MCA: Multiple-descriptors, Multiscale Codependence Analysis

In guenardg/codep: Multiscale Codependence Analysis

Multiple-descriptors, Multiscale Codependence Analysis

Description

Class, Functions, and methods to perform Multiscale Codependence Analysis (MCA)

Usage

MCA(Y, X, emobj)

test.cdp(object, alpha = 0.05, max.step, response.tests = TRUE)

permute.cdp(object, permute, alpha = 0.05, max.step, response.tests = TRUE)

parPermute.cdp(
  object,
  permute,
  alpha = 0.05,
  max.step,
  response.tests = TRUE,
  nnode,
  seeds,
  verbose = TRUE,
  ...
)

Arguments

Y	A numeric matrix or vector containing the response variable(s).
X	A numeric matrix or vector containing the explanatory variable(s).
emobj	A eigenmap-class object.
object	A cdp-class object.
alpha	The type I (alpha) error threshold used by the testing procedure.
max.step	The maximum number of steps to perform when testing for statistical significance.
response.tests	A boolean specifying whether to test individual response variables.
permute	The number of random permutations used for testing. When omitted, the number of permutations is calculated using function minpermute.
nnode	The number of parallel computation nodes.
seeds	Seeds for computation nodes' random number generators when using parallel computation during the permutation test.
verbose	Whether to return user notifications.
...	Parameters to be passed to function parallel::makeCluster

Details

Multiscale Codependence Analysis (MCA) allows to calculate correlation-like (i.e.codependence) coefficients between two variables with respect to structuring variables (Moran's eigenvector maps). The purpose of this function is limited to parameter fitting.

Test procedures are handled through test.cdp (parametric testing) or permute.cdp (permutation testing). Moreover, methods are provided for printing (print.cdp), displaying a summary of the tests (summary.cdp), plotting results (plot.cdp), calculating fitted (fitted.cdp) and residuals values (redisuals.cdp), and making predictions (predict.cdp).

It is noteworthy that the test procedure used by MCA deviates from the standard R workflow since intermediate testing functions (test.cdp and permute.cdp) need first to be called before any testing be performed.

Function parPermute.cdp allows the user to spread the number of permutation on many computation nodes. It relies on package parallel. Omitting argument nnode lets function parallel::detectCores specify the number of node. Similarly, omitting parameter seeds lets the function define the seeds as a set of values drawn from a uniform random distribution between with minimum value -.Machine$integer.max and maximum value .Machine$integer.max.

Value

A cdp-class object.

Functions

• MCA(): Main function to compute the multiscale codependence analysis

• test.cdp(): Parametric statistical testing for multiscale codependence analysis

• permute.cdp(): Permutation testing for multiscale codependence analysis.

• parPermute.cdp(): Permutation testing for multiscale codependence analysis using parallel processing.

Author(s)

Guillaume Guenard and Pierre Legendre, Bertrand Pages Maintainer: Guillaume Guenard <[e-mail]>

References

Guénard, G., Legendre, P., Boisclair, D., and Bilodeau, M. 2010. Multiscale codependence analysis: an integrated approach to analyse relationships across scales. Ecology 91: 2952-2964

Guénard, G. Legendre, P. 2018. Bringing multivariate support to multiscale codependence analysis: Assessing the drivers of community structure across spatial scales. Meth. Ecol. Evol. 9: 292-304

Examples

### Example 1: St. Marguerite River Salmon Transect
data(salmon)

## Converting the data from data frames to to matrices:
Abundance <- log1p(as.matrix(salmon[,"Abundance",drop = FALSE]))
Environ <- as.matrix(salmon[,3L:5])

## Creating a spatial eigenvector map:
map1 <- eigenmap(x = salmon[,"Position"], weighting = wf.binary,
                 boundaries = c(0,20))

## Case of a single descriptor:
mca1 <- MCA(Y = Abundance, X = Environ[,"Substrate",drop = FALSE],
            emobj = map1)
mca1
mca1_partest <- test.cdp(mca1)
mca1_partest
summary(mca1_partest)
par(mar = c(6,4,2,4))
plot(mca1_partest, las = 3, lwd=2)
mca1_pertest <- permute.cdp(mca1)
## Not run: 
## or:
mca1_pertest <- parPermute.cdp(mca1, permute = 999999)

## End(Not run)
mca1_pertest
summary(mca1_pertest)
plot(mca1_pertest, las = 3)
mca1_pertest$UpYXcb$C # Array containing the codependence coefficients

## With all descriptors at once:
mca2 <- MCA(Y = log1p(as.matrix(salmon[,"Abundance",drop = FALSE])),
            X = as.matrix(salmon[,3L:5]), emobj = map1)
mca2
mca2_partest <- test.cdp(mca2)
mca2_partest
summary(mca2_partest)
par(mar = c(6,4,2,4))
plot(mca2_partest, las = 3, lwd=2)
mca2_pertest <- permute.cdp(mca2)
## Not run: 
## or:
    mca2_pertest <- parPermute.cdp(mca2, permute = 999999)

## End(Not run)
mca2_pertest
summary(mca2_pertest)
plot(mca2_pertest, las = 3, lwd=2)
mca2_pertest$UpYXcb$C # Array containing the codependence coefficients
mca2_pertest$UpYXcb$C[,1L,] # now turned into a matrix.

### Example 2: Doubs Fish Community Transect

data(Doubs)

## Sites with no fish observed are excluded:
excl <- which(rowSums(Doubs.fish) == 0)

## Creating a spatial eigenvector map:
map2 <- eigenmap(x = Doubs.geo[-excl,"DFS"])
## The eigenvalues are in map2$lambda, the MEM eigenvectors in matrix map2$U

## MCA with multivariate response data analyzed on the basis of the Hellinger
## distance:
Y <- LGTransforms(Doubs.fish[-excl,],"Hellinger")

mca3 <- MCA(Y = Y, X=Doubs.env[-excl,], emobj = map2)
mca3_pertest <- permute.cdp(mca3)
## Not run: 
## or:
mca3_pertest <- parPermute.cdp(mca3, permute = 999999)

## End(Not run)

mca3_pertest
summary(mca3_pertest)
par(mar = c(6,4,2,4))
plot(mca3_pertest, las = 2, lwd=2)

## Array containing all the codependence coefficients:
mca3_pertest$UpYXcb$C

## Display the results along the transect
spmeans <- colMeans(Y)
pca1 <- svd(Y - rep(spmeans, each=nrow(Y)))
par(mar = c(5,5,2,5) + 0.1)
plot(y = pca1$u[,1L], x = Doubs.geo[-excl,"DFS"], pch = 21L, bg = "red",
     ylab = "PCA1 loadings", xlab = "Distance from river source (km)")

## A regular transect of sites from 0 to 450 (km) spaced by 1 km intervals
## (451 sites in total). It is used for plotting spatially-explicit
## predictions.

x <- seq(0,450,1)
newdists <- matrix(NA, length(x), nrow(Doubs.geo[-excl,]))
for(i in 1L:nrow(newdists))
  newdists[i,] <- abs(Doubs.geo[-excl,"DFS"] - x[i])

## Calculating predictions for the regular transect under the same set of
## environmental conditions from which the codependence model was built.
prd1 <- predict(mca3_pertest,
                newdata = list(target = eigenmap.score(map2, newdists)))

## Projection of the predicted species abundance on pca1:
Uprd1 <-
  (prd1 - rep(spmeans, each = nrow(prd1))) %*%
  pca1$v %*% diag(pca1$d^-1)
lines(y = Uprd1[,1L], x = x, col=2, lty = 1)

## Projection of the predicted species abundance on pca2:
plot(y = pca1$u[,2L], x = Doubs.geo[-excl,"DFS"], pch = 21L, bg = "red",
     ylab = "PCA2 loadings", xlab = "Distance from river source (km)")
lines(y = Uprd1[,2L], x = x, col = 2, lty = 1)

## Displaying only the observed and predicted abundance for Brown Trout.
par(new = TRUE)
plot(y = Y[,"TRU"], Doubs.geo[-excl,"DFS"], pch = 21L,
     bg = "green", ylab = "", xlab = "", new = FALSE, axes = FALSE)
axis(4)
lines(y = prd1[,"TRU"], x = x, col = 3)
mtext(side = 4, "sqrt(Brown trout rel. abundance)", line = 2.5)

### Example 3: Borcard et al. Oribatid Mite

## Testing the (2-dimensional) spatial codependence between the Oribatid Mite
## community structure and environmental variables, while displaying the
## total effects of the significant variables on the community structure
## (i.e., its first principal component).

data(mite)

map3 <- eigenmap(x = mite.geo)

Y <- LGTransforms(mite.species, "Hellinger")

## Organize the environmental variables
mca4 <- MCA(Y = Y, X = mite.env, emobj = map3)
mca4_partest <- test.cdp(mca4, response.tests = FALSE)
summary(mca4_partest)
plot(mca4_partest, las = 2, lwd = 2)
plot(mca4_partest, col = rainbow(1200)[1L:1000], las = 3, lwd = 4,
     main = "Codependence diagram", col.signif = "white")

## Making a regular point grid for plotting the spatially-explicit
## predictions:
rng <- list(
  x = seq(min(mite.geo[,"x"]) - 0.1, max(mite.geo[,"x"]) + 0.1, 0.05),
  y = seq(min(mite.geo[,"y"]) - 0.1, max(mite.geo[,"y"]) + 0.1, 0.05))
grid <- cbind(x = rep(rng[["x"]], length(rng[["y"]])),
              y = rep(rng[["y"]], each = length(rng[["x"]])))
newdists <- matrix(NA, nrow(grid), nrow(mite.geo))
for(i in 1L:nrow(grid)) {
  newdists[i,] <- ((mite.geo[,"x"] - grid[i,"x"])^2 +
                     (mite.geo[,"y"] - grid[i,"y"])^2)^0.5
}

spmeans <- colMeans(Y)
pca2 <- svd(Y - rep(spmeans, each = nrow(Y)))

prd2 <- predict(mca4_partest,
                newdata = list(target = eigenmap.score(map3, newdists)))
Uprd2 <-
  (prd2 - rep(spmeans, each = nrow(prd2))) %*%
  pca2$v %*% diag(pca2$d^-1)

## Printing the response variable (first principal component of the mite
## community structure).
prmat <- Uprd2[,1L]
dim(prmat) <- c(length(rng$x), length(rng$y))
zlim <- c(min(min(prmat), min(pca2$u[,1L])), max(max(prmat),
                                                 max(pca2$u[,1L])))
image(z = prmat, x = rng$x, y = rng$y, asp = 1, zlim = zlim,
      col = rainbow(1200L)[1L:1000], ylab = "y", xlab = "x")
points(
  x=mite.geo[,"x"], y=mite.geo[,"y"], pch=21L,
  bg = rainbow(1200L)[round(1+(999*(pca2$u[,1L]-zlim[1L])/(zlim[2L]-zlim[1L])),0)])

guenardg/codep documentation built on April 16, 2024, 9:01 p.m.