Data, Covariance, and Correlation Matrix

1 Data, Covariance, and Correlation MatrixNathaniel E. HelwigAssistant Professor of Psychology and StatisticsUniversity of Minnesota (Twin Cities)Updated 16-Jan-2017 Nathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 1 CopyrightCopyrightc 2017 by Nathaniel E. HelwigNathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 2 Outline of Notes1) The Data MatrixDefinitionPropertiesR code2) The Covariance MatrixDefinitionPropertiesR code3) The Correlation MatrixDefinitionPropertiesR code4) Miscellaneous TopicsCrossproduct calculationsVec and KroneckerVisualizing dataNathaniel E.

2 Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 3 The Data MatrixThe Data MatrixNathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 4 The Data MatrixDefinitionThe Organization of DataThe data Matrix refers to the array of numbersX= x11x12 x1px21x22 x2px31x32 xnp wherexijis thej-th variable collected from thei-th item ( , subject).items/subjects are rowsvariables are columnsXis a data Matrix of ordern p(# items by # variables).Nathaniel E.

3 Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 5 The Data MatrixDefinitionCollection of Column VectorsWe can view a data Matrix as a collection of column vectors:X= x1x2 xp wherexjis thej-th column ofXforj {1,..,p}.Then 1 vectorxjgives thej-th variable s scores for E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 6 The Data MatrixDefinitionCollection of Row VectorsWe can view a data Matrix as a collection of row vectors:X= x 1x n wherex iis thei-th row ofXfori {1.}

4 ,n}.The 1 pvectorx igives thei-th item s scores for E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 7 The Data MatrixPropertiesCalculating Variable (Column) MeansThe sample mean of thej-th variable is given by xj=1nn i=1xij=n 11 nxjwhere1ndenotes ann 1 vector of onesxjdenotes thej-th column ofXNathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 8 The Data MatrixPropertiesCalculating Item (Row) MeansThe sample mean of thei-th item is given by xi=1pp j=1xij=p 1x i1pwhere1pdenotes anp 1 vector of onesx idenotes thei-th row ofXNathaniel E.

5 Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 9 The Data MatrixR CodeData Frame and Matrix Classes in R> data(mtcars)> class(mtcars)[1] " "> dim(mtcars)[1] 32 11> head(mtcars)mpg cyl disp hp drat wt qsec vs am gear carbMazda RX4 6 160 110 0 1 4 4 Mazda RX4 Wag 6 160 110 0 1 4 4 Datsun 710 4 108 93 1 1 4 1 Hornet 4 Drive 6 258 110 1 0 3 1 Hornet Sportabout 8 360 175 0 0 3 2 Valiant 6 225 105 1 0 3 1> X <- (mtcars)> class(X)

6 [1] " Matrix "Nathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 10 The Data MatrixR CodeRow and Column Means> # get row means (3 ways)> rowMeans(X)[1:3]Mazda RX4 Mazda RX4 Wag Datsun > c(mean(X[1,]), mean(X[2,]), mean(X[3,]))[1] > apply(X,1,mean)[1:3]Mazda RX4 Mazda RX4 Wag Datsun > # get column means (3 ways)> colMeans(X)[1:3]mpg cyl > c(mean(X[,1]), mean(X[,2]), mean(X[,3]))[1] > apply(X,2,mean)[1:3]mpg cyl E.

7 Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 11 The Data MatrixR CodeOther Row and Column Functions> # get column medians> apply(X,2,median)[1:3]mpg cyl > c(median(X[,1]), median(X[,2]), median(X[,3]))[1] > # get column ranges> apply(X,2,range)[,1:3]mpg cyl disp[1,] 4 [2,] 8 > cbind(range(X[,1]), range(X[,2]), range(X[,3]))[,1] [,2] [,3][1,] 4 [2,] 8 E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 12 The Covariance MatrixThe Covariance MatrixNathaniel E.

8 Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 13 The Covariance MatrixDefinitionThe Covariation of DataThe covariance Matrix refers to the symmetric array of numbersS= s21s12s13 s1ps21s22s23 s2ps31s32s23 s2p wheres2j= (1/n) ni=1(xij xj)2is the variance of thej-th variablesjk= (1/n) ni=1(xij xj)(xik xk)is the covariance between thej-th andk-th variables xj= (1/n) ni=1xijis the mean of thej-th variableNathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 14 The Covariance MatrixDefinitionCovariance Matrix from Data MatrixWe can calculate the covariance Matrix such asS=1nX cXcwhereXc=X 1n x =CXwith x = ( x1.)

9 , xp)denoting the vector of variable meansC=In n 11n1 ndenoting a centering matrixNote that the centered matrixXchas the formXc= x11 x1x12 x2 x1p xpx21 x1x22 x2 x2p xpx31 x1x32 x2 x3p x1xn2 x2 xnp xp Nathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 15 The Covariance MatrixPropertiesVariances are NonnegativeVariances are sums-of-squares, which implies thats2j 0 >0 as long as there does not exist an such thatxj= 1nThis implies that..tr(S) 0 where tr( )denotes the Matrix trace function pj=1 j 0 where( 1.

10 , p)are the eigenvalues ofSIfn<p, then j=0 for at least onej {1,..,p}. Ifn pand thepcolumns ofXare linearly independent, then j>0 for allj {1,..,p}.Nathaniel E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 16 The Covariance MatrixPropertiesThe Cauchy-Schwarz InequalityFrom the Cauchy-Schwarz inequality we have thats2jk s2js2kwith the equality holding if and only ifxjandxkare linearly could also write the Cauchy-Schwarz inequality as|sjk| sjskwheresjandskdenote the standard deviations of the E. Helwig (U of Minnesota)Data, Covariance, and Correlation MatrixUpdated 16-Jan-2017 : Slide 17 The Covariance MatrixR CodeCovariance Matrix by Hand (hard way)> n <- nrow(X)> C <- diag(n) - Matrix (1/n, n, n)> Xc <- C %*% X> S <- t(Xc) %*% Xc / (n-1)> S[1:3,1:6]mpg cyl disp hp drat wtmpg # or #> Xc <- scale(X, center=TRUE, scale=FALSE)> S <- t(Xc) %*% Xc / (n-1)> S[1:3,1.]