### Correspondence Analysis and Detrended Correspondence Analysis

Correspondence analysis (CA) is one of two names given to a method of ordination that was widely employed in the 70's and 80's. In one of the more interesting aspects of the history of vegetation science, it was soon determined that CA was independently developed by Hill in England (under the name reciprocal averaging) and by Benzecri in France (under the name analyse factorielle des corresponences). Legendre and Legendre (1998) trace its origins back farther than that outside of ecology.

CA can be calculated by a number of different algorithms. Due to the popularity of the analysis there are a number of different implementations of CA in R. A function called ca() is included in the mva library. The CoCoAn library includes CA as a special case of CCA (see Lab 10) in the function CAIV(), and the vegan library includes CA as a special case of DECORANA() or cca(see below). The method presented here is due to Legendre and Legendre (1998), who calculate CA as an eigenanalysis of a particular matrix.

Labs 6 and 7 describe eigenanalysis of a number of different matrices. PCA is specifically an eigenanalysis of a correlation or covariance matrix. PCO is an eigenanalysis of a broad variety of symmetric dissimilarity/distance matrices. CA is an eigenanalysis of a Chi-square distance matrix. A Chi-square distance matrix is defined by the deviation from expectation. Similar to the Chi-square statistic, it is defined as

(observed - expected) / sqrt(expected)
where observed and expected are abundances of species in sample plots. Species which occur in a sample plot in an abundance greater than expected have positive values, and species which occur less then expected or which are absent have negative values. Expected values are based on the row and column sums of the vegetation matrix.

In contrast to the ordination methods we have employed so far, CA provides a configuration for species as well as sample plots in the ordination. That is, species centroids (the hypothetical mode of their distribution along the eigenvectors) can be plotted as well as the locations of the plots. This dual nature is one of the characteristics that first attracted ecologists to CA.

We can perform CA much as we did PCA in Lab 6, using the vegetation matrix (as opposed to a dissimilarity/distance matrix). Using the LabDSV ca() function as shown.

ca.1 <- ca(veg)
CA has two conventions for plotting results, commonly called "scaling type 1" and "scaling type 2". In scaling type 1, the locations of the sample plots are calculated as the means of the species scores for species that occur in the plot, weighted by species abundance. Thus, species scores often lie outside the range of plot scores in the graph. In scaling type 2, species scores are calculated as the means of the plot scores in which the species occurs, weighted by its abundance in each plot. In this case, sample plots often lie outside the range of species scores. Thus, when plotting the ordination, you need to specify a "scaling." We can plot them with the LabDSV modification of the plot() function as shown.
plot(ca.1,scaling=1)

The LabDSV convention is to plot samples in black (as for PCA, PCO, and NMDS) and species in red (in a different symbol as well) as shown. CA is known to be sensitive to rare species and outlier plots, and what you see is an indication of that. One species (to be identified later) is an extreme outlier on the y axis, and since the x axis is scaled to maintain an aspect ratio of 1.0, condenses the plots and species along both axes. To identify the offending species, we can use the LabDSV specid() function. Similar to how we used identify() way back in Lab 1, specid() works similarly. Call specid() with an argument to specify the CA and the scaling, and optionally a vector of species names as follows.

specid(ca.1,scaling=1,names(veg))
[1] 6 14 33 72 75 155 156 157 160
Click on the graph near a point to identify, clicking mouse button 2 or 3 to quit when you're done. The function returns the column numbers in the veg matrix of the species you identify.

We can identify sample plots with the LabDSV plotid() function in the same way.

plotid(ca.1,scaling=1,site$labels) [1] 89 94 95 134 137 153 Programs like DECORANA (Hill and Gaugh, 1980) have options to "downweight" rare species to minimize the problem out species as outliers. One example of the problem is to identify the number of plots each species occurs in in the diagram. Back in Lab 1 we calculated the number of plots each species occurred in as spc.pres. Let's use that as the identifier. plot(ca.1,scaling=1) specid(ca.1,scaling=1,spc.pres)  Many (but not all) of the species lying at extremes of the cloud are species that only occur once or a few times. Accordingly, while ca() doesn't have a rare species downweighting function, we can leave them out all together, using logical subscripts (see Lab 1 for a refresher if necessary). ca.2 <- ca(veg[,spc.pres>=10]) This calculates a CA using only species that occur 10 or more times (that's 67 out of 169). Let's see what it looks like. plot(ca.2,scaling=1) The dispersion is a little better, but qualitatively it's pretty similar to before. Similar to PCA, PCO, and NMDS, there are LabDSV points() and surf() functions for CA as well. Often, you will want better dispersion of the sample plots before calculating the surface, and scaling type 2 will work better. For example, using elevation again plot(ca.1,scaling=2) surf(ca.1,elev,scaling=2)  Family: gaussian Link function: identity Formula: var ~ s(x) + s(y) Estimated degrees of freedom: 2.573253 4.742089 total = 8.315343 GCV score: 160641.8 D^2 = 0.6174  Because the CA plot program uses two colors already, the default for surf() for CA is to plot contours in green (col=3) to start. Despite the fact that the graph is not very interpretable visually, the D^2 square is not too bad (0.617 vs 0.870 for PCO). Surfaces for slope and aspect value (see Lab 8 are not very satisfactory either, despite reasonable D^2s. In fairness, the Bryce data set probably has too much beta-diversity for CA. Although less sensitive then PCA, CA also suffers when applied to high beta-diversity. ### Detrended Correspondence Analysis For a variety of reasons, including a tendency to produce "horseshoe" shaped plots, and a tendency to compress the ends of gradients, Hill and Gaugh (1980) developed a revised version of CA called "Detrended Correspondence Analysis", called DECORANA or DCA for short. Originally a stand-alone FORTRAN program, DECORANA was recently ported to R by Jari Oksanen. It is available as part of his vegan package. Accordingly, to use decorana you must first obtain and install vegan, and then load it. library(vegan) decorana() is a faithful port of DECORANA (actually uses Hill's original FORTRAN code). vegan has a different set of conventions than LabDSV, especially with respect to plotting, and it's probably just as well to cover them here. Oksanen is really a master with R, and his code reflects much greater finesse and a deeper immersion into R than LabDSV. It's indicative of R that there isn't just one way to do things, but that fairly strong conventions are beginning to emerge. To produce an ordination with vegan, you simply call an ordination function like you did with LabDSV, storing the result in an ordination object. To plot the ordination, you can simply plot is, again like LabDSV. Generally, however, you will want to store the (invisible) result of the plot in another variable. This variable has class "ordiplot," and stores all the information about the plot, such as which axes were plotted, and the coordinates of sites and species on just those axes, not all. This greatly simplifies subsequent labeling and surfacing, as the routines now magically know which axes were plotted, as well as some other information. Let me give an example using decorana library(vegan) # if you haven't already veg.dca <- decorana(veg) veg.dca.plt <- plot(veg.dca) # notice the assignment used with a plot function  Now, to identify species (for example) you use an identify function on the ordiplot object, specifying either "species" or "sites". identify(veg.dca.plt,'species') [1] 39 45 75 90 91 144 155 156 160 169 By default, the identify function labels in black, but to get the LabDSV convention of species in red, you could just do identify(veg.dca.plt,'species',col=2) With a little bit of practice, you'll get the hang of the vegan ordiplot convention, and find it quite helpful. If you want to, you can look directly at the ordiplot object. veg.dca.plt $sites
DCA1         DCA2
1   -1.80693591  0.516629756
2   -1.87728354  0.869333630
3   -1.67522711  0.855566371
4   -1.77212021  0.564803776
5   -1.83270809  0.509189062
6   -2.03194434  0.617906119
.         .            .
.         .            .
.         .            .
157 -0.42922861 -0.548282629
158 -0.76827770 -0.270540304
159 -0.21667241 -0.554282368
160 -0.35125690 -0.238909761

$species DCA1 DCA2 junost 2.90922011 1.025643152 ameuta -2.60907005 0.355392235 arcpat -2.27476065 0.343951025 arttri 3.45120077 -1.654125085 . . . . . . . . . towmin -0.05610011 1.706885447 tradub 3.08098986 0.606207632 valacu -0.78198398 1.189948147 vicame -3.03292127 1.625904397 attr(,"class") [1] "ordiplot"  ### Back to Ecology The dispersion appears much better than we obtained with CA. Unlike ca(), we don't have dual scalings for decorana; it calculates similar to scaling = 1, with species scores outside the plots. Let's see if some of the same species are near the edge of the cloud. veg.dca.plt <- plot(veg.dca) identify(veg.dca.plt,'species',col=2)   [1] 5 8 14 29 31 33 36 39 44 58 72 75 87 90 91 92 127 144 155 [20] 156 160 169  While the X axis has been stretched extraordinarily compared to CA, the species near the edge are still basically the same. Again, in contrast to ca(), decorana() DOES allow downweighting of rare species with the weigh=TRUE argument. Let's see if it helps. dca.2 <- decorana(veg,iweigh=TRUE) dca.2.plt <- plot(dca.2)  Unfortunately, the plots are even more congested than before, so it will be difficult to get interpretations of the configuration. Fortunately, we don't have to worry about this too much. the vegan library contains a function called ordisurf which is similar to our previous surf function from LabDSV. However, instead of overlaying the contours on the existing plot, it replots the sites and then puts on the contours. In our case, this effectively spreads out the points. attach(site) ## if you haven't already ordisurf(dca.2.plt,site)  Notice that we ordiplot the ordiplot object, not the decorana object. The default action is to draw a new plot, so to put a second set of contours on, use add=TRUE in your ordisurf command. > ordisurf(dca.2.plt,slope,col=3,add=TRUE) We can always resort to re-plotting by hand when the canned routines don't so what we want. The dca.2.plt ordiplot object has the plot scores stored as dca.2.plt$sites, so we can plot those on their own scale.


> plot(dca.2.plt$sites[,1],dca.2.plt$sites[,2],asp=1)


> ordisurf(dca.2,elev) Family: gaussian Link function: identity Formula: var ~ s(x) + s(y) Estimated degrees of freedom: 6.858568 5.219111 total = 13.07768 GCV score: 117732.2 D^2 = 0.7369

The D^2 of 0.740 is not too bad (better than we got for PCA and PCO with Euclidean or Manhattan distance, worse than PCO or NMDS with Bray/Curtis). Slope and aspect values (not shown) are also better than PCA or PCO with Euclidean distance, but worse than PCO with Bray/Curtis, but similar to NMDS.

While DECORANA was the ordination method of choice in the 70's and 80's, it was severely criticized for some of the "detrending" algorithms. While they were arguably inelegant (if not heavy-handed), empirically (as seen here) they were regarded as a significant improvement on CA. Part of the reason that the controversy died is that Canonical Correspondence Analysis (CCA) came along and rendered many of the arguments moot. CCA is presented in Lab 10.

### Functions Used In This Lab

#### ca()

This particular version of CA is implemented in pure S, and does not call any C or FORTRAN code. Accordingly it's a little slow, but can be used by anyone by simply cutting and pasting. In addition, the algorithm is easily understood (not hidden in a compiled program). The variable naming convention follows Legendre and Legendre (1998) in most parts.
ca <- function(x)
{
p <- as.matrix(x/sum(x))
rs <- as.vector(apply(p,1,sum))
cs <- as.vector(apply(p,2,sum))
cp <- rs %*% t(cs)
Qbar <- as.matrix((p - cp) / sqrt(cp))
Q.svd <- svd(Qbar)
V <- diag(1/sqrt(cs)) %*% Q.svd$v Vhat <- diag(1/sqrt(rs)) %*% Q.svd$u
F <- diag(1/rs) %*% p %*% V
Fhat <- diag(1/cs) %*% t(p) %*% Vhat
tmp <- list(V=V,Vhat=Vhat,F=F,Fhat=Fhat)
class(tmp) <- "ca"
return(tmp)
}


#### plot.ca()

plot.ca <-
function(ca,x=1,y=2,scaling=1,title="",pltpch=1,spcpch=3,pltcol=1,spccol=2,...)
{
if (scaling == 1) {
plot(ca$V[,x],ca$V[,y],type='n',asp=1,xlab=paste("CA",x),
ylab=paste("CA",y),main=title)
points(ca$F[,x],ca$F[,y],pch=pltpch,col=pltcol)
points(ca$V[,x],ca$V[,y],pch=spcpch,col=spccol)
} else {
plot(ca$Vhat[,x],ca$Vhat[,y],type='n',asp=1,xlab=paste("CA",x),
ylab=paste("CA",y),main=title)
points(ca$Vhat[,x],ca$Vhat[,y],pch=pltpch,col=pltcol)
points(ca$Fhat[,x],ca$Fhat[,y],pch=spcpch,col=spccol)
}
}


specid.ca <- function(ca, ids=seq(1:nrow(ca$V)), x=1, y=2, scaling=1, all=FALSE, col=2) { if(missing(ca)) { stop("You must specify a ca object from ca()") } if(scaling==1) { if(all) { text(ca$V[,x],ca$V[,y],ids,col=col) } else { identify(ca$V[,x],ca$V[,y],ids,col=col) } } else { if(all) { text(ca$Fhat[,x],ca$Fhat[,y],ids,col=col) } else { identify(ca$Fhat[,x],ca$Fhat[,y],ids,col=col) } } }  #### plotid.ca() plotid.ca <- function(ca, ids=seq(1:nrow(ca$F)),x = 1, y = 2, scaling=1, col = 1)
{
if(missing(ca)) {
stop("You must specify a ca object from ca()")
}
if(scaling==1) {
identify(ca$F[,x],ca$F[,y],ids)
} else {
identify(ca$Vhat[,x],ca$Vhat[,y],ids)
}
}


#### points.ca()

points.ca <- function(ca, logical, x = 1, y = 2, scaling=1, col = 3,
pch = "*", cex=1, ...)
{
if(missing(ca)) {
stop("You must specify a ca object from ca()")
}
if(missing(logical)) {
stop("You must specify a logical subscript")
}
if(scaling==1) {
points(ca$F[, x][logical], ca$F[, y][logical],
col=col,pch=pch,cex=cex)
} else {
points(ca$Vhat[, x][logical], ca$Vhat[, y][logical],
col=col,pch=pch,cex=cex)
}
}


#### surf.ca()

surf.ca <- function(ca, var, x=1, y=2, scaling=1, col=3, labcex=0.8,
family=gaussian, ...)
{
if(missing(ca)) {
stop("You must specify a ca object from ca()")
}
if(missing(var)) {
stop("You must specify a variable to surface")
}
if (scaling==1) {
x <- ca$F[,x] y <- ca$F[,y]
} else {
x <- ca$Vhat[,x] y <- ca$Vhat[,y]
}
if (is.logical(var)) {
tmp <- gam(var~s(x)+s(y),family=binomial)
} else {
tmp <- gam(var~s(x)+s(y),family=family)
}
print(tmp)
d2  <- (tmp$null.deviance-tmp$deviance)/tmp$null.deviance cat(paste("D^2 = ",formatC(d2,width=4),"\n")) }  An alternative ordisurf function that produces an D^2 statistic for comparison to other labdsv surf functions. ordisurf <- function (x, y, choices = c(1, 2), knots = 10, family = "gaussian", col = "red", thinplate = TRUE, add = FALSE, display = "sites", w = weights(x), main, nlevels = 10, levels, labcex = 0.6, ...) { GRID = 25 w <- eval(w) if (!is.null(w) && length(w) == 1) w <- NULL if (!require(mgcv)) stop("Requires package mgcv'") X <- scores(x, choices = choices, display = display, ...) x1 <- X[, 1] x2 <- X[, 2] if (thinplate) mod <- gam(y ~ s(x1, x2, k = knots), family = family, weights = w) else mod <- gam(y ~ s(x1, k = knots) + s(x2, k = knots), family = family, weights = w) d2 <- (mod$null.deviance - mod$deviance)/mod$null.deviance
cat(paste("D^2 = ", formatC(d2, width = 4), "\n"))
xn1 <- seq(min(x1), max(x1), len = GRID)
xn2 <- seq(min(x2), max(x2), len = GRID)
newd <- expand.grid(x1 = xn1, x2 = xn2)
fit <- predict(mod, type = "response", newdata = as.data.frame(newd))
poly <- chull(cbind(x1, x2))
poly <- c(poly, poly[1])
npol <- length(poly)
np <- nrow(newd)
inpoly <- numeric(np)
inpoly <- .C("pnpoly", as.integer(npol), as.double(x1[poly]),
as.double(x2[poly]), as.integer(np), as.double(newd[,
1]), as.double(newd[, 2]), inpoly = as.integer(inpoly),
PACKAGE = "vegan")\$inpoly
is.na(fit) <- inpoly == 0
plot(X, asp = 1, ...)
}
if (!missing(main) || (missing(main) && !add)) {
if (missing(main))
main <- deparse(substitute(y))
title(main = main)
}
if (missing(levels))
levels <- pretty(range(fit, finite = TRUE), nlevels)
contour(xn1, xn2, matrix(fit, nrow = GRID), col = col, add = TRUE,
levels = levels, labcex = labcex, drawlabels = !is.null(labcex) &&
labcex > 0)
return(mod)
}
`