ecodist (version 2.1.3)

pmgram: Piecewise multivariate correlogram

Description

This function calculates simple and partial piecewise multivariate correlograms.

Usage

pmgram(data, space, partial, breaks, nclass, stepsize, equiprobable = FALSE, 
  resids = FALSE, nperm = 1000)

Value

Returns a object of class mgram, which is a list containing two objects: mgram is a matrix with one row for each distance class and 4 columns:

lag

midpoint of the distance class.

ngroup

number of distances in that class.

piecer or Iwt

Mantel r value or appropriate statistic (see Details).

pval

two-sided p-value.

resids is a vector of the residuals (if calculated) and can be accessed with the residuals() method.

Arguments

data

lower-triangular dissimilarity matrix. This can be either an object of class dist (treated as one column) or a matrix or data frame with one or two columns, each of which is an independent lower-triangular dissimilarity in vector form.

space

lower-triangular matrix of geographic distances.

partial

optional, lower-triangular dissimilarity matrix of ancillary data.

breaks

locations of class breaks. If specified, overrides nclass and stepsize.

nclass

number of distance classes. If not specified, Sturge's rule will be used to determine an appropriate number of classes.

stepsize

width of each distance class. If not specified, nclass and the range of space.d will be used to calculate an appropriate default.

equiprobable

if TRUE, create nclass classes of equal number of distances; if FALSE, create nclass classes of equal width

resids

if resids=TRUE, will return the residuals for each distance class. Otherwise returns 0.

nperm

number of permutations to use. If set to 0, the permutation test will be omitted.

Author

Sarah Goslee

Details

The standard Mantel correlogram calculated by mgram tests the hypothesis that the mean compositional dissimilarity within a distance class differs from the mean of all the other distance classes combined. This function instead produces a piecewise correlogram by testing the relationship between dissimilarities within each distance class on its own, without reference to relationships across other distance classes.

This function does four different analyses: If data has 1 column and partial is missing, calculates a multivariate correlogram for data.

If data has 2 columns and partial is missing, calculates a piecewise Mantel cross-correlogram, calculating the Mantel r between the two columns for each distance class separately.

If data has 1 column and partial exists, calculates a partial multivariate correlogram based on residuals of data ~ partial.

If data has 2 columns and partial exists, does a partial Mantel cross-correlogram, calculating partial Mantel r for each distance class separately.

The Iwt statistic used for the multivariate correlograms is not the standard Mantel r. For one variable, using Euclidean distance, this metric converges on the familiar Moran autocorrelation. Like the Moran autocorrelation function, this statistic usually falls between -1 and 1, but is not bounded by those limits. Unlike the Moran function, this correlogram can be used for multivariate data, and can be extended to partial tests.

The Mantel r is used for piecewise cross-correlograms.

The comparisons in vignette("dissimilarity", package="ecodist") may help.

See Also

mgram, mantel, residuals.mgram, plot.mgram

Examples

data(bump)

par(mfrow=c(1, 2))
image(bump, col=gray(seq(0, 1, length=5)))

z <- as.vector(bump)
x <- rep(1:25, times=25)
y <- rep(1:25, each=25)

X <- col(bump)
Y <- row(bump)
# calculate dissimilarities for data and space
geo.dist <- dist(cbind(as.vector(X), as.vector(Y)))
value.dist <- dist(as.vector(bump))

### pmgram() is time-consuming, so this was generated
### in advance and saved.
### set.seed(1234)
### bump.pmgram <- pmgram(value.dist, geo.dist, nperm=10000)

data(bump.pmgram)
plot(bump.pmgram)

#### Partial pmgram example

# generate a simple surface
# with complex nonlinear spatial pattern

x <- matrix(1:25, nrow=25, ncol=25, byrow=FALSE)
y <- matrix(1:25, nrow=25, ncol=25, byrow=TRUE)

# create z1 and z2 as functions of x, y
# and scale them to [0, 1]
z1 <- x + 3*y
z2 <- y - cos(x)

z1 <- (z1 - min(z1)) / (max(z1) - min(z1))
z2 <- (z2 - min(z2)) / (max(z2) - min(z2))

z12 <- (z1 + z2*2)/3

# look at patterns

layout(matrix(c(
1, 1, 2, 2,
1, 1, 2, 2,
3, 3, 4, 4, 
3, 3, 5, 5), nrow=4, byrow=TRUE))


image(z1, col=gray(seq(0, 1, length=20)), zlim=c(0,1))
image(z2, col=gray(seq(0, 1, length=20)), zlim=c(0,1))
image(z12, col=gray(seq(0, 1, length=20)), zlim=c(0,1))

# analyze the pattern of z across space
z1 <- as.vector(z1)
z2 <- as.vector(z2)
z12 <- as.vector(z12)
z1.d <- dist(z1)
z2.d <- dist(z2)
z12.d <- dist(z12)

space <- cbind(as.vector(x), as.vector(y))
space.d <- dist(space)

# take partial correlogram without effects of z1
### pmgram() is time-consuming, so this was generated
### in advance and saved.
### set.seed(1234)
### z.no <- pmgram(z12.d, space.d, nperm=1000, resids=FALSE)
### save(z.no, file="ecodist/data/z.no.rda")
data(z.no)
plot(z.no)


# take partial correlogram of z12 given z1
### pmgram() is time-consuming, so this was generated
### in advance and saved.
### set.seed(1234)
### z.z1 <- pmgram(z12.d, space.d, z2.d, nperm=1000, resids=FALSE)
### save(z.z1, file="ecodist/data/z.z1.rda")
data(z.z1)
plot(z.z1)