R has an amazing variety of functions for cluster analysis. In this section, I will describe three of the many approaches: hierarchical agglomerative, partitioning, and model based. While there are no best solutions for the problem of determining the number of clusters to extract, several approaches are given below. Data Preparation Prior to clustering data, you may want to remove or estimate missing data and rescale variables for comparability. # Prepare Data mydata <- na.omit(mydata) # listwise deletion of missing mydata <- scale(mydata) # standardize variables Partitioning K-means clustering is the most popular partitioning method. # Determine number of clusters wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var)) for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") A robust version of K-means based on mediods can be invoked by using pam( ) instead of kmeans( ). Hierarchical Agglomerative
Related: R Resources
ChemWiki: The Dynamic Chemistry E-textbook - ChemwikiFactor AnalysisThis section covers principal components and factor analysis. The later includes both exploratory and confirmatory methods. Principal Components The princomp( ) function produces an unrotated principal component analysis. # Pricipal Components Analysis # entering raw data and extracting PCs # from the correlation matrix fit <- princomp(mydata, cor=TRUE) summary(fit) # print variance accounted for loadings(fit) # pc loadings plot(fit,type="lines") # scree plot fit$scores # the principal components biplot(fit) click to view Use cor=FALSE to base the principal components on the covariance matrix. The principal( ) function in the psych package can be used to extract and rotate principal components. # Varimax Rotated Principal Components # retaining 5 components library(psych) fit <- principal(mydata, nfactors=5, rotate="varimax") fit # print results mydata can be a raw data matrix or a covariance matrix. Exploratory Factor Analysis mydata can be a raw data matrix or a covariance matrix.
StatNotes: Topics in Multivariate Analysis, from North Carolina State UniversityLooking for Statnotes? StatNotes, viewed by millions of visitors for the last decade, has now been converted to e-books in Adobe Reader and Kindle Reader format, under the auspices of Statistical Associates Publishers. The e-book format serves many purposes: readers may cite sources by title, publisher, year, and (in Adobe Reader format) page number; e-books may be downloaded to PCs, Ipads, smartphones, and other devices for reference convenience; and intellectual property is protected against piracy, which had become epidemic. Click here to go to the new Statnotes website at . Or you may use the Google search box below to search the website, which contains free e-books and web pages with overview summaries and tables of contents. Or you may click on a specific topic below to view the specific overview/table of contents page.
Airbnb New User Bookings, Winner’s Interview: 3rd place: Sandro Vega PonsAirBnB New User Bookings was a popular recruiting competition that challenged Kagglers to predict the first country where a new user would book travel. This was the first recruiting competition on Kaggle with scripts enabled. AirBnB encouraged participants to prove their chops through their collaboration and code sharing in addition to their final models. Sandro Vega Pons took 3rd place, ahead of 1,462 other competitors, using an ensemble of GradientBoosting, MLP, a RandomForest, and an ExtraTreesClassifier. The Basics What was your background prior to entering this challenge? I currently work as a postdoctoral researcher at the NeuroInformatics Laboratory, FBK in Trento, Italy. How did you get started competing on Kaggle? I first heard about Kaggle around three years ago, when a colleague showed me the website. Sandro's top 8 finishes What made you decide to enter this competition? Let's Get Technical What preprocessing and supervised learning methods did you use? Fig. 1 Feature Engineering:
'r' tag wikikarthik/wesandersonWiki: Statistical MethodsBasic statistics help: Correspondence Analysis Factor Analysis Some nice explanations: KMO and Bartlett's Test of Sphericity (Factor Analysis) The Kaiser-Meyer-Olkin measure of sampling adequacy tests whether the partial correlations among variables are small. Path Analysis Structural Equation Modeling Software, including AMOS (which looks good, but kind of expensive): have been seeing several papers (both as a reviewer and as a reader of published work) that use AMOS for CFA, path analysis, or SEM models. Hi Matthew, Thanks very much for sending me the messages on the CRTNET listserv related to Amos. Up until version 4.02, when a model included means and intercepts as explicit model parameters, Amos used a different baseline model than most other SEM programs used in computing fit measures like NFI, NNFI, CFI, etc. Best regards, Jim Raftery, A. (1993). Raftery, A. (1995). Thanks for your input!
How To Perform A Logistic Regression In RLogistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values. Logistic regression implementation in R R makes it very easy to fit a logistic regression model. The dataset We’ll be working on the Titanic dataset. The data cleaning process When working with a real dataset we need to take into account the fact that some data might be missing or corrupted, therefore we need to prepare the dataset for our analysis. training.data.raw <- read.csv('train.csv',header=T,na.strings=c("")) Now we need to check for missing values and look how many unique values there are for each variable using the sapply() function which applies the function passed as argument to each column of the dataframe. data <- subset(training.data.raw,select=c(2,3,5,6,7,8,10,12))
D G Rossiter - Publications & Computer ProgramsRossiter, DG 2017. Technical note: Processing the Harmonized World Soil Database in R Version 1.4, 10-Aug-2017, 35 pp. Self-published online.Self-Organising Maps for Customer Segmentation using RSelf-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the greater Dublin area as an example data set. This work is based on a talk given to the Dublin R Users group in January 2014. If you are keen to get down to business: The slides from a talk on this subject that I gave to the Dublin R Users group in January 2014 are available here The code for the Dublin Census data example is available for download from here. SOMs were first described by Teuvo Kohonen in Finland in 1982, and Kohonen’s work in this space has made him the most cited Finnish scientist in the world. The SOM Grid SOM visualisation are made up of multiple “nodes”. SOM Heatmaps Typical SOM visualisations are of “heatmaps”. SOM Algorithm SOMs in R
Метод на най-малките квадрати » Физичен практикумЕксперименталните данни често се придружават от някакъв шум. Дори да успеем да постигнем точни и постоянни стойности на контролните величини, измерените резултантни величини винаги варират. Необходим е процес, известен като регресия или пасване на крива, за получаване количествена оценка на тенденцията на измерените експериментални величини. В процеса на пасване на крива се избира такава крива, която да дава добро приближение с експерименталните данни. Идеята на метода е проста. където са стойностите на контролната величина, са съответните измерени стойности на резултатната величина, а е избраната функционална зависимост, която трябва да бъде пасната. Тук ще се спрем на случая на линейна зависимост между една независима контролна величина и една резултатна величина, т.е. тя има вида: Ако формулираме по друг начин задачата си — трябва да прекараме права през набора от експериментални точки, така че сумата (1) да е минимална: Решавайки тази система, получаваме коефициентите на правата: