Visualization of a correlation matrix with the Correlplot package

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Jan Graffelman - Universitat Politecnica de Catalunya; University of Washington

Jan de Leeuw - University of California Los Angeles

2024-01-23

Introduction

This documents gives some instructions on how to create graphical representations of a correlation matrix in the statistical environment R with package Correlplot, using a variety of different statistical methods (Graffelman and De Leeuw (2023)). We use principal component analysis (PCA), multidimensional scaling (MDS), principal factor analysis (PFA), weighted alternating least squares (WALS), correlograms (CRG) and corrgrams to produce displays of correlation structure. The next section shows how to use the functions of the package in order to create the different graphical representations, using both R base graphics and ggplot2 graphics (Wickham (2016)). The computation of goodness-of-fit statistics is also addressed. All methods are illustrated on a single data set, the wheat kernel data introduced below.

Graphical representations of a correlation matrix

We first load some packages we will use:

library(calibrate)
#> Loading required package: MASS
library(ggplot2)
library(corrplot)
#> corrplot 0.92 loaded
library(Correlplot)

Throughout this vignette, we will use the wheat kernel data set taken from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/seeds) in order to illustrate the different plots. The wheat kernel data (Charytanowicz et al. (2010)) consists of 210 wheat kernels, for which the variables area (\(A\)), perimeter (\(P\)), compactness (\(C = 4*\pi*A/P^2\)), length, width, asymmetry coefficient and groove (length of the kernel groove) were registered. There are 70 kernels of each of three varieties Kama, Rosa and Canadian; here we will only use the kernels of variety Kama. The data is made available with:

data("Kernels")
X <- Kernels[Kernels$variety==1,]
X <- X[,-8]
head(X)
#>    area perimeter compactness length width asymmetry groove
#> 1 15.26     14.84      0.8710  5.763 3.312     2.221  5.220
#> 2 14.88     14.57      0.8811  5.554 3.333     1.018  4.956
#> 3 14.29     14.09      0.9050  5.291 3.337     2.699  4.825
#> 4 13.84     13.94      0.8955  5.324 3.379     2.259  4.805
#> 5 16.14     14.99      0.9034  5.658 3.562     1.355  5.175
#> 6 14.38     14.21      0.8951  5.386 3.312     2.462  4.956

The correlation matrix of the variables is given by:

p <- ncol(X)
R <- cor(X)
round(R,digits=3)
#>               area perimeter compactness length  width asymmetry groove
#> area         1.000     0.976       0.371  0.835  0.900    -0.050  0.721
#> perimeter    0.976     1.000       0.165  0.921  0.802    -0.054  0.794
#> compactness  0.371     0.165       1.000 -0.146  0.667     0.037 -0.131
#> length       0.835     0.921      -0.146  1.000  0.551    -0.037  0.866
#> width        0.900     0.802       0.667  0.551  1.000    -0.027  0.447
#> asymmetry   -0.050    -0.054       0.037 -0.037 -0.027     1.000 -0.011
#> groove       0.721     0.794      -0.131  0.866  0.447    -0.011  1.000

1. The corrgram

The corrgram (Friendly (2002)) is a tabular display of the entries of a correlation matrix that uses colour and shading to represent correlations. Corrgrams can be made with the fuction corrplot

corrplot(R, method="circle",type="lower")

A corrgram of the wheat kernel data.

This shows most correlations are positive, and correlations with asymmetry are weak.

2. The correlogram

The correlogram (Trosset (2005)) represents correlations by the cosines of angles between vectors.

theta.cos <- correlogram(R,xlim=c(-1.1,1.1),ylim=c(-1.1,1.1),main="CRG")

The correlogram of the wheat kernel data.

The vector theta.cos contains the angles (in radians) of each variable with respect to the positive \(x\)-axis. The approximation provided by these angles to the correlation matrix is calculated by

Rhat.cor <- angleToR(theta.cos)

The correlogram always perfectly represents the correlations of the variables with themselves, and these have a structural contribution of zero to the loss function. We calculate the root mean squared error (RMSE) of the approximation by using function rmse. We include the diagonal of the correlation matrix in the RMSE calculation by supplying a weight matrix of only ones.

W1 <- matrix(1,p,p)
rmse.crg <- rmse(R,Rhat.cor,W=W1)
rmse.crg
#> [1] 0.2437535

This gives and RMSE of 0.2438, which shows this representation has a large amount of error. The correlogram can be modified by using a linear interpretation rule, rendering correlations linear in the angle (Graffelman (2013)). This representation is obtained by:

theta.lin <- correlogram(R,ifun="lincos",labs=colnames(R),xlim=c(-1.1,1.1),
                         ylim=c(-1.1,1.1),main="CRG")

Linear correlogram of the wheat kernel data.

The approximation to the correlation matrix by using this linear interpretation function is calculated by

Rhat.corlin <- angleToR(theta.lin,ifun="lincos")
rmse.lin <- rmse(R,Rhat.corlin,W=W1)
rmse.lin
#> [1] 0.1667556

and this gives a RMSE of 0.1668. In this case, the linear representation is seen to improve the approximation.

Function ggcorrelogram can be used to create correlograms on a ggplot2 canvas (Wickham (2016)). The output object contains the field theta containing the vector of angles.

set.seed(123)
crgR <- ggcorrelogram(R,main="CRG",vjust=c(0,2,-1,2,0,-1,2),
                     hjust=0)
crgR

Correlogram of the wheat kernel data on a ggplot2 canvas.

crgR$theta
#> [1]  0.0000000 -0.1476639  1.1635195 -0.4055100  0.3330992  1.5467130 -0.4710096

3. The PCA biplot of the correlation matrix

We create a PCA biplot of the correlation matrix, doing the calculations for a PCA by hand, using the singular value decomposition of the (scaled) standardized data. Alternatively, standard R function princomp may be used to obtain the coordinates needed for the correlation biplot. We use function bplot from package calibrate to make the biplot:

n <- nrow(X)
Xt <- scale(X)/sqrt(n-1)
res.svd <- svd(Xt)
Fs <- sqrt(n)*res.svd$u # standardized principal components
Gp <- res.svd$v%*%diag(res.svd$d) # biplot coordinates for variables
bplot(Fs,Gp,colch=NA,collab=colnames(X),xlab="PC1",ylab="PC2",main="PCA")

PCA biplot of the (standardized) wheat kernel data.

The joint representation of kernels and variables emphasizes this is a biplot of the (standardized) data matrix. However, this plot is a double biplot because scalar products between variable vectors approximate the correlation matrix. We stress this by plotting the variable vectors only, and adding a unit circle. In order to facilitate recovery of the approximations to the correlations between the variables, correlation tally sticks can be added as with the tally function, as is shown in the figure below:

bplot(Gp,Gp,colch=NA,rowch=NA,collab=colnames(X),xl=c(-1,1),yl=c(-1,1),main="PCA")
circle()
tally(Gp[-6,1:2],values=seq(-0.2,0.8,by=0.2))

PCA biplot of the correlation matrix with correlation tally sticks.

The PCA biplot of the correlation matrix can be obtained from a correlation-based PCA or also directly from the spectral decomposition of the correlation matrix. The rank two approximation, obtained by means of scalar products between vectors, is calculated by:

Rhat.pca <- Gp[,1:2]%*%t(Gp[,1:2])

In principle, PCA also tries to approximate the ones on the diagonal of the correlation matrix. These are included in the RMSE calculation by supplying a unit weight matrix.

rmse.pca <- rmse(R,Rhat.pca,W=W1)
rmse.pca
#> [1] 0.145959

This gives a RMSE of 0.1460, which is an improvement over the previous correlograms. Function rmse can also be used to calculate the RMSE of each variable separately:

rmse(R,Rhat.pca,W=W1,per.variable=TRUE)
#>       area       peri       comp       leng       widt       asym       groo 
#> 0.01429494 0.02168169 0.03158330 0.02386245 0.02047550 0.27686959 0.06000407

This shows that asymmetry, the shortest biplot vector, has the worst fit.

PCA biplots can also be made on a ggplot2 canvas using the function ggbplot. We redo the biplots of the data matrix and the correlation matrix. It is convenient first to establish the range of variation along both axes considering both row and column markers. We find these ranges using jointlim:

limits <- jointlim(Fs,Gp)
limits
#> $xlim
#> [1] -2.250838  2.529940
#> 
#> $ylim
#> [1] -2.650718  1.960994

Next, we prepare two dataframes, one with the coordinates and names for the rows, and one for the columns.

df.rows <- data.frame(Fs[,1:2])
colnames(df.rows) <- c("PA1","PA2")
df.rows$strings <- 1:n

df.cols <- data.frame(Gp[,1:2])
colnames(df.cols) <- c("PA1","PA2")
df.cols$strings <- colnames(R)

We compute the variance decomposition table:

lambda      <- res.svd$d^2
lambda.frac <- lambda/sum(lambda)
lambda.cum  <- cumsum(lambda.frac)
decom <- round(rbind(lambda,lambda.frac,lambda.cum),digits=3)
colnames(decom) <- paste("PC",1:p,sep="")
decom
#>               PC1   PC2   PC3   PC4   PC5   PC6   PC7
#> lambda      4.205 1.516 0.999 0.208 0.051 0.020 0.001
#> lambda.frac 0.601 0.217 0.143 0.030 0.007 0.003 0.000
#> lambda.cum  0.601 0.817 0.960 0.990 0.997 1.000 1.000

And use the table to construct axis labels that inform about the percentage of variance explained by each PC.

xlab <- paste("PC1 (",round(100*lambda.frac[1],digits=1),"%)",sep="")
ylab <- paste("PC2 (",round(100*lambda.frac[2],digits=1),"%)",sep="")

Finally, we construct the PCA biplot of the data matrix with ggbplot.

biplotX <- ggbplot(df.rows,df.cols,main="PCA biplot",xlab=xlab,
              ylab=ylab,xlim=limits$xlim,ylim=limits$ylim,
              colch="")
biplotX

PCA biplot on a ggplot canvas

The biplot of the correlation matrix is obtained by supplying the same biplot markers twice, once for the rows and once for the columns. Because the goodness-of-fit of the correlation matrix requires the squared eigenvalues (Gabriel (1971); Graffelman and De Leeuw (2023)), we first establish new labels for the axes:

lambdasq      <- lambda^2
lambdasq.frac <- lambdasq/sum(lambdasq)
lambdasq.cum  <- cumsum(lambdasq.frac)
decomsq <- round(rbind(lambdasq,lambdasq.frac,lambdasq.cum),
                 digits=3)
colnames(decomsq) <- paste("PC",1:p,sep="")
decomsq
#>                  PC1   PC2   PC3   PC4   PC5 PC6 PC7
#> lambdasq      17.682 2.299 0.997 0.043 0.003   0   0
#> lambdasq.frac  0.841 0.109 0.047 0.002 0.000   0   0
#> lambdasq.cum   0.841 0.950 0.998 1.000 1.000   1   1

xlab <- paste("PC1 (",round(100*lambdasq.frac[1],digits=1),"%)",sep="")
ylab <- paste("PC2 (",round(100*lambdasq.frac[2],digits=1),"%)",sep="")

biplotR <- ggbplot(df.cols,df.cols,main="PCA Correlation biplot",xlab=xlab,
              ylab=ylab,xlim=c(-1,1),ylim=c(-1,1),
              rowarrow=TRUE,rowcolor="blue",colch="",
              rowch="")

biplotR

PCA Correlation biplot on a ggplot canvas.

We now add correlation tally sticks to the biplot, in order to favour “reading off” the approximations of the correlations. Increments of 0.2 in the correlation scale are marked off along the biplot vectors. For the shortest biplot vector, asym, we use a modified scale with 0.01 increments. Note that the goodness-of-fit of the correlation matrix is (always) better or as good as the goodness-of-fit of the standardized data matrix (Graffelman and De Leeuw (2023)).

biplotR <- ggtally(Gp[-6,1:2],biplotR,values=seq(-0.2,0.8,by=0.2),dotsize=0.2)
biplotR <- ggtally(Gp[6,1:2],biplotR,values=seq(-0.01,0.01,by=0.01),dotsize=0.2)
biplotR

PCA Correlation biplot with correlation tally sticks.

4. The MDS map of a correlation matrix

We transform correlations to distances with the \(\sqrt{2(1-r)}\) transformation (Hills (1969)), and use the cmdscale function from the stats package to perform metric multidimensional scaling. We mark negative correlations with a dashed red line.

Di <- sqrt(2*(1-R))
out.mds <- cmdscale(Di,eig = TRUE)
Fp <- out.mds$points[,1:2]
opar <- par(bty = "l")
plot(Fp[,1],Fp[,2],asp=1,xlab="First principal axis",
     ylab="Second principal axis",main="MDS")
textxy(Fp[,1],Fp[,2],colnames(R),cex=0.75)
par(opar)

ii <- which(R < 0,arr.ind = TRUE)

for(i in 1:nrow(ii)) {
  segments(Fp[ii[i,1],1],Fp[ii[i,1],2],
           Fp[ii[i,2],1],Fp[ii[i,2],2],col="red",lty="dashed")
}

MDS map of the correlation matrix of the wheat kernel data.

We calculate distances in the map, and convert these back to correlations:

Dest <- as.matrix(dist(Fp[,1:2]))
Rhat.mds <- 1-0.5*Dest*Dest

Again, correlations of the variables with themselves are perfectly approximated (zero distance), and we include these in the RMSE calculations:

rmse.mds <- rmse(R,Rhat.mds,W=W1)
rmse.mds
#> [1] 0.06837469

The same MDS map can be made on a ggplot2 canvas with

colnames(Fp) <- paste("PA",1:2,sep="")
rownames(Fp) <- as.character(1:nrow(Fp))
Fp <- data.frame(Fp)
Fp$strings <- colnames(R)
MDSmap <- ggplot(Fp, aes(x = PA1, y = PA2)) + 
            coord_equal(xlim = c(-1,1), ylim = c(-1,1)) +
            ggtitle("MDS") +
            xlab("First principal axis") + ylab("Second principal axis") +
            theme(plot.title = element_text(hjust = 0.5, 
                                            size = 8),
                  axis.ticks = element_blank(),
                  axis.text.x = element_blank(),
                  axis.text.y = element_blank())
MDSmap <- MDSmap + geom_point(data = Fp, aes(x = PA1, y = PA2), 
                        colour = "black", shape = 1)
MDSmap <- MDSmap + geom_text(data = Fp, aes(label = strings), 
                        size = 3, alpha = 1,
                      vjust = 2) 

Z <- matrix(NA,nrow=nrow(ii),ncol=4)
for(i in 1:nrow(ii)) {
  Z[i,] <- c(Fp[ii[i,1],1],Fp[ii[i,1],2],Fp[ii[i,2],1],Fp[ii[i,2],2])
}
colnames(Z) <- c("X1","Y1","X2","Y2")
Z <- data.frame(Z)

MDSmap <- MDSmap + geom_segment(data=Z,aes(x=X1,y=Y1,
                                           xend=X2,yend=Y2),
                         alpha=0.75,color="red",linetype=2)   
MDSmap

MDS map on ggplot canvas.

5. The PFA biplot of a correlation matrix

Principal factor analysis can be performed by the function pfa of package Correlplot. We extract the factor loadings.

out.pfa <- Correlplot::pfa(X,verbose=FALSE)
L <- out.pfa$La

The biplot of the correlation matrix obtained by PFA is in fact the same as what is known as a factor loading plot in factor analysis, to which a unit circle can be added. The approximation to the correlation matrix and its RMSE are calculated as:

Rhat.pfa <- L[,1:2]%*%t(L[,1:2])
rmse.pfa <- rmse(R,Rhat.pfa)
rmse.pfa
#> [1] 0.01119688

In this case, the diagonal is excluded, for PFA explicitly avoids fitting the diagonal. To make the factor loading plot, aka PFA biplot of the correlation matrix:

bplot(L,L,pch=NA,xl=c(-1,1),yl=c(-1,1),xlab="Factor 1",ylab="Factor 2",main="PFA",rowch=NA,
      colch=NA)
circle()

PFA biplot of the correlation matrix of the wheat kernel data.

The RMSE of the plot obtained by PFA is 0.0112, lower than the RMSE obtained by PCA. Note that variable area reaches the unit circle for having a communality of 1, or, equivalently, specificity 0, i.e. PFA produces what is known as a Heywood case in factor analysis. The specificities are given by:

diag(out.pfa$Psi)
#>        area        peri        comp        leng        widt        asym 
#> 0.000000000 0.011494664 0.158097108 0.007885055 0.022509545 0.997799829 
#>        groo 
#> 0.258768379

We also construct the PFA biplot on a ggplot2 canvas with

L.df <- data.frame(L,rownames(L))
colnames(L.df) <- c("PA1","PA2","strings")
ggbplot(L.df,L.df,xlab="Factor 1",ylab="Factor 2",main="PFA biplot",
        rowarrow=TRUE,rowcolor="blue",colch="",rowch="")

PFA biplot on a ggplot canvas.

6. The WALS biplot of a correlation matrix

The correlation matrix can also be factored using weighted alternating least squares, avoiding the fit of the ones on the diagonal of the correlation matrix by assigning them weight 0, using function ipSymLS (De Leeuw (2006)).

Wdiag0 <- matrix(1,nrow(R),nrow(R))
diag(Wdiag0) <- 0
Fp.als <- ipSymLS(R,w=Wdiag0,eps=1e-15)

bplot(Fp.als,Fp.als,rowch=NA,colch=NA,collab=colnames(R),
      xl=c(-1.1,1.1),yl=c(-1.1,1.1),main="WALS")
circle()

WALS biplot of the correlation matrix of the wheat kernel data.

Weighted alternating least squares has, in contrast to PFA, no restriction on the vector length. If the vector lengths in the biplot are calculated, then variable area is seen to just move out of the unit circle.

Rhat.wals <- Fp.als%*%t(Fp.als)
sqrt(diag(Rhat.wals))
#> [1] 1.00124368 0.99394213 0.91345321 0.99646265 0.99026217 0.04686397 0.86124152
rmse.als <- rmse(R,Rhat.wals)
rmse.als
#> [1] 0.01118619

The RMSE of the approximation obtained by WALS is 0.011186, slightly below the RMSE of PFA. The WALS low rank approximation to the correlation matrix can also be obtained by the more generic function wAddPCA, which allows for non-symmetric matrices and adjustments (see the next section). In order to get uniquely defined biplot vectors for each variable, corresponding to symmetric input, an eigendecomposition is applied to the fitted correlation matrix.

out.wals <- wAddPCA(R, Wdiag0, add = "nul", verboseout = FALSE, epsout = 1e-10)
Rhat.wals <- out.wals$a%*%t(out.wals$b)
out.eig <- eigen(Rhat.wals)
Fp.adj <- out.eig$vectors[,1:2]%*%diag(sqrt(out.eig$values[1:2]))
rmse.als <- rmse(R,Rhat.wals)
rmse.als
#> [1] 0.01118619

We also make the WALS biplot on a ggplot2 canvas with

Fp.als.df <- data.frame(Fp.als,colnames(R))
colnames(Fp.als.df) <- c("PA1","PA2","strings")
ggbplot(Fp.als.df,Fp.als.df,xlab="Dimension 1",ylab="Dimension 2",main="WALS biplot",
        rowarrow=TRUE,rowcolor="blue",colch="",rowch="")

WALS biplot of the correlation matrix of the wheat kernel data.

7. The WALS biplot using a scalar adjustment of the correlation matrix

A scalar adjustment can be employed to improve the approximation of the correlation matrix, and the adjusted correlation matrix is factored for a biplot representation. That means we seek the factorization

\[ {\mathbf R} - \delta {\mathbf J} = {\mathbf R}_a = {\mathbf G} {\mathbf G}', \]

where both \(\delta\) and \(\mathbf G\) are chosen such that the (weighted) residual sum of squares is minimal. This problem is solved by using function wAddPCA.

out.wals <- wAddPCA(R, Wdiag0, add = "one", verboseout = FALSE, epsout = 1e-10)
delta <- out.wals$delta[1,1]
Rhat <- out.wals$a%*%t(out.wals$b)
out.eig <- eigen(Rhat)
Fp.adj <- out.eig$vectors[,1:2]%*%diag(sqrt(out.eig$values[1:2]))

The optimal adjustment \(\delta\) is 0.071. The corresponding biplot is shown below.

bplot(Fp.adj,Fp.adj,rowch=NA,colch=NA,collab=colnames(R),
      xl=c(-1.2,1.2),yl=c(-1.2,1.2),main="WALS adjusted")
circle()

WALS biplot of the correlation matrix of the wheat kernel data, with the use of an additive adjustment.

Note that, when calculating the fitted correlation matrix, adjustment \(\delta\) is added back. The fitted correlation matrix and its RMSE are now calculated as:

Rhat.adj <- Fp.adj%*%t(Fp.adj)+delta
rmse.adj <- rmse(R,Rhat.adj)
rmse.adj
#> [1] 0.005560242

This gives RMSE 0.0056. This is smaller than the RMSE obtained by WALS without adjustment. We summarize the values of the RMSE of all methods in a table below:

rmsevector <- c(rmse.crg,rmse.lin,rmse.pca,rmse.mds,rmse.pfa,rmse.als,rmse.adj)
methods <- c("CRG (cos)","CRG (lin)","PCA","MDS",
"PFA","WALS R","WALS Radj")
results <- data.frame(methods,rmsevector)
results <- results[order(rmsevector),]
results
#>     methods  rmsevector
#> 7 WALS Radj 0.005560242
#> 6    WALS R 0.011186190
#> 5       PFA 0.011196885
#> 4       MDS 0.068374694
#> 3       PCA 0.145959040
#> 2 CRG (lin) 0.166755585
#> 1 CRG (cos) 0.243753461

A summary of RMSE calculations for four methods (PCA and WALS, both with and without \(\delta\) adjustment) can be obtained by the functions FitRwithPCAandWALS and rmsePCAandWALS; the first applies the four methods to the correlation matrix, and the latter calculates the RMSE statistics for the four approximations. The bottom line of the table produced by rmsePCAandWALS gives the overall RMSE for each methods.

output <- FitRwithPCAandWALS(R,eps=1e-15)
rmsePCAandWALS(R,output)
#>         PCA  PCA-A   WALS WALS-A
#> area 0.0143 0.0542 0.0081 0.0043
#> peri 0.0217 0.0405 0.0088 0.0064
#> comp 0.0316 0.0590 0.0110 0.0054
#> leng 0.0239 0.0485 0.0067 0.0045
#> widt 0.0205 0.0626 0.0076 0.0068
#> asym 0.2769 0.1182 0.0181 0.0049
#> groo 0.0600 0.0675 0.0135 0.0061
#> All  0.1460 0.0706 0.0112 0.0056

The scalar adjustment changes the interpretation of the origin, and it is convenient to show the zero correlation point on each biplot vector. We do so by creating tally sticks, redoing the biplot on a ggplot2 canvas. The origin now represents correlation 0.07 for all variables.

Fp.adj.df <- data.frame(Fp.adj,colnames(R))
colnames(Fp.adj) <- c("PA1","PA2")
colnames(Fp.adj.df) <- c("PA1","PA2","strings")
WALSbiplot.adj <- ggbplot(Fp.adj.df,Fp.adj.df,xlab="Dimension 1",ylab="Dimension 2",
                          main="WALS adjusted",rowarrow=TRUE,
                          rowcolor = "blue",rowch="",colch="")
WALSbiplot.adj <- ggtally(Fp.adj[-6,1:2],WALSbiplot.adj,
                          adj=-out.wals$delta[1,1],
                   values=seq(-0.2,0.8,by=0.2),dotsize=0.2)
WALSbiplot.adj

WALS biplot of the correlation matrix of the wheat kernel data, with additive adjustment and tally sticks.

8. The WALS biplot using column adjustment of the correlation matrix

We generate an asymmetric approximation to the correlation matrix using a column adjustment, based on the decomposition

\[ {\mathbf R} = \delta {\mathbf 1} {\mathbf 1}' + {\mathbf 1} {\mathbf q}' + {\mathbf G} {\mathbf G}' + {\mathbf E}. \]

This decomposition can be obtained with function FitDeltaQSym.

out.walsq.sym <- FitRDeltaQSym(R,Wdiag0,itmax.inner=30000,itmax.outer=30000,
                     eps=1e-8)
Gq.sym <- out.walsq.sym$C
rownames(Gq.sym) <- colnames(R)
Rhat.wsym <- out.walsq.sym$Rhat
rmse.wsym <- rmse(R,Rhat.wsym)
rmse.wsym
#> [1] 0.00540069

This approximation as a RMSE of 0.0054, a very minor improvement in comparison with using a single scalar adjustment.

The corresponding biplot can be obtained with

Gq.sym.df <- data.frame(Gq.sym)
Gq.sym.df$strings <- colnames(R)
colnames(Gq.sym.df) <- c("PA1","PA2","strings")
ll <- 1.5
WALSbiplotq.sym <- ggbplot(Gq.sym.df,Gq.sym.df,main="WALS-q-sym biplot",xlab="Dimension 1",
                           ylab="Dimension 2",
                           ylim=c(-ll,ll),xlim=c(-ll,ll),circle=TRUE,
                           rowarrow=TRUE,rowcolor="blue",rowch="",
                           colch="")
for(i in c(1:5,7)) {
   WALSbiplotq.sym <- ggtally(Gq.sym[i,1:2],WALSbiplotq.sym,
                              adj=-out.walsq.sym$delta-out.walsq.sym$q[i], 
              values=seq(-0.2,1,by=0.2),dotsize=0.2)  
}
WALSbiplotq.sym

WALS biplot of the correlation matrix with column adjustment and tally sticks.

This biplot is similar in appearance to the previous biplot that uses a single scalar adjustment only. However, in principle it no longer has a unique origin, as the origin represents a (slightly) different correlation for each variable:

out.walsq.sym$delta + out.walsq.sym$q
#> [1] 0.07779402 0.07332050 0.07528872 0.07546010 0.07847079 0.07575909 0.07854010

For this data, a single scalar adjustment seems preferable.

References

Charytanowicz, M., J. Niewczas, P. Kulczycki, P. A. Kowalski, S. Lukasik, and S. Zak. 2010. “A Complete Gradient Clustering Algorithm for Features Analysis of x-Ray Images.” In Information Technologies in Biomedicine, edited by Ewa Pietka and Jacek Kawa, 15–24. Berlin-Heidelberg: Springer-Verlag.

De Leeuw, J. 2006. “A Decomposition Method for Weighted Least Squares Low-Rank Approximation of Symmetric Matrices.” Department of Statistics, UCLA. https://escholarship.org/uc/item/1wh197mh.

Friendly, M. 2002. “Corrgrams: Exploratory Displays for Correlation Matrices.” The American Statistician 56 (4): 316–24. https://doi.org/10.1198/000313002533.

Gabriel, K. R. 1971. “The Biplot Graphic Display of Matrices with Application to Principal Component Analysis.” Biometrika 58 (3): 453–67.

Graffelman, J. 2013. “Linear-Angle Correlation Plots: New Graphs for Revealing Correlation Structure.” Journal of Computational and Graphical Statistics 22 (1): 92–106. https://doi.org/10.1080/15533174.2012.707850.

Graffelman, J., and J. De Leeuw. 2023. “Improved Approximation and Visualization of the Correlation Matrix.” The American Statistician, 1–20. https://doi.org/10.1080/00031305.2023.2186952.

Hills, M. 1969. “On Looking at Large Correlation Matrices.” Biometrika 56 (2): 249–53.

Trosset, M. W. 2005. “Visualizing Correlation.” Journal of Computational and Graphical Statistics 14 (1): 1–19. https://doi.org/10.1198/106186005X27004.

Wickham, H. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.