# 411 search results for "PCA"

## PCA for NIR Spectra_part 005: "Reconstruction"

February 27, 2012
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We saw how to plot the raw spectra (X), how to calculate the mean spectrum, how to center the sprectra (subtracting the mean spectrum from every spectra of the original matrix X). After that we have developed the PCAs with the NIPALS algorithm, getting...

## PCA for NIR Spectra_part 004: "Projections"

February 26, 2012
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This plot in 2D, help us to decide the number of PCs, it is easy to create in R, once we have discompose the X matrix into a P matrix (loadings) and a T matrix (scores).For this plot, we just need the T matrix.> CPs<-seq(1,10,by=1)>  matp...

## PCA for NIR Spectra_part 003: "NIPALS"

February 25, 2012
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> X<-yarn\$NIR> X_nipals<-nipals(X,a=10,it=100)Two matrices are generated (P and T)As in other posts, we are going to look to the loadings & scores, for firsts three principal components:> wavelengths<-seq(1,268,by=1)> matplot(w...

## PCA for NIR Spectra_part 002: "Score planes"

February 23, 2012
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The idea of this post is to compare the score plots for the first 3 principal components obtained with the algorithm “svd” with the scores plot of  other chemometric software (Win ISI in this case). Previously I had exported the yarn spectra t...

February 22, 2012
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There are different algorithms to calculate the Principal Components (PCs). Kurt Varmuza & Peter Filzmozer explain  them in their book: “Introduction to Multivariate Statistical Analysis in Chemometrics”.I´m going to apply one of them, to...

## Modelling returns using PCA : Evidence from Indian equity market

December 26, 2011
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As my finance term paper, I investigated an interesting question where I tried to identify macroeconomic variables that explain the returns on equities. Much of the debate has already taken place on this topic which has given rise to two competing theo...

## PCA file calculation with "R".

December 5, 2011
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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...

## Big-Data PCA: 50 years of stock data

June 17, 2011
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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...

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

September 16, 2010
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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

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

August 3, 2010
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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...