression | (R news & tutorials) - 0 views

www.r-bloggers.com/es-basic-polynomial-regression

tutorial series basic basico regression regresion news tutorials polynomial no lineal

shared by Alpha Teleco on 29 Apr 13 - No Cached
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Tutorial regresión no lineal basica en R.
  • Polynomial regression can be used to explore a predictor at different levels of curvilinearity.
  • This tutorial will demonstrate how polynomial regression can be used in a hierarchical fashion to best represent a dataset in R.
  • ...24 more annotations...
  • Scatterplot
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      El grafico de dispersion demuestra que estos datos pueden no ser lineales.
  • The preceding scatterplot demonstrates that these data may not be linear.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Como crear variables de orden lineal, cuadratico y cubico.
  • Creating The Higher Order Variables
  • In this tutorial, we will explore the a linear, quadratic, and cubic model.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Paso 1: centrar la variable, restando la media de cada punto y guardando el resultado en una nueva variable de R.
  • Step 1: Centering
  • To center a variable, simply subtract its mean from each data point and save the result into a new R variable, as demonstrated below.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Paso 2: Crear los terminos de orden superior multiplicando la variable creada por si misma y guardando el resultado en una nueva variable.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Crear los modelos lineales y no lineales.
  • Once the input variable has been centered, the higher order terms can be created.
  • we can simply multiply our centered term from step one and save the result into a new R variable, as demonstrated below.
  • Step 2: Multiplication
  • Creating The Models
  • Now we have all of the pieces necessary to assemble our linear and curvilinear models.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      Evaluar los modelos y compararlos, en el ejemplo se observa que no ha una diferencia significativa entre los tres modelos.
  • Evaluating The Models
  • In this case, the quadratic and cubic terms are not statistically significant themselves nor are their models statistically significant beyond the linear model.
    • Alpha Teleco
      Alpha Teleco on 29 Apr 13
       
      A traves de los modelos que hay en este tutorial se pueden crear modelos muy complejos, utilizando las mismas sencillas tecnicas de este tutorial.
  • it can be helpful to summarize and compare our potential models using the summary(MODEL) and anova(MODEL1, MODEL2,… MODELi) functions.
  • More On Interactions, Polynomials, and HLR
  • The regression topics covered in these tutorials can be mixed and matched to create exceedingly complex models.
  • The good news is that more complex models can be created using the same techniques covered here. The basic principles remain the same.
  • Alpha Teleco on 29 Apr 13
     
    Tutorial
Alexis Agustín

See how Deloitte uses R for actuarial analysis - 0 views

blog.revolutionanalytics.com/...-r-for-actuarial-analysis.html

Deloitte Analisis de datos Regression Poisson extrapolacion

shared by Alexis Agustín on 25 Apr 13 - No Cached
    • Alexis Agustín
      Alexis Agustín on 25 Apr 13
       
      Aqui un consultor de Deloitte nos muestra como usa R para explorar datos y ajustarlos en distribuciones, calculando proyecciones usando la regresion de Poisson. Ademas lo contrapone a Excel, concluyendo que no hay una hoja de calculo en la que confundir columnas, si no que en lineas de codigo simple que se puede copiar se puede trabajar mas facilmente con grandes cantidades de datos
  • 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.
  • Jim Guszcza (Predictive Analytics lead at Deloitte Consulting and Assistant Professor at UW-Madison) who gave a great webinar presentation yesterday
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  • Just one simple line of [R] code that would work just as well for a 100-by-100 loss triangle as it would for a 10-by-10 triangle. No hidden cells in the spreadsheet, no risk of spreadsheet error. It's a little bit of code you could look at in one screen, it's replicable ... and this does all the work that a spreadsheet would do. 
  • He uses the Allstate Claim Prediction Challenge data (from a recent Kaggle competition) to fit a Tweedie model to 13 million records of claim data. (The Tweedie distribution is often used to model insurance claims, where many claims are exactly zero, and non-zero claims follow a continuous Gamma-like distribution.) Using the forthcoming rxGLM function, he fit the model to this large data set in just over two minutes (140.22 seconds) using a single quad-core PC.
pajoje lololo

R (programming language) - Wikipedia, the free encyclopedia - 1 views

en.wikipedia.org/...R_(programming_language)

shared by pajoje lololo on 24 Apr 13 - Cached
    • David Moya
      David Moya on 24 Apr 13
       
      Anualmente se celebra un encuentro para los usuarios que utilizan el lenguaje R. Entre los últimos destinos en los que se han celebrado las reuniones, podemos destacar Rennes, Tennessee o Albacete.
  • R is an interpreted language; users typically access it through a command-line interpreter.
  • R supports matrix arithmetic
  • ...7 more annotations...
  • R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.
  • R's extensible object-system includes objects for (among others): regression models, time-series and geo-spatial coordinates.
  • Although mostly used by statisticians and other practitioners requiring an environment for statistical computation and software development, R can also operate as a general matrix calculation toolbox - with performance benchmarks comparable to GNU Octave or MATLAB.
  • "useR!" is the name given to the official annual gathering of R users.
  • Here is the list of useR! conference: useR! 2004, Vienna, Austria useR! 2006, Vienna, Austria useR! 2007, Ames, Iowa, USA useR! 2008, Dortmund, Germany useR! 2009, Rennes, France useR! 2010, Gaithersburg, Maryland, USA useR! 2011, Coventry, United Kingdom useR! 2012, Nashville, Tennessee, USA useR! 2013, Albacete, Spain
    • David Moya
      David Moya on 24 Apr 13
       
      R es un lenguaje basado en el uso de línea de comandos. Soporta matrices, escalares y vectores además de operar estadísticamente. Su cálculo en el ámbito de matrices es comparable a otros softwares como pueden ser Matlab o GNU Octave.
    • pajoje lololo
      pajoje lololo on 24 Apr 13
       
      Hemos elegido consultar la información general sobre R en la versión en inglés de Wikipedia, ya que nos parecía más completa y elaborada. En primer lugar se enumeran las capacidades principales de este lenguaje y algunos de sus principales usos. También se destaca el hecho de que es 'GNU project' y que está disponible bajo licencia GPL. A pesar de ser un lenguaje que utiliza la linea de comandos, se descaca que existen diversas interfaces gráficas para facilitar su uso. En un apartado posterior se enumeran algunas de las principales versiones de estas interfaces. http://en.wikipedia.org/wiki/R_(programming_language)#Graphical_user_interfaces Se destaca en varios de los apartados del artículo la enorme versatilidad y capacidad de adaptación a distintos campos que posee este lenguaje debido a la gran cantidad de paquetes y extensiones que soporta. También se indica que debido a su herencia del lenguaje S, está más dirigido a la programación orientada a objetos que la mayoria de lenguajes estadísticos. Finalmente se destaca el uso comercial que se le ha dado a este lenguaje, y encontramos ejemplos como el de Oracle y el de Revolution Analytics.
Bora Jasan

Quick-R: Basic Statistics - 0 views

www.statmethods.net/stats

statistics basic programming R

shared by Bora Jasan on 01 May 13 - No Cached
    • Bora Jasan
      Bora Jasan on 01 May 13
       
      Siguiendo con el funcionamiento del lenguaje de Programación R, podemos comprobar que puede describir básicas y complejas estadísticas, como por ejemplo: Tablas de contingencia (registrar y analizar la relación entre dos o más variables). Regresiones o ajustes lineales (simple o múltiple): Este tipo de estadísticas también se estudian en Matemáticas II tanto a nivel teórico como a nivel práctico (con Matlab). [...]
    • Bora Jasan
      Bora Jasan on 06 May 13
       
      Siguiendo con el funcionamiento del lenguaje de Programación R, podemos comprobar que puede describir básicas y complejas estadísticas, como por ejemplo: Tablas de contingencia (registrar y analizar la relación entre dos o más variables). Regresiones o ajustes lineales (simple o múltiple): Este tipo de estadísticas también se estudian en Matemáticas II tanto a nivel teórico como a nivel práctico (con Matlab). [...]
  • This section describes basic (and not so basic) statistics. It includes code for obtaining descriptive statistics, frequency counts and crosstabulations (including tests of independence), correlations (pearson, spearman, kendall, polychoric), t-tests (with equal and unequal variances), nonparametric tests of group differences
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  • multiple linear regression (including diagnostics, cross-validation and variable selection), analysis of variance (including ANCOVA and MANOVA), and statistics based on resampling.
nerub val

R Programming/Introduction - Wikibooks, open books for an open world - 1 views

en.wikibooks.org/...Introduction

shared by nerub val on 24 Apr 13 - Cached
  • R is statistical software which is used for data analysis.
  • It includes a huge number of statistical procedures such as t-test, chi-square tests, standard linear models, instrumental variables estimation, local polynomial regressions, etc. It also provides high-level graphics
  • capabilities
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  • R is an object oriented programming language. This means that virtually everything can be stored as an R object. Each object has a class. This class describes what the object contains and what each function does with it. For instance, plot(x) produces different outputs depending on whether x is a regression object or a vecto
  • The assignment symbol is "<-". Alternatively, the classical "=" symbol can be used.
  • Arguments are passed to functions inside round brackets (parentheses). One can easily combine functions. For instance you can directly type
  • The symbol "#" comments to the end of the line:
  • Commands are normally separated by a newline. If you want to put more than one statement on a line, you can use the ";" delimiter.
  • You can also have one statement on multiple lines. R is case sensitive: a and A are two different objects. Traditionally underscores "_" are not used in names. It is often better to use dots ".". One should avoid using an underscore as the first character of an object name.
  • Here are some things editors do to keep this book internally consistent. If you have something to contribute, go ahead and make your contribution. Other editors can touch up your edits afterwards so that they conform to the guidelines.
  • The local manual of style WB:LMOS for the R programming book, including a brief explanation of why we do it that way, is: Examples use "source" tags : <source lang="rsplus"> a <- 1:10 ; mean(a) </source>. That makes them look pretty to our readers. The name of packages are in bold  : '''Hmisc'''. Name of functions are in "code" tags: <code>lm()</code>. Page titles -- the part after "R Programming/" -- are in sentence case, like "R Programming/Working with data frames". We couldn't decide between sentence case and title case, so I flipped a coin. Every page has <noinclude>{{R Programming/Navigation}}</noinclude> at the top and {{R Programming/Navbar|Mathematics|Probability Distributions}} at the bottom. That makes it easier to navigate from one page to another online.
    • nerub val
      nerub val on 24 Apr 13
       
      La lenguaje R es un software de estatisticas que hace analisis de los datos.
    • nerub val
      nerub val on 24 Apr 13
       
      Las ventajas de la lenguaje R
    • nerub val
      nerub val on 24 Apr 13
       
      las caracteristicas de la lenguaje R
    • nerub val
      nerub val on 24 Apr 13
       
      Cómo podemos empezar a hacer un programa con esa lenguaje?
    • nerub val
      nerub val on 24 Apr 13
       
      Aqui se exponen otras alternativas al lenguaje R. Y aconseja en la ultima parte cuales son los recomendados para cada nivel de dominio del lenguaje R
  • Beginners can have a look at GNU PSPP or Gretl. Intermediate users can check out Stata. Advanced users who like matrix programming may prefer MATLAB or Octave. Very advanced users may use C or Fortran.
Enrique Diaz Romero

An Introduction to R - 1 views

stat.ethz.ch/...R-intro.html

shared by Enrique Diaz Romero on 24 Apr 13 - No Cached
    • nerub val
      nerub val on 24 Apr 13
       
      las carateristicas generales de R
    • nerub val
      nerub val on 24 Apr 13
       
      El desarrollo de R
  • 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.)
    • Enrique Diaz Romero
      Enrique Diaz Romero on 25 Jun 13
       
      El entorno de R entre otras cosas nos permite el uso y almacenamiento eficaz de datos, así como su análisis en forma de matrices, un gran numero de herramientas graficas y un lenguaje claro y sencillo de programación en el que podemos encontrar un gran numero de funciones.
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  • his section presumes the reader has some familiarity with statistical methodology, in particular with regression analysis and the analysis of variance. Later we make some rather more ambitious presumptions, namely that something is known about generalized linear models and nonlinear regression. The requirements for fitting statistical models are sufficiently well defined to make it possible to construct general tools that apply in a broad spectrum of problems. R provides an interlocking suite of facilities that make fitting statistical models very simple. As we mention in the introduction, the basic output is minimal, and one needs to ask for the details by calling extractor functions.
  • When you use the R program it issues a prompt when it expects input commands. The default prompt is ‘>’, which on UNIX might be the same as the shell prompt, and so it may appear that nothing is happening. However, as we shall see, it is easy to change to a different R prompt if you wish. We will assume that the UNIX shell prompt is ‘$’.
  • R can be regarded as an implementation of the S language which was developed at Bell Laboratories by Rick Becker, John Chambers and Allan Wilks, and also forms the basis of the S-Plus systems. The evolution of the S language is characterized by four books by John Chambers and coauthors. For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A. Becker, John M. Chambers and Allan R. Wilks. The new features of the 1991 release of S are covered in Statistical Models in S edited by John M. Chambers and Trevor J. Hastie. The formal methods and classes of the methods package are based on those described in Programming with Data by John M. Chambers. See References, for precise references. There are now a number of books which describe how to use R for data analysis and statistics, and documentation for S/S-Plus can typically be used with R, keeping the differences between the S implementations in mind. See What documentation exists for R?.
  • R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al. It provides a wide variety of statistical and graphical techniques
    • Enrique Diaz Romero
      Enrique Diaz Romero on 25 Jun 13
       
      ¿Que es R y de donde viene? R nos lo presentan como un lenguaje y entorno que desciende del reconocido sistema S desarrollado por los laboratorios Bell, y que nos ofrece una serie de técnicas u herramientas para el análisis estadístico y gráfico
  • Enrique Diaz Romero on 25 Jun 13
     
    Introducción completa al lenguaje R de programación para análisis de datos estadísticos.
Carlos Espinosa

Lenguaje R: ¿ - 1 views

  El Lenguaje R:   ¿Qué es R? R es un lenguaje y entorno de programación para análisis estadístico y gráfico. Se trata de un proyecto de software...

started by Carlos Espinosa on 30 Apr 13 no follow-up yet
LaRuPu upv

Statistical Computing with R: A tutorial - 0 views

math.illinoisstate.edu/...rtutor.html

shared by LaRuPu upv on 29 Apr 13 - Cached
  • R is a software package especially suitable for data analysis and graphical representation
  • is not particularly efficient in handling large data sets
  • Students can quickly grasp the role of outliers and influential points in a simple linear regression
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      No es eficiente manejando bases de datos grandes
  • ...9 more annotations...
  • R can be used as an ordinary calculator
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Fácil manejo, se puede interactuar con expresiones simples
  • > 2 + 3 * 5      # Note the order of operations. > log (10)       # Natural logarithm with base e=2.718282 > 4^2            # 4 raised to the second power > 3/2            # Division > sqrt (16)      # Square root > abs (3-7)      # Absolute value of 3-7 > pi             # The mysterious number > exp(2)         # exponential function > 15 %/% 4       # This is the integer divide operation > # This is a comment line
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Al igual que Matlab, puede usarse como una potente calculadora
  • x<- log(2.843432) *pi
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Ejemplos
  • R can handle complex numbers, too.
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Para fijar valores a variables, usamos '
  • Important note: since there are many built-in functions in R, make sure that the new object names you assign are not already used by the system. A simple way of checking this is to type in the name you want to use. If the system returns an error message telling you that such object is not found, it is safe to use the name. For example, c (for concatenate) is a built-in function used to combine elements so NEVER assign an object to c!
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      También admite el uso de números complejos
  • Character vector:
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Al igual que en los lenguajes de programación no puedes poner una variable con nombre de función, en R no puedes asignar una variable con nombre de objeto
  • Note that m1*m2 is NOT the usual matrix multiplication. To do the matrix multiplication, you should use %*% operator instead
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Vectores de caracteres
  • 2.4 Finding roots: a simple example
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      Para multiplicar matrices, usamos %*%
  • R is a software package especially suitable for data analysis and graphical representation. Functions and results of analysis are all stored as objects, allowing easy function modification and model building. R provides the language, tool, and environment in one convenient package. It is very flexible and highly customizable. Excellent graphical tools make R an ideal environment for EDA (Exploratory Data Analysis). Since most high level functions are written in R language itself, you can learn the language by studying the function code.
    • LuCla EdPa
      LuCla EdPa on 29 Apr 13
       
      La información que ya hemos adquirido es suficiente para tener una idea del funcionamiento de R, omitimos el resto
  • Rafa Llopis on 29 Apr 13
     
    Ejemplos gráficos y explicación matemática de lo que es capaz de hacer R
  • LaRuPu upv on 01 May 13
     
    uso de R para crear estadisticas y graficos en este caso en la industria del automovil.
Rafa Llopis

R aplicado a la psicología - 0 views

personality-project.org/r

psicología r Aplicaciones

shared by Rafa Llopis on 29 Apr 13 - No Cached
  • 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
    • Rafa Llopis
      Rafa Llopis on 29 Apr 13
       
      Esta web afirma que la mejor ventaja del lenguaje R es que es una comunidad abierta y que mejora constantemente.
  • 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.
    • Rafa Llopis
      Rafa Llopis on 29 Apr 13
       
      Uso del lenguaje R para estudios psicológicos.
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