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CRAN Task Views

Posted by Armando Brito Mendes | Filed under estatística, visualização

Uma lista de temas com uma descrição dos principais pacotes R ligados ao tema

CRAN Task Views

Bayesian	Bayesian Inference
ChemPhys	Chemometrics and Computational Physics
ClinicalTrials	Clinical Trial Design, Monitoring, and Analysis
Cluster	Cluster Analysis & Finite Mixture Models
DifferentialEquations	Differential Equations
Distributions	Probability Distributions
Econometrics	Econometrics
Environmetrics	Analysis of Ecological and Environmental Data
ExperimentalDesign	Design of Experiments (DoE) & Analysis of Experimental Data
Finance	Empirical Finance
Genetics	Statistical Genetics
Graphics	Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
HighPerformanceComputing	High-Performance and Parallel Computing with R
MachineLearning	Machine Learning & Statistical Learning
MedicalImaging	Medical Image Analysis
MetaAnalysis	Meta-Analysis
Multivariate	Multivariate Statistics
NaturalLanguageProcessing	Natural Language Processing
NumericalMathematics	Numerical Mathematics
OfficialStatistics	Official Statistics & Survey Methodology
Optimization	Optimization and Mathematical Programming
Pharmacokinetics	Analysis of Pharmacokinetic Data
Phylogenetics	Phylogenetics, Especially Comparative Methods
Psychometrics	Psychometric Models and Methods
ReproducibleResearch	Reproducible Research
Robust	Robust Statistical Methods
SocialSciences	Statistics for the Social Sciences
Spatial	Analysis of Spatial Data
SpatioTemporal	Handling and Analyzing Spatio-Temporal Data
Survival	Survival Analysis
TimeSeries	Time Series Analysis
WebTechnologies	Web Technologies and Services
gR	gRaphical Models in R

To automatically install these views, the ctv package needs to be installed, e.g., via
install.packages("ctv")
library("ctv")
and then the views can be installed via install.views or update.views (which first assesses which of the packages are already installed and up-to-date), e.g.,
install.views("Econometrics")
or
update.views("Econometrics")

Tags: data mining, R-software, software estatístico

Read more | Comments off | May 29th, 2015

Montes de recursos sobre R

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, software

Muitos recursos para o R que vão de exemplos introdutórios até ao multivariado.

Do it yourself Introduction to R R is a free statistical programming language environment. It is completely free to anyone — like the air you breath is free. For more information on why everyone should be using R, see here.

The goal of this site is to allow someone to overcome the intimidation associated with learning the very basics of R and showing them the tools for continued usage. Let’s get started. Some assumptions: This site assumes you are using a Windows operating system and have a basic understanding of file structures and paths. You will also need to have administrator privileges in order to install R. Some of the notes linked on this page are standard HTML pages; most of the links on this page are in R script file format (they have the file extension.R). Beyond that; the site and any instructions or links on it should be self-explanatory. It is STRONGLY recommended that one progress through the modules in order. A brief explanation of this page is here. UPDATE NOTE: April 23, 2015: current R version is 3.2.0 These pages have been tested for use with Firefox, other browsers may display the pages incorrectly.

Tags: análise de dados, data mining, Estat Descritiva, R-software, software estatístico

Read more | Comments off | May 29th, 2015

Using Open Source in Higher Education: R Tutorials

Posted by Armando Brito Mendes | Filed under estatística, software

Alguns bons tutoriais para aprender mais.

Recent Posts

• R Tutorial: A Simple Script to Create and Analyze a Data File, Part Two
• Book Review: R High Performance Programming
• R Tutorial: A Script to Create and Analyze a Simple Data File, Part One
• Nonparametric Statistical Analysis Using R: The Sign Test
• Book Review: Mastering Beaglebone Robotics

Recent Comments

	R Tutorial: A Script… on R Tutorial: A Script to Create…
	Rees Morrison on Using R for Basic Cross Tabula…

Archives

• February 2015
• August 2014
• June 2014

Categories

• Book Reviews
• R Tutorials
• Raspberrypi Computing
• Uncategorized

Tags: análise de dados, R-software, software estatístico

Read more | Comments off | March 6th, 2015

An Example of R Versatility

Posted by Armando Brito Mendes | Filed under estatística, software

By Dave Collingridge

In my last blog post I mentioned a few advantages to learning R. One of those advantages is that R opens up a world of new data analyses. There are novel techniques available in R that are not found in the ANALYZE drop down menus of SPSS, Stata, and Statistica. Novel techniques in R can be a big help in situations where data are not well-suited for traditional analyses like t-tests, ANOVA, and regression.

Tags: data mining, R-software

Read more | Comments off | February 6th, 2015

Base R Version

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, software, visualização

Excelentes exemplos de gráficos que podem usar nos trabalhos.

One Variable: Numeric Variable

One Variable: Factor Variable

Two Variables: Two Numeric Variables

Two Variables: Two Factor Variables

Two Variables: One Factor and One Numeric

Three Variables: Three Factor Variables

Three Variables: One Numeric and Two Factor Variables

Three Variables: Two Numeric and One Factor Variables

Three Variables: Three Numeric Variables

Scatterplot Matrix of all Numeric Vars, colored by a Factor variable

Tags: Estat Descritiva, R-software, software estatístico

Read more | Comments off | January 21st, 2015

SticiGui – online statistics book

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino

• Chapter 0, Preface.
• • Content, Philosophy, and Goals, Overview, Prerequisites, Design criteria and implementation, Advantages of Java over proprietary Statistics Packages, Suggestions for evaluating the materials, About the Author, Acknowledgments.
• Chapter 1, Introduction.
• • How to use these online materials
• Chapter 2, Reasoning and Fallacies.
• • Rules of reasoning, arguments, validity and soundness, some valid rules of reasoning, formal fallacies, common formal fallacies, informal fallacies, fallacies of relevance and fallacies of evidence, fallacies of relevance, common fallacies of relevance, fallacies of evidence, common fallacies of evidence, summary, key terms.
• Chapter 3, Statistics.
• • Introduction. Data: types of variables, sample data sets, frequency tables, histograms, skewness and modes, percentiles and quartiles, estimating percentiles from histograms, summary, key terms.
• Chapter 4, Measures of Location and Spread.
• • Measures of location: mean, median and mode; spread and variability, importance of variability, measures of spread: range, IQR and SD, affine transformations, Markov’s inequality and Chebychev’s inequality for lists, summary, key terms.
• Chapter 5, Multivariate Data and Scatterplots.
• • Multivariate data, scatterplots, describing scatterplots: linearity and nonlinearity, homoscedasticity and heteroscedasticity, outliers, summary, key terms.
• Chapter 6, Association.
• • Association, post hoc ergo propter hoc, summary, key terms.
• Chapter 7, Correlation and Association.
• • The correlation coefficient, the effect of nonlinear association, homoscedasticity and heteroscedasticity and outliers on the correlation coefficient, summary, key terms.
• Chapter 8, Computing the Correlation Coefficient.
• • computing the correlation coefficient, standard units, computing r, ecological correlation, summary, key terms.
• Chapter 9, Regression.
• • SD line, graph of averages, regression line, estimating using the regression line, the equation of the regression line, summary, key terms.
• Chapter 10, Regression Diagnostics.
• • Residuals and residual plots, reading residual plots, summary, key terms.
• Chapter 11, Errors in Regression.
• • The RMS error of regression, the distribution in a vertical slice through a scatterplot, the regression effect, the regression fallacy, summary, key terms.
• Chapter 12, Counting.
• • Counting can be hard, The Fundamental Rule of Counting, permutations, combinations, card hands, summary, key terms.
• Chapter 13, The Meaning of Probability: Theories of probability.
• • Theories of Probability, Random events, Equally Likely Outcomes, Frequency Theory, Subjective Theory, shortcomings of the theories, summary, key terms.
• Chapter 14, Set Theory: The Language of Probability.
• • Naive set theory, connecting probability to set theory, summary, key terms.
• Chapter 15, Categorical Logic.
• • Sets and categories, existential and universal quantifiers, categorical syllogisms, testing syllogisms, summary, key terms.
• Chapter 16, Propositional Logic.
• • Logical operations, evaluating compound propositions, logical arguments as compound propositions, valid arguments versus sound arguments, logic and sets, summary, key terms.
• Chapter 17, Probability: Axioms and Fundaments.
• • The axioms of probability, conditioning, the multiplication rule, Bayes’ rule, independence, summary, key terms.
• Chapter 18, The “Let’s Make a Deal” (Monty Hall) Problem.
• • Background, assumptions and arguments, assumptions and rules of the game, argument 1 (don’t switch—naive), argument 2 (don’t switch—conditional probability), argument 3 (switch—heuristic), argument 4 (switch—conditional probability), summary, key terms.
• Chapter 19, Probability Meets Data.
• • Introduction, a box model for the Let’s Make a Deal problem, the binomial probability distribution, dependence of the binomial on n and p, when the binomial does not apply, using the binomial distribution, continuation of the Let’s Make a Deal problem, summary, key terms.
• Chapter 20, Random Variables and Discrete Distributions.
• • Random variables, sampling from 0-1 boxes, geometric distribution, the negative binomial distribution, sampling without replacement, the hypergeometric distribution, calculating binomial, geometric, hypergeometric, and negative binomial probabilities, discrete distributions, case study: trade secret litigation, summary, key terms.
• Chapter 21, The Long Run and the Expected Value.
• • The Law of Large Numbers, implications of the law of large numbers, expected value of a random variable, expected value of the sample sum, expected value of binomial hypergeometric distributions, properties of the expected value, expected value of the sample mean and sample percentage, gambling and fair bets, expected values of some common distributions, summary, key terms.
• Chapter 22, Standard Error.
• • Expected value of a transformation of a random variable, standard error of random variables, the standard error transformations of a random variable, independent random variables, standard errors of some common random variables, the SE of a single draw from a box of numbered tickets, SE of the sample sum of n random draws with replacement from a Box of Tickets, the SE of the sample mean of n random draws from a box of numbered tickets, the square-root law, the law of averages, the standard error of the binomial, geometric and negative binomial distributions, SE of the sample sum and mean of a simple random sample, the SE of the hypergeometric distribution, the finite population correction, summary, key terms.
• Chapter 23, The Normal Curve, the Central Limit Theorem, and Markov’s and Chebychev’s Inequalities for Random Variables.
• • The normal approximation, standard units for random variables, the normal curve, the normal approximation to probability histograms, the continuity correction, the normal approximation to the hypergeometric distribution, Markov’s and Chebychev’s inequalities for random variables, summary, key terms.
• Chapter 24, Sampling.
• • Parameters and statistics, why sample?, sample surveys, The Hite Report, bias in surveys, Sampling designs: cluster sampling, stratified sampling, multistage sampling, hybrid designs, ways of drawing samples, convenience samples, quota samples, systematic samples, probability samples, simple random samples, systematic random samples, Sampling from hypothetical populations, summary, key terms.
• Chapter 25, Estimating Parameters from Simple Random Samples.
• • Quantifying the error of estimators: bias, standard error, and mean squared error, estimating means and percentages, a conservative estimate of the SE of the sample percentage, the Bootstrap estimate of the SD of a list of zeros and ones, the sample standard deviation and the sample variance, caveats, summary, key terms.
• Chapter 26, Confidence Intervals.
• • Confidence intervals, conservative confidence intervals for percentages, conservative confidence intervals for the mean of bounded populations, approximate confidence intervals for percentages, approximate confidence intervals for the population mean, exact confidence intervals for percentages, confidence intervals for the median and percentiles, summary.
• Chapter 27, Hypothesis Testing: Does Chance explain the Results?.
• • Hypothesis testing, Examples of hypothesis testing problems, significance level and power, test statistics and P-values, hypotheses about parameters; one-sided and two-sided alternatives, case study: employment discrimination, caveats, the meaning of rejection, statistical significance and practical importance, interpreting P-values, multiplicity and data mining, garbage in, garbage out, summary.
• Chapter 28, Does Treatment Have an Effect?.
• • The Method of Comparison, confounding, historical controls, longitudinal and cross-sectional comparisons, Simpson’s Paradox, experiments and observational studies, assessing online instructions, the Placebo Effect, John Snow’s study of the mode of communication of cholera, The Kassel Dowsing Experiment, summary.
• Chapter 29, Testing Equality of Two Percentages.
• • Fisher’s Exact Test for an effect–dependent samples, the normal approximation to Fisher’s Exact Test, testing equality of two percentages using independent samples, Fisher’s Exact Test using independent samples, the Z test for the equality of two percentages using independent Samples, the normal approximation to Fisher’s exact test and the z Test, summary, key terms.
• Chapter 30, Approximate Hypothesis Tests: the z Test and the t Test.
• • z Tests, P values for z tests, examples of z tests, z test for a population percentage, the z test for a population mean, z-test for a difference of population means (paired samples, independent samples), t tests, nearly normally distributed populations, Student’s t-curve, t test for the mean of a nearly normal population, hypothesis tests and confidence intervals, confidence intervals using Student’s t curve, summary, key terms
• Chapter 31, The Multinomial Distribution and the Chi-Squared Test for Goodness of Fit.
• • The multinomial distribution, the chi-square statistic, the sampling distribution of the chi-square statistic and the chi-square curve, the chi-square test of goodness of fit, summary, key terms.

Tags: Estat Descritiva, inferência

Read more | Comments off | January 19th, 2015

Spreadsheet Addiction

Posted by Armando Brito Mendes | Filed under estatística, materiais para profissionais

Um bom e muito completo relato dos defeitos do MS Excel para análise de dados.
Some people will think that the “addiction” in the title is over the top, or at least used metaphorically. It is used literally, and is not an exaggeration.

Addiction is the persistent use of a substance where that use is detrimental to the user. It is not the substance that is the problem — more limited use may be beneficial. It is the extent and circumstances of the use that determine if the behavior is addictive or not.

Spreadsheets are a wonderful invention. They are an excellent tool for what they are good at. The problem is that they are often stretched far beyond their home territory. Dangerous abuse of spreadsheets is only too common.

I know there are many spreadsheets in financial companies that take all night to compute. These are complicated and commonly fail. When such spreadsheets are replaced by code more suited to the task, it is not unusual for the computation time to be cut to a few minutes and the process much easier to understand.

A 2012 example of spreadsheet addiction.

The technology acceptance model holds that there are two main factors that determine the uptake of a technology: the perceived usefulness and the perceived ease-of-use. Perception need not correspond to reality.

The perception of the ease-of-use of spreadsheets is to some extent an illusion. It is dead easy to get an answer from a spreadsheet, however, it is not necessarily easy to get the right answer. Thus the distorted view.

The difficulty of using alternatives to spreadsheets is overestimated by many people. Safety features can give the appearance of difficulty when in fact these are an aid.

The hard way looks easy, the easy way looks hard.

The remainder of this page is divided into the sections:

Spreadsheet Computation
The Treatment Center (Alternatives)
If You Must Persist
Specific Problems with Excel
Additional Links

Tags: análise de dados, Excel, programação em folha de cálculo

Read more | Comments off | December 1st, 2014

Data Visualization with JavaScript

Posted by Armando Brito Mendes | Filed under estatística, SAD - DSS, software, visualização

Um bom e-book sobre como construir visualizações com JavaScript

It’s getting hard to ignore the importance of data in our lives. Data is critical to the largest social organizations in human history. It can affect even the least consequential of our everyday decisions. And its collection has widespread geopolitical implications. Yet it also seems to be getting easier to ignore the data itself. One estimate suggests that 99.5% of the data our systems collect goes to waste. No one ever analyzes it effectively.

Data visualization is a tool that addresses this gap.

Effective visualizations clarify; they transform collections of abstract artifacts (otherwise known as numbers) into shapes and forms that viewers quickly grasp and understand. The best visualizations, in fact, impart this understanding subconsciously. Viewers comprehend the data immediately—without thinking. Such presentations free the viewer to more fully consider the implications of the data: the stories it tells, the insights it reveals, or even the warnings it offers. That, of course, defines the best kind of communication.

If you’re developing web sites or web applications today, there’s a good chance you have data to communicate, and that data may be begging for a good visualization. But how do you know what kind of visualization is appropriate? And, even more importantly, how do you actually create one? Answers to those very questions are the core of this book. In the chapters that follow, we explore dozens of different visualizations and visualization techniques and tool kits. Each example discusses the appropriateness of the visualization (and suggests possible alternatives) and provides step-by-step instructions for including the visualization in your own web pages.

To give you a better idea of what to expect from the book, here’s a quick description of what the book is, and what it is not.

Tags: belo, data mining, desnvolvimento de software

Read more | Comments off | October 28th, 2014

F1Timeline

Posted by Armando Brito Mendes | Filed under estatística, visualização

mais uma excelente animação, neste caso com tudo sobre os pilotos da F1 desde tempos imemoriais.

Hi, I’m Peter Cook and I love turning data into insightful, beautiful and interactive works.

Tags: belo, data mining

Read more | Comments off | October 24th, 2014

Moving Past Default Charts

Posted by Armando Brito Mendes | Filed under estatística, visualização

Um excelente tutorial para aprender a trabalhar com os parâmetros dos gráficos em R.

Tags: belo, data mining, R-software, software estatístico

Read more | Comments off | October 23rd, 2014

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