PCA on Chewing-gums

Load the data and the packages

With the following command

library(R4SPISE2022)
data("sixteenGums4Descriptors", package = "data4PCCAR")
chgu <- sixteenGums4Descriptors$ratingsIntensity
chguColors <- sixteenGums4Descriptors$color4Products
descColors <- sixteenGums4Descriptors$color4Descriptors

chgu: the intensities;
chguColors: colors for the chewing-gums;
descColors: colors for the descriptors.

More information on the dataset is available on the corresponding vignette: vignette("A2_DataChewingGums").

Do the PCA

res.pca <- epPCA(chgu, 
                 center = TRUE, # Center the data
                 scale = "ss1", # Scale the variables
                 graphs = FALSE)

The default of this function will center (to have means equal 0) and scale (to have the standard deviations equal 1; i.e., scale = TRUE) all variables. When we set scale = "ss1", the varaibles are scaled to have their sums of squares equal 1.

Plot the results

res.plot.pca <- OTAplot(
    resPCA = res.pca,
    data = chgu
    ) %>% suppressMessages()

results.stats: useful statistics after performing PCA, among which one can find the factor scores, loadings, eigenvalues, and contributions;
results.graphs: all the PCA graphs (heatmaps, screeplot, factor map, correlation circles, loading maps etc.)
description.graphs: the titles of these graphs, used as titles in the PPTX file.

Correlations of the variables

We can check the data by plotting the correlation matrix between the variables. This correlation matrix is where the components are extracted.

res.plot.pca$results.graphs$correlation

Scree plot

The scree plot shows the eigenvalues of each component. These eigenvalues give the variance of each component (or called dimension in the figure). In other words, the singular values, which are the square root of the eigenvalues, give the standard deviations of these components. The sum of the eigenvalues is equal to the total inertia of the data.

res.plot.pca$results.graphs$scree

According to an elbow test, there are 4 dimensions in this data; but, according to the Kaiser test (eigenvalue > 1), there is only 1 dimension.

Factor scores (rows)

Here, the observation map shows how the observations (i.e., rows) are represented in the component space. If center = TRUE when running PCA, the origin should be at the center of all observations. (If it’s not, something is off.)

res.plot.pca$results.graphs$factorScoresI12

Circle of correlation

The circle of correlations illustrate how the variables are correlated with each other and with the dimensions. From this figure, the length of an arrow indicates how much this variable is explained by the two given dimensions. The cosine between any two arrows gives their correlation. The cosine between a variable and an axis gives the correlation between that variable and the corresponding dimension.

In this figure, an angle closer to 0° indicates a correlation close to 1; an angle closer to 180° indicates a correlation close to -1; and an 90° angle indicates 0 correlation. However, it’s worth noted that this implication of correlation might only be true within the given dimensions. When a variable is far away from the circle, it is not fully explained by the dimensions, and other dimensions might be characterized by other pattern of relationship between this and other variables.

res.plot.pca$results.graphs$cosineCircleArrowJ12

Inference plots and results

The inference analysis of PCA (performed by OTAplotInference) includes bootstrap test of the proportion of variance explained, permutation tests of the eigenvalues, and the bootstrap tests of the column factor scores \(\mathbf{G}\) (i.e., the right singular vectors scaled to have the variance of each dimension equals the corresponding singular value; \(\mathbf{G} = \mathbf{Q}\boldsymbol{\Delta}\) with \(\mathbf{X = P}\boldsymbol{\Delta}\mathbf{Q}^\top\)). These column factor scores are stored as fj in the output of epPCA.

res.plot.pca.inference <- OTAplotInference(
    resPCA = res.pca,
    data = chgu
    )

Bootstrap and permutation tests on the eigenvalues

The inference scree plot illustrates the 95% bootstrap confidence intervals of the percentage of variance explained by each eigenvalue. If an interval includes 0, the component explains reliably larger than 0% of the variance.

res.plot.pca.inference$results.graphs$scree

The bootstrap test identifies three significant dimensions that explains the variance reliably larger than 0%. The permutation test identifies one significant dimension with an eigenvalue significantly larger than 0.

Bootstrap test on the column factor scores

The bar plot illustrates the bootstrap ratios which equals \[\frac{M_{g_{j}boot}}{SD_{g_{j}boot}},\] where \(M_{g_{j}boot}\) is the mean of the bootstrapped sample of the jth column factor score and \(SD_{g_{j}boot}\) is the bootstrapped standard deviation of the factor score. A bootstrap ratio is equivalent to a t-statistics for the column factor score with the \(\mathrm{H}_0: g_j = 0\). The threshold is set to 2 to approximate the critical t-value of 1.96 at \(\alpha\) = .05.

res.plot.pca.inference$results.graphs$BR1

res.plot.pca.inference$results.graphs$BR2

The results show no significant factor scores for both dimensions that is reliably different from 0.

Vincent Guillemot, Hervé Abdi, Ju-Chi Yu & Soudeh Ardestani Khoubrouy