# Chapter 13 Principal Component Analysis

Principal component analysis (PCA) was originally described by Pearson (1901) although it is more often attributed to Hotelling (1933) who proposed it independently. The method and several of its implications for data analysis are presented in the seminal paper of Rao (1964). PCA is used to generate a few key variables from a larger set of variables that still represent as much of the variation in the dataset as possible. PCA is a powerful technique to analyze quantitative descriptors (such as species abundances), but cannot be applied to binary data (such as species absence/presence).

Based on a dataset containing normally distributed variables, the first axis (or principal-component axis) of a PCA is the line that goes through the greatest dimension of the concentration ellipsoid describing the multinormal distribution. Similarly, the following axes go through the dimensions of the concentration ellipsoid by decreasing length. One can thus derive a maximum of p principal axes form a data set containing p variables.

For this, PCA rotates the original system of axes defined by the variables in a way that the successive new axes are orthogonal to one another and correspond to the successive dimensions of maximum variance of the scatter of points. The new variables produced in a PCA are uncorrelated with each other (axes are orthogonal to each other) and therefore may be used in other analyses such as multiple regression (Gotelli and Ellison 2004). The principal components give the positions of the objects in the new system of coordinates. PCA works on an association matrix among variables containing the variances and covariances of the variables. PCA preserves Euclidean distances and detects linear relationships. As a consequence, raw species abundance data are subjected to a pre-transformation (i.e. a Hellinger transformation) before computing a PCA.

To do a PCA you need: - A set of variables (with no distinction between independent or dependent variables, i.e. a set of species OR a set of environmental variables). - Samples that are measured for the same set of variables. - Generally a dataset that is longer than it is wide is preferred.

PCA is most useful with data with more than two variables, but is easier to describe using a two dimensional example. In this example (derived from Clarke and Warwick 2001) , we have nine sites with abundance data for two species:

Site Species 1 Species 2
A 6 2
B 0 0
C 5 8
D 7 6
E 11 6
F 10 10
G 15 8
H 18 14
I 14 14

If you plotted these samples in the two dimensions, the plot would look like this: This is a straight forward scatter plot and is an ordination plot, but you can imagine that it is more difficult to produce and visualize such a plot if there were more than two species. In that case, you would want to reduce the number of variables to principal components. If the 2D plot above was too complex and we wanted to reduce the data into a single dimension, we would be doing so with a PCA: In this case, the first principal component is the line of best fit through the points (sites) with the sites perpendicular to the line.

A second principal component is then added perpendicular to the first axis: The final plot then is the two PC axes rotated where the axes are now principal components as opposed to species: For PCAs with more than two variables, principal components are added like this (Clarke and Warwick 2001):

PC1 = axis that maximises the variance of the points that are projected perpendicularly onto the axis. PC2 = must be perpendicular to PC1, but the direction is again the one in which variance is maximised when points are perpendicularly projected. PC3 and so on: perpendicular to the first two axes. Essentially, when there are more than two dimensions, PCA produces a new space in which all PCA axes are orthogonal (e.g. where the correlation among any two axes is null) and where the PCA axes are ordered according to the percent of the variance of the original data they explain.

The “spe” data includes 27 fish taxa. To simplify the 27 fish taxa into a smaller number of fish-related variables or to identify where different sites or samples are associated with particular fish taxa we can run a PCA.

Run a PCA on Hellinger-transformed species data:

# Run the PCA using the rda() function (NB: rda() is used
# for both PCA and RDA)
spe.h.pca <- rda(spe.hel)

# Extract the results
summary(spe.h.pca)  #overall results

The summary looks like this:   Interpretation of ordination results:

A new word from this output is "eigenvalue". An eigenvalue is the value of the change in the length of a vector, and for our purposes is the amount of variation explained by each axis in a PCA. As you can see, the summary function produces a fair amount of information. From the summary we can see how much of the variance in the data is explained by the unconstrained variables (i.e. variables where we have made no effort to define relationships between the variables). In this case, the total variance of the sites explained by the species is 0.5. The summary also tells you what proportion of the total explained variance is explained by each principal component in the PCA: the first axis of the PCA thus explains 51.33% of the variation, and the second axis 12.78%. You can also extract certain parts of the output, try this:

summary(spe.h.pca, display = NULL)  # only eigenvalues and their contribution to the variance
eigen(cov(spe.hel))  # also compute the eigenvalues

Sometimes you may want to extract the scores (i.e. the coordinates within a PCA biplot) for either the “sites” (the rows in your dataset, whether they be actual sites or not) or the “species” (the variables in your data, whether they be actual species or some other variables). This is useful if you want to then use a principal component as a variable in another analysis, or to make additional graphics. For example, with the “spe” dataset, you might want to obtain a single variable that is a composite of all the fish abundance data and then use that use that variable in a regression with another variable, or plot across a spatial gradient. To extract scores from a PCA, use the scores() function:

spe.scores <- scores(spe.h.pca, display = "species", choices = c(1,
2))  # species scores on the first two PCA axes
site.scores <- scores(spe.h.pca, display = "sites", choices = c(1,
2))  # sites scores on the first two PCA axes
# Note: if you don’t specify the number of principal
# components to extract (e.g. choices=c(1,2) or
# choices=c(1:2) then all of the scores will be extracted
# for all of the principal components.

The PCA on the “spe” fish data produces as many principal components as there are fish taxon (columns), which in this case means that 27 principal components are produced. In many cases though, you may have done a PCA to reduce the number of variables to deal with and produce composite variables for the fish. In this case, you are likely interested in knowing how many of these principal components are actually significant or adding new information to the PCA (i.e. how many principal components do you need to retain before you aren’t really explaining any more variance with the additional principal components). To determine this, you can use the Kaiser-Guttman criterion and produce a barplot showing at what point the principal components are no longer explaining significant amount of variance. The code for the barplot below shows the point at which the variance explained by a new principal component explains less than the average amount explained by all of the eigenvalues:

# Identify the significant axis using the Kaiser-Guttman
# criterion
ev <- spe.h.pca$CA$eig
ev[ev > mean(ev)]
n <- length(ev)
barplot(ev, main = "Eigenvalues", col = "grey", las = 2)
abline(h = mean(ev), col = "red")
legend("topright", "Average eigenvalue", lwd = 1, col = 2, bty = "n") From this barplot, you can see that once you reach PC6, the proportion of variance explained falls below the average proportion explained by the other components. If you take another look at the PCA summary, you will notice that by the time you reach PC5, the cumulative proportion of variance explained by the principal components is 85%.

A PCA is not just for species data. It can also be run and interpreted in the same way using standardized environmental variables:

# Run the PCA
env.pca <- rda(env.z)  # or rda(env, scale=TRUE)

# Extract the results
summary(env.pca)
summary(env.pca, scaling = 2)

Scaling refers to what portion of the PCA is scaled to the eigenvalues. Scaling = 2 means that the species scores are scaled by eigenvalues, whereas scaling = 1 means that site scores are scaled by eigenvalues. Scaling = 3 means that both species and site scores are scaled symmetrically by square root of eigenvalues. Using scaling = 1 means that the Euclidean distances among objects (e.g. the rows in your data) are preserved, whereas scaling = 2 means that the correlations amongst descriptors (e.g. the columns in this data) are preserved. This means that when you look at a biplot of a PCA that has been run with scaling=2, the angle between descriptors represents correlation.

# Identify the significant axis using the Kaiser-Guttman
# criterion
ev <- env.pca$CA$eig
ev[ev > mean(ev)]
n <- length(ev)
barplot(ev, main = "Eigenvalues", col = "grey", las = 2)
abline(h = mean(ev), col = "red")
legend("topright", "Average eigenvalue", lwd = 1, col = 2, bty = "n")

Compare this eigenvalue barplot with the one you created for the species PCA.

As you saw in the explanation of the summary output, a lot of information can be extracted from a PCA before even plotting it. However, many of us interpret data best visually and a figure of a PCA is often the best way to convey major patterns. PCA biplots can be plotted in base R. A PCA biplot includes the x-axis as the first Principal Component and the y-axis as the second Principal Component.

For some plotting exercises, let's start with the species PCA. A basic biplot without any customization could be plotted like this, where the site positions are shown by black numbers and species’ positions are shown by red species codes. Remember, species positions come from plotting species along PCs and the site positions are derived from the weighted sums of species scores.

plot(spe.h.pca) Basically, the plot above plots the PCA like this:

plot(spe.h.pca, type = "n")  #produces a blank biplot with nothing displayed but the axes
points(spe.h.pca, dis = "sp", col = "blue")  #points are added for the species (columns) (dis=)
# use text() instead of points() if you want the labels
points(spe.h.pca, dis = "sites", col = "red")  #points are added for the sites (rows) To create more detailed plots and to play with aesthetics, try this code:

#Biplot of the PCA on transformed species data (scaling 1)
windows()
plot(spe.h.pca)
windows()
biplot(spe.h.pca)
windows()
plot(spe.h.pca, scaling=1, type="none", # scaling 1 = distance biplot :
# distances among objects in the biplot approximate their Euclidean distances
# but angles among descriptor vectors DO NOT reflect their correlation
xlab = c("PC1 (%)", round((spe.h.pca$CA$eig/sum(spe.h.pca$CA$eig))*100,2)), #this comes from the summary
ylab = c("PC2 (%)", round((spe.h.pca$CA$eig/sum(spe.h.pca$CA$eig))*100,2)))
points(scores(spe.h.pca, display="sites", choices=c(1,2), scaling=1),
pch=21, col="black", bg="steelblue", cex=1.2)
text(scores(spe.h.pca, display="species", choices=c(1), scaling=1),
scores(spe.h.pca, display="species", choices=c(2), scaling=1),
labels=rownames(scores(spe.h.pca, display="species", scaling=1)),
col="red", cex=0.8)  

This code produces 3 plots, the final plot is the most visually pleasing: What conclusions can you draw from this plot? From the plot, you can see the site scores shown by the blue dots. If you superimposed site labels on top, you could see what sites are closer to each other in terms of the species found at those sites, but even without the specific labels, you can see that there are only a few sites that are farther away from the majority. The species names are shown by their names in red and from the plot, you can see for example that the species "ABL" is not found or not found in the same prevalence in the majority of sites as other species closer to the centre of the ordination.

Biplots need to be interpreted in terms of angles from center of plot, not just in terms of proximity. Two variables at 90 degree angle are uncorrelated. Two variables in opposing directions are negatively correlated. Two variables very close together are strongly correlated, at least in the space described by these axes.

Now let's look at a plot of the environmental PCA:

#Biplot of the PCA on the environmental variables (scaling 2)
windows()
plot(env.pca)
windows()
plot(env.pca, scaling=2, type="none", # scaling 2 = correlation biplot :
# distances among abjects in the biplot DO NOT approximate their Euclidean distances
# but angles among descriptor vectors reflect their correlation
xlab = c("PC1 (%)", round((env.pca$CA$eig/sum(env.pca$CA$eig))*100,2)),
ylab = c("PC2 (%)", round((env.pca$CA$eig/sum(env.pca$CA$eig))*100,2)),
xlim = c(-1,1), ylim=c(-1,1))
points(scores(env.pca, display="sites", choices=c(1,2), scaling=2),
pch=21, col="black", bg="darkgreen", cex=1.2)
text(scores(env.pca, display="species", choices=c(1), scaling=2),
scores(env.pca, display="species", choices=c(2), scaling=2),
labels=rownames(scores(env.pca, display="species", scaling=2)),
col="red", cex=0.8) Remember that a PCA biplot is really just a scatter plot, where the axes are scores extracted from composite variables. This means that there are many different ways you can plot a PCA. For example, try using your ggplot() skills from Workshop 4 to extract PCA scores and plot an ordination in ggplot.

Use of PCA axis as composite explanatory variables:

In some cases, users want to reduce several environmental variables in a few numbers of composite variables. When PCA axes represent ecological gradients (i.e. when environmental variables are correlated with PCA axis in a meaningful way), users can use site scores along PCA Axis in further analyses instead of the raw environmental data. In other words, sites scores along PCA Axis represent linear combinations of descriptors and can consequently be used as proxy of ecological conditions in “post-ordination” analyses. In the example above, the first PCA axis can be related to a gradient from oligotrophic, oxygen-rich to eutrophic, oxygen-deprived water: from left to right, a first group display the highest values of altitude (alt) and slope (pen), and the lowest values in river discharge (deb) and distance from the source (das). The second group of sites has the highest values in oxygen content (oxy) and the lowest in nitrate concentration (nit). A third group shows intermediate values in almost all the measured variables. If users are then interested in identifying if a specific species is associated with ologotrophic or eutrophic water, one can correlate the species abundances to sites scores along PCA Axis 1. For example, if one want to assess if the species TRU prefers oligotrophic or eutrophic water, the following linear model can be used :

Sites_scores_Env_Axis1 <- scores(env.pca, display = "sites",
choices = c(1), scaling = 2)
spe$ANG plot(Sites_scores_Env_Axis1, spe$TRU)
summary(lm(spe$TRU ~ Sites_scores_Env_Axis1)) abline(lm(spe$TRU ~ Sites_scores_Env_Axis1))

This simple model shows that the abundance of species TRU is significantly dependent of site scores along PCA axis 1 (t = -5.30, p = 1.35e-05, adj-R2 = 49.22%), i.e. depends of an oligotrophic-eutrophic gradient. The following plot identifies a preference of this species for oligotrophic water.

Challenge 3 Run a PCA of the “mite” species abundance data. What are the significant axes of variation? Which groups of sites can you identify? Which species are related to each group of sites? Use:

mite.spe <- mite  #mite data is from the vegan package

Challenge 3 - Solution <hidden> Your code likely looks something like the following.

# Hellinger transformation of mite data and PCA
mite.spe.hel <- decostand(mite.spe, method = "hellinger")
mite.spe.h.pca <- rda(mite.spe.hel)

# What are the significant axes?
ev <- mite.spe.h.pca$CA$eig
ev[ev > mean(ev)]
n <- length(ev)
barplot(ev, main = "Eigenvalues", col = "grey", las = 2)
abline(h = mean(ev), col = "red")
legend("topright", "Average eigenvalue", lwd = 1, col = 2, bty = "n")

# Output summary/results
summary(mite.spe.h.pca, display = NULL)
windows()

# Plot the biplot
plot(mite.spe.h.pca, scaling = 1, type = "none", xlab = c("PC1 (%)",
round((mite.spe.h.pca$CA$eig/sum(mite.spe.h.pca$CA$eig)) *
100, 2)), ylab = c("PC2 (%)", round((mite.spe.h.pca$CA$eig/sum(mite.spe.h.pca$CA$eig)) *
100, 2)))
points(scores(mite.spe.h.pca, display = "sites", choices = c(1,
2), scaling = 1), pch = 21, col = "black", bg = "steelblue",
cex = 1.2)
text(scores(mite.spe.h.pca, display = "species", choices = c(1),
scaling = 1), scores(mite.spe.h.pca, display = "species",
choices = c(2), scaling = 1), labels = rownames(scores(mite.spe.h.pca,
display = "species", scaling = 1)), col = "red", cex = 0.8)

And your resulting biplot likely looks something like this: A dense cluster of related species and sites can be seen in the biplot. </hidden>