Simply put by one of its staunchest advocates, "R is the most powerful statistical computing language on the planet; there is no statistical equation that cannot be calculated in R." Beyond "just" a language, R is a toolset, a community, and a lot of free software. "Everyone can, with open source R," [...]
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By Gabriel Vasconcelos The complete subset regressions (CSR) is a forecasting method proposed by Elliott, Gargano and Timmermann in 2013. It is as very simple but powerful technique. Suppose you have a set of variables and you want to forecast … Continue reading →
This is part 3 of a multi-part guide on working with time series data in R. You can find the previous parts here: Part 1, Part 2. Generated data like
Welcome to Introduction to R for Data Science Session 6: Linear Regression + EDA, and Normality tests [Linear Regression in R: Exploratory Data Analysis, assumptions of the simple linear model, correlation, and visualization. Predictions from the linear model. Confidence Intervals and Residuals. Inspecting the basic linear model. Infulential cases and the Influence Plot.]The course is co-organized by Data Science Serbia and Startit. You will find all course material (R scripts, data sets, SlideShare presentations, readings) on these pages.Check out the Course Overview to acess the learning material presented thus far.Data Science Serbia Course Pages [in Serbian]Startit Course Pages [in Serbian]Lecturersdipl. ing Branko Kovač, Data Analyst at CUBE, Data Science Mentor at Springboard, Data Science Serbia Goran S. Milovanović, Phd, DataScientist@DiploFoundation, Data Science Serbia Summary of Session 6, 02. June 2016 :: Linear Regression + EDA, and Normality tests.Linear Regression + EDA and Normality tests. Linear Regression in R: Exploratory Data Analysis, assumptions of the simple linear model, correlation, and visualization. Predictions from the linear model. Confidence Intervals and Residuals. Inspecting the basic linear model. Infulential cases and the Influence Plot.Session 6 SlideShare Session 6 R Script Readings for Session 7 Intro to R for Data Science SlideShare :: Session 6 Introduction to R for Data Science :: Session 6 [Linear Regression in R] from Goran S. Milovanovic R script :: Session 6######################################################## # Introduction to R for Data Science # SESSION 6 :: 2 June, 2016 # Simple Linear Regression in R # Data Science Community Serbia + Startit # :: Goran S. Milovanović and Branko Kovač :: ######################################################## # clear rm(list=ls()) #### read data library(datasets) data(iris) ### iris data set description: # https://stat.ethz.ch/R-manual/R-devel/library/iriss/html/iris.html ### Exploratory Data Analysis (EDA) str(iris) summary(iris) ### EDA plots # plot layout: 2 x 2 par(mfcol = c(2,2)) # boxplot iris$Sepal.Length boxplot(iris$Sepal.Length, horizontal = TRUE, xlab="Sepal Length") # histogram: iris$Sepal.Length hist(iris$Sepal.Length, main="", xlab="Sepal.Length", prob=T) # overlay iris$Sepal.Length density function over the empirical distribution lines(density(iris$Sepal.Length), lty="dashed", lwd=2.5, col="red") # boxplot iris$Petal.Length boxplot(iris$Petal.Length, horizontal = TRUE, xlab="Petal Length") # histogram: iris$Petal.Length, hist(iris$Petal.Length, main="", xlab="Petal Length", prob=T) # overlay iris$Petal.Length density function over the empirical distribution lines(density(iris$Petal.Length), lty="dashed", lwd=2.5, col="red")Created by Pretty R at inside-R.org# NOTE: Boxplot "fences" and outlier detection # Boxplot in R recognizes as outliers those data points that are found beyond OUTTER fences # Source: http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm # Q3 = 75 percentile, Q1 = 25 percentile # IQ = Q3 - Q1; Interquartile range # lower inner fence: Q1 - 1.5*IQ # upper inner fence: Q3 + 1.5*IQ # lower outer fence: Q1 - 3*IQ # upper outer fence: Q3 + 3*IQ # A point beyond an inner fence on either side is considered a mild outlier # A point beyond an outer fence is considered an extreme outlier # plot variable density in general: Sepal Width # plot layout par(mfcol = c(1,2)) # NOTE: this is kernel density estimation in R. You are not testing any distribution yet. PLengthDensity
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Introduction Welcome to the second part of my introductory series on text analysis using R (the first article can be accessed here). My aim in the present piece is to provide a practical introduction to cluster analysis. I'll begin with some background before moving on to the nuts and bolts of clustering. We have a fair bit to…
As we embark on our journey into the realm of intermediate statistics, we'll discover how to wield the powerful tool of R, a versatile programming language, to navigate this wilderness with precision and insight.
Get the most appropriate answer to why learn R and check out major reasons to learn R programming language and know the importance of R for Data Science field.
With this introduction to R, you’ll master the basics of data analysis in R. You’ll learn how to manipulate data structures such as vectors and data frames.
[social4i size="large" align="float-right"] Guest post by Christopher Johnson from www.codeitmagazine.com One of the most powerful aspects of using R is that you can download free packages for so many tools and types of analysis. Text analysis is still somewhat in its infancy, but is very promising. It is estimated that as much as 80% of the
I'm working on a new R package to make it easier to forecast timeseries with the xgboost machine learning algorithm. So far in tests against large competition data collections (thousands of timeseries), it performs comparably to the nnetar neural network method, but not as well as more traditional timeseries methods like auto.arima and theta.
In previous blogs, we talked about citation measures as one metric for scientific productivity. Raw citation counts are inherently problematic – different fields cite at different rates, and citation counts rise and fall in the months to years after a publication appears. Therefore, a number of bibl
Recently I’ve worked with word2vec and doc2vec algorithms that I found interesting from many perspectives. Even though I used them for another purpose, the main thing they were developed for is Text analysis. As I noticed, my 2014 year’s article Twitter sentiment analysis is one of the most popular blog posts on the blog even today. […] The post Twitter sentiment analysis with Machine Learning in R using doc2vec approach appeared first on AnalyzeCore - data is beautiful, data is a story.
KDnuggets subscribers now have access to the WorldData.AI Partners Plan at no cost! Check out the world’s largest external curated data platform, integrating data from all leading global sources. Data Repositories Anacode Chinese Web Datastore: A collection of crawled Chinese news and blogs in JSON format Appen Open…
This is an introduction to support vector regression in R. It demonstrate how to train and tune a support vector regression model.
Today, I want to show how I use Thomas Lin Pederson’s awesome ggraph package to plot decision trees from Random Forest models. I am very much a visual person, so I try to plot as much of my results as possible because it helps me get a better feel f...
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Explore the process to install RStudio, R and R Packages along with their updation in different operating systems. Also, learn about the R Packages concept thoroughly.
R ggplot2 Histogram visualizes the statistical information organized in specified bins or ranges. Let's see how to Create R ggplot Histogram.
