302 search results for "PCA"

PCA file calculation with "R".

December 5, 2011
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PCA file calculation with "R".

X es la matriz centrada (X is the centered matrix). Xcov es la matriz de covarianzas de X (Xcov is the covariance matrix of X).Con la función "eigen" calculamos los "eigenvectors" y "eigenvalues" de Xcov.(With the function "eigen" we calculate the "ei...

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Big-Data PCA: 50 years of stock data

June 17, 2011
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Big-Data PCA: 50 years of stock data

In this post, Revolution engineer Sherry LaMonica shows us how to use the RevoScaleR big-data package in Revolution R Enterprise to do principal components analysis on 50 years of stock market data -- ed. Principal components analysis, or PCA, seeks to find a set of orthogonal axes such that the first axis, or first principal component, accounts for as...

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Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation

September 16, 2010
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Principal Component Analysis (PCA) vs Ordinary Least Squares (OLS): A Visual Explanation

Over at stats.stackexchange.com recently, a really interesting question was raised about principal component analysis (PCA). The gist was “Thanks to my college class I can do the math, but what does it MEAN?” I felt like this a number of times in my life. Many of my classes were focused on the technical implementations they kinda

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Using R and r.mapcalc (GRASS) to Estimate Mean Topographic Curvature

August 3, 2010
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Using R and r.mapcalc (GRASS) to Estimate Mean Topographic Curvature

Recently I was re-reading a paper on predictive soil mapping (Park et al, 2001), and considered testing one of their proposed terrain attributes in GRASS. The attribute, originally described by Blaszczynski (1997), is the distance-weighted mean differe...

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Tutorial: Principal Components Analysis (PCA) in R

May 20, 2010
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Found this tutorial by Emily Mankin on how to do principal components analysis (PCA) using R. Has a nice example with R code and several good references. The example starts by doing the PCA manually, then uses R's built in prcomp() function to do the s...

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Compcache on Ubuntu on Amazon EC2

May 4, 2010
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Compcache on Ubuntu on Amazon EC2

The following fully-automatic Bash script downloads, compiles, and initializes compcache version 0.6.2 on Ubuntu Karmic Koala (9.10). This script creates two swaps with a maximum of 4GB uncompressed size each. Two swaps are used to take advantage of 2 CPUs (or CPU cores in a multicore CPU). Compcache is a fascinating memory compression system. The

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Modern Honey Network Machinations with R, Python, phantomjs, HTML & JavaScript

August 23, 2015
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Modern Honey Network Machinations with R, Python, phantomjs, HTML & JavaScript

This was (initially) going to be a blog post announcing the new mhn R package (more on what that is in a bit) but somewhere along the way we ended up taking a left turn at Albuquerque (as we often do here at ddsec hq) and had an adventure in a twisty maze of Modern Honey Network...

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5 New R Packages for Data Scientists

August 20, 2015
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5 New R Packages for Data Scientists

by Joseph Rickert One great beauty of the R ecosystem, and perhaps the primary reason for R’s phenomenal growth, is the system for contributing new packages. This, coupled to the rock solid stability of CRAN, R’s primary package repository, gives R a great advantage. However, anyone with enough technical knowhow to formulate a proper submission can contribute a package...

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R News From JSM 2015

August 13, 2015
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R News From JSM 2015

by Joseph Rickert We can declare 2015 the year that R went mainstream at the JSM. There is no doubt about it, the calculations, visualizations and deep thinking of a great many of the world's statisticians are rendered or expressed in R and the JSM is with the program. In 2013 I was happy to have stumbled into a...

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Matrix Factorization Comes in Many Flavors: Components, Clusters, Building Blocks and Ideals

August 6, 2015
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Matrix Factorization Comes in Many Flavors: Components, Clusters, Building Blocks and Ideals

Unsupervised learning is covered in Chapter 14 of The Elements of Statistical Learning. Here we learn about several data reduction techniques including principal component analysis (PCA), K-means clustering, nonnegative matrix factorization (NMF) ...

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