Dissimilarity-Based Functions for Ecological Analysis

Description

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.

Details

This package contains well-established dissimilarity-based ecological analyses, such as nmds and mantel, and experimental/research analyses such as xmantel. Helper functions such as crosstab and cor2m facilitate analysis of community data.

Because many of the analyses are time-consuming, this package includes worked examples that can be loaded using data().

Index of help topics:

MRM                     Multiple Regression on distance Matrices
addord                  Fit new points to an existing NMDS
                        configuration.
bcdist                  Bray-Curtis distance
bump                    Nine-bump spatial pattern
bump.pmgram             Nine-bump spatial pattern
cor2m                   Two-matrix correlation table
corgen                  Generate correlated data
crosstab                Data formatting
dim.dist                Dimension of a distance object
distance                Calculate dissimilarity/distance metrics
ecodist-package         Dissimilarity-Based Functions for Ecological
                        Analysis
fixdmat                 Distance matrix conversion
full                    Full symmetric matrix
graze                   Site information and grazed vegetation data.
iris.fit                Example of adding to an ordination
iris.nmds               Example for nmds
iris.vf                 Example for vector fitting on ordination
iris.vfrot              Example for vector fitting on rotated
                        ordination
lower                   Lower-triangular matrix
mantel                  Mantel test
mgram                   Mantel correlogram
mgroup                  Mantel test for groups
min.nmds                Find minimum stress configuration
nmds                    Non-metric multidimensional scaling
pathdist                Graph extension of dissimilarities
pco                     Principal coordinates analysis
plot.mgram              Plot a Mantel correlogram
plot.nmds               Plot information about NMDS ordination
plot.vf                 Plots fitted vectors onto an ordination diagram
pmgram                  Piecewise multivariate correlogram
relrange                Relativize a compositional data matrix.
residuals.mgram         Residuals of a Mantel correlogram
rotate2d                Rotate a 2D ordination.
vf                      Vector fitting
xdistance               Cross-distance between two datasets.
xmantel                 Cross-Mantel test
xmgram                  Cross-Mantel correlogram
z.no                    Example for pmgram
z.z1                    Example for pmgram

Author(s)

Sarah Goslee and Dean Urban

Maintainer: Sarah Goslee <Sarah.Goslee@ars.usda.gov>

Multiple Regression on distance Matrices

Description

Multiple regression on distance matrices (MRM) using permutation tests of significance for regression coefficients and R-squared.

Usage

MRM(formula = formula(data), data, nperm = 1000,
	method = "linear", mrank = FALSE)

Arguments

Details

Performs multiple regression on distance matrices following the methods outlined in Legendre et al. 1994. Specificaly, the permutation test uses a pseudo-t test to assess significance, rather than using the regression coefficients directly.

Value

Author(s)

Sarah Goslee

References

Lichstein, J. 2007. Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology 188: 117-131.

Legendre, P.; Lapointe, F. and Casgrain, P. 1994. Modeling brain evolution from behavior: A permutational regression approach. Evolution 48: 1487-1499.

See Also

mantel

Examples

  data(graze)

  # Abundance of this grass is related to forest cover but not location
  MRM(dist(LOAR10) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)

  # Abundance of this legume is related to location but not forest cover
  MRM(dist(TRRE3) ~ dist(sitelocation) + dist(forestpct), data=graze, nperm=10)

  # Compare to presence/absence of grass LOAR10 using logistic regression
  LOAR10.presence <- ifelse(graze$LOAR10 > 0, 1, 0)
  MRM(dist(LOAR10.presence) ~ dist(sitelocation) + dist(forestpct), 
  	data=graze, nperm=10, method="logistic")

Fit new points to an existing NMDS configuration.

Description

Uses a brute force algorithm to find the location for each new point that minimizes overall stress.

Usage

addord(origconf, fulldat, fulldist, isTrain, bfstep = 10, maxit = 50, epsilon = 1e-12)

Arguments

Details

A region comprising the original ordination configuration plus one standard deviation is divided into a grid of bfstep rows and columns. For a new point, the grid cell with the lowest stress is identified. That cell is divided into a finer grid, and the lowest-stress cell identified. This process is repeated up to maxit times, or until stress changes less than epsilon.

Value

Author(s)

Sarah Goslee

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# rotate the configuration to maximize variance
iris.rot <- princomp(iris.nmin)$scores

# rotation preserves distance apart in ordination space
cor(dist(iris.nmin), dist(iris.rot))

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

# repeat for the rotated ordination
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vfrot <- vf(iris.rot, iris[,1:4], nperm=1000)
### save(iris.vfrot, file="ecodist/data/iris.vfrot.rda")
data(iris.vfrot)

par(mfrow=c(1,2))
plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)
plot(iris.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="Rotated NMDS")
plot(iris.vfrot)

####### addord example

# generate new data points to add to the ordination
# this might be new samples, or a second dataset

iris.new <- structure(list(Sepal.Length = c(4.6, 4.9, 5.4, 5.2, 6, 6.5, 6, 
6.8, 7.3), Sepal.Width = c(3.2, 3.5, 3.6, 2.3, 2.8, 3, 2.7, 3.1, 
3.2), Petal.Length = c(1.2, 1.5, 1.5, 3.5, 4.1, 4.2, 4.8, 5, 
5.7), Petal.Width = c(0.26, 0.26, 0.26, 1.2, 1.3, 1.4, 1.8, 2, 
2), Species = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), class = "data.frame", row.names = c(NA, 
-9L))

# provide a dist object containing original and new data
# provide a logical vector indicating which samples were used to
# construct the original configuration

iris.full <- rbind(iris, iris.new)
all.d <- dist(iris.full[,1:4])
is.orig <- c(rep(TRUE, nrow(iris)), rep(FALSE, nrow(iris.new)))

### addord() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.fit <- addord(iris.nmin, iris.full[,1:4], all.d, is.orig, maxit=100)
### save(iris.fit, file="ecodist/data/iris.fit.rda")
data(iris.fit)

plot(iris.fit$conf, col=iris.full$Species, pch=c(18, 4)[is.orig + 1], xlab="NMDS 1", ylab="NMDS 2")
title("Demo: adding points to an ordination")
legend("bottomleft", c("Training set", "Added point"), pch=c(4, 18))
legend("topright", levels(iris$Species), fill=1:3)

Bray-Curtis distance

Description

Returns the Bray-Curtis (also known as Sorenson, 1 - percent similarity) pairwise distances for the objects in the data. It is duplicated by functionality within distance but remains for backward compatibility and because it is substantially faster.

Usage

bcdist(x, rmzero = FALSE)

Arguments

Value

This function returns a column-order lower-triangular distance matrix. The returned object has an attribute, Size, giving the number of objects, that is, nrow(x). The length of the vector that is returned is nrow(x)*(nrow(x)-1)/2.

Author(s)

Sarah Goslee

See Also

dist, distance

Examples

data(graze)
system.time(graze.bc <- bcdist(graze[, -c(1:2)]))
# equivalent to but much faster than:
system.time(graze.bc2 <- distance(graze[, -c(1:2)], "bray-curtis"))

all.equal(graze.bc, graze.bc2)

Nine-bump spatial pattern

Description

A two-dimensional artificial "landscape" illustrating the kind of spatial pattern that might be seen across mountain peaks.

Usage

data(bump)

Format

The format is: int [1:25, 1:25] 2 2 2 2 2 2 2 2 2 2 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:25] "1" "3" "5" "7" ... ..$ : chr [1:25] "V1" "V3" "V5" "V7" ...

Author(s)

Sarah Goslee

See Also

bump.pmgram, pmgram

Examples

data(bump)
image(bump)

Nine-bump spatial pattern

Description

An object of class mgram for use in the example for pmgram. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(bump.pmgram)

Format

See pmgram for current format specification.

Author(s)

Sarah Goslee

See Also

bump, pmgram

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))

### pgram() is time-consuming, so this was generated
### in advance and saved.
### set.seed(1234)
### bump.pmgram <- pmgram(value.dist, geo.dist, nperm=10000)
### save(bump.pmgram, file="ecodist/data/bump.pmgram.rda")

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

Two-matrix correlation table

Description

Generate a correlation table between the variables of two data sets, originally for comparing species abundances and environmental variables.

Usage

cor2m(x, y, trim = TRUE, alpha = 0.05)

Arguments

Details

cor2m generates a correlation table between the variables of two matrices. The original use case is to compare species abundances and environmental variables. It results in a data frame with species (or the first matrix) as columns and environmental variables (or the second matrix) as rows, so it's easy to scan. Correlations less than a user-specified alpha (0.05 by default) can be set to NA. cor2m generates a correlation table between the variables of two matrices. The original use case is to compare species abundances and environmental variables. The result has species (or the first matrix) as columns and environmental variables (or the second matrix) as rows, so it's easy to scan. Correlations less than a user-specified alpha can be set to NA. If trim, correlations less than the critical value for the provided alpha are set to to NA. The critical value is computed as a t-test with n-2 df. cor2m(x, y, trim=FALSE) is equivalent to cor(x, y)

Value

Returns a data frame of correlations between the variables of 2 data frames.

Author(s)

Dean Urban

Examples

data(graze)
speciesdata <- graze[, 3:7]
envdata <- graze[, 1:2]
sppenv.cor <- cor2m(envdata, speciesdata)
print(sppenv.cor, na.print="")

Generate correlated data

Description

Generate correlated data of a given length.

Usage

  corgen(len, x, r, population = FALSE, epsilon = 0)

Arguments

Details

Either x or len must be specified. If epsilon = 0, it has no effect, otherwise the sampling process is repeated until the sample correlation is within epsilon of r. This option allows the production of exactly-correlated data, within the limits of epsilon. Setting epsilon > 0 invalidates the population setting; data will be correlated within that range, rather than sampled from that population.If epsilon = 0, it has no effect, otherwise the sampling process is repeated until the sample correlation is within epsilon of r. This option allows the production of exactly-correlated data, within the limits of epsilon. Setting epsilon > 0 invalidates the population setting; data will be correlated within that range, rather than sampled from that population.If epsilon = 0, it has no effect, otherwise the sampling process is repeated until the sample correlation is within epsilon of r. This option allows the production of exactly-correlated data, within the limits of epsilon. Setting epsilon > 0 invalidates the population setting; data will be correlated within that range, rather than sampled from that population.

Value

Author(s)

Sarah Goslee

Examples

# create two random variables of length 100 with correlation
# of 0.10 +/- 0.01
xy <- corgen(len=100, r=.1, epsilon=0.01)
with(xy, cor(x, y))

# create two random variables of length 100 drawn from a population with
# a correlation of -0.82
xy <- corgen(len=100, r=-0.82, population=TRUE)
with(xy, cor(x, y))

# create a variable y within 0.01 of the given correlation to x
x <- 1:100
y <- corgen(x=x, r=.5, epsilon=.01)$y
cor(x, y)

Data formatting

Description

Converts field data of the form site, species, observation into a site by species data frame.

Usage

crosstab(rowlab, collab, values, type = "sum", data, allrows, allcols,
na.as.0 = TRUE, check.names = TRUE, ...)

Arguments

Details

Field data are often recorded as a separate row for each site-species combination. This function reformats such data into a data frame for further analysis based on unique row and column labels. The three vectors should all be the same length (including duplicates). The three vectors may also be provided as names of columns in the data frame specified by the data argument.

If allrows or allcols exists, rows and/or columns of zeros are inserted for any elements of allrows/allcols not present in rowlab/collab.

If values is missing the number of occurrences of combinations of rowlab and collab will be returned. Thus, crosstab(rowlab, collab) is equivalent to table(rowlab, collab).

If type is "count", the unique combinations of rowlab, collab and values will be returned.

Value

data frame with rowlab as row headings, collab as columns, and values as the data.

Author(s)

Sarah Goslee

Examples

# Make a random example
plotnames <- rep(1:5, each = 6)
speciesnames <- rep(c("A", "B", "C"), 10)
freqdata <- runif(30)

# number of samples of each species and plot
crosstab(plotnames, speciesnames)

# can use the data argument
speciesdata <- data.frame(plots = plotnames, species = speciesnames,
  freq = freqdata, stringsAsFactors=FALSE)

# mean frequency by species and plot
crosstab(plots, species, freq, data=speciesdata, type="mean")

# can specify additional possible row or column levels
crosstab(plots, species, freq, data=speciesdata, type="mean", allcols=LETTERS[1:5])

Dimension of a distance object

Description

Returns NULL for the dimensions of a distance object.

Usage

  ## S3 method for class 'dist'
dim(x)

Arguments

Details

The spdep package overwrites the base R behavior of dim.dist() to return c(n, n) where n is the size of the full matrix. The base R behavior returns NULL. This function restores base R behavior within ecodist, because otherwise spdep being loaded breaks ecodist functionality.

Value

NULL

Author(s)

Sarah Goslee

Examples

	data(graze)
	dim(dist(graze))

Calculate dissimilarity/distance metrics

Description

This function calculates a variety of dissimilarity or distance metrics. Although it duplicates the functionality of dist() and bcdist(), it is written in such a way that new metrics can easily be added. distance() was written for extensibility and understandability, and is not necessarily an efficient choice for use with large matrices.

Usage

distance(x, method = "euclidean", sprange=NULL, spweight=NULL, icov, inverted = FALSE)

Arguments

Value

Returns a lower-triangular distance matrix as an object of class "dist".

Author(s)

Sarah Goslee

See Also

dist, bcdist

Examples

data(iris)
iris.bc <- distance(iris[, 1:4], "bray-curtis")

# The effect of specifying icov:

# calculate Mahalanobis distance for the full iris dataset
iris.md <- full(distance(iris[, 1:4], "mahal"))
iris.md[1, 2] # Mahalanobis distance between samples 1 and 2 

# calculate Mahalanobis for just one species
setosa.md <- full(distance(iris[iris$Species == "setosa", 1:4], "mahal"))
setosa.md[1, 2] # Mahalanobis distance between samples 1 and 2 

# use the covariance matrix for the full dataset to scale for one species
setosa.scaled.md <- full(distance(iris[iris$Species == "setosa", 1:4],
  "mahal", icov=var(iris[,1:4])))
setosa.scaled.md[1, 2] # Mahalanobis distance between samples 1 and 2 

Distance matrix conversion

Description

Convert a row-order lower-triangular distance matrix to a full symmetric matrix.

Usage

  fixdmat(v)

Arguments

Details

R distance functions such as dist and bcdist return a lower triangular distance matrix in column order. Some other programs return the lower triangular matrix in row order. To use this matrix in R functions, it must be converted from row order to column order.

Value

full symmetric distance matrix.

Author(s)

Sarah Goslee

See Also

lower, full

Examples

x.vec <- seq_len(6)
x.vec

# Make an R-style column order symmetric matrix
full(x.vec)

# Extract the lower triangle from a symmetric matrix
# in column order
lower(full(x.vec))

# Convert to or from a row order symmetric matrix
fixdmat(x.vec)
lower(fixdmat(x.vec))

fixdmat(c(1, 2, 4, 3, 5, 6))

Full symmetric matrix

Description

Convert a column order distance matrix to a full symmetric matrix.

Usage

  full(v)

Arguments

Details

Converts a column order lower-triangular distance matrix as written by R functions into a symmetric matrix. Note that lower() used on a 1x1 matrix will return the single element, which may not be the correct behavior in all cases, while full() used on a single element will return a 2x2 matrix.

Value

full symmetric matrix.

Author(s)

Sarah Goslee

See Also

lower, fixdmat

Examples

# Given a vector:
x.vec <- seq_len(6)
x.vec

# Make an R-style column order symmetric matrix
full(x.vec)

# Extract the lower triangle from a symmetric matrix
# in column order
lower(full(x.vec))

# Convert to or from a row order symmetric matrix
fixdmat(x.vec)
lower(fixdmat(x.vec))

fixdmat(c(1, 2, 4, 3, 5, 6))

Site information and grazed vegetation data.

Description

This data frame contains site location, landscape context and dominant plant species abundances for 25 of the plant species found in 50 grazed pastures in the northeastern United States. Elements are the mean values for canopy cover for ten 0.5 x 2 m quadrats.

Usage

data(graze)

Format

A data frame with 50 observations on the following 25 variables.

sitelocation

Site location along a geographic gradient.

forestpct

Percentage forest cover within a 1-km radius.

ACMI2

Percentage canopy cover.

ANOD

Percentage canopy cover.

ASSY

Percentage canopy cover.

BRIN2

Percentage canopy cover.

CIAR4

Percentage canopy cover.

CIIN

Percentage canopy cover.

CIVU

Percentage canopy cover.

DAGL

Percentage canopy cover.

ELRE4

Percentage canopy cover.

GAMO

Percentage canopy cover.

LOAR10

Percentage canopy cover.

LOCO6

Percentage canopy cover.

LOPE

Percentage canopy cover.

OXST

Percentage canopy cover.

PLMA2

Percentage canopy cover.

POPR

Percentage canopy cover.

PRVU

Percentage canopy cover.

RAAC3

Percentage canopy cover.

RUCR

Percentage canopy cover.

SORU2

Percentage canopy cover.

STGR

Percentage canopy cover.

TAOF

Percentage canopy cover.

TRPR2

Percentage canopy cover.

TRRE3

Percentage canopy cover.

VEOF2

Percentage canopy cover.

Details

Site locations fall along a southwest-northeast transect through the northeastern United States. This is a synthetic gradient calculated from latitude and longitude. Forest landcover is taken from the USGS 1992 National Land Cover Dataset. All forest classes were combined, and the percentage within 1 km of the sample sites was calculated using a GIS.

Author(s)

Sarah Goslee

Source

Details of these data are available in Tracy and Sanderson (2000) and Goslee and Sanderson (2010). The 1992 NLCD data can be obtained from http://www.mrlc.gov/. Species codes are from http://plants.usda.gov (2010).

References

Tracy, B.F. and M.A. Sanderson. 2000. Patterns of plant species richness in pasture lands of the northeast United States. Plant Ecology 149:169-180.

Goslee, S.C., Sanderson, M.A. 2010. Landscape Context and Plant Community Composition in Grazed Agricultural Systems. Landscape Ecology 25:1029-1039.

Examples

data(graze)

Example of adding to an ordination

Description

A fitted ordination for use in the example for addord. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(iris.fit)

Format

The format of this object is a list with: X1, X2, etc: ordination configuration: coordinates for each point. stress: goodness of fit for each point. isTrain: logical vector indicating whether each point was used in the original ordination.

Author(s)

Sarah Goslee

See Also

nmds, addord

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# generate new data points to add to the ordination
# this might be new samples, or a second dataset

iris.new <- structure(list(Sepal.Length = c(4.6, 4.9, 5.4, 5.2, 6, 6.5, 6, 
6.8, 7.3), Sepal.Width = c(3.2, 3.5, 3.6, 2.3, 2.8, 3, 2.7, 3.1, 
3.2), Petal.Length = c(1.2, 1.5, 1.5, 3.5, 4.1, 4.2, 4.8, 5, 
5.7), Petal.Width = c(0.26, 0.26, 0.26, 1.2, 1.3, 1.4, 1.8, 2, 
2), Species = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), class = "data.frame",
row.names = c(NA, -9L))

# provide a dist object containing original and new data
# provide a logical vector indicating which samples were used to
# construct the original configuration

iris.full <- rbind(iris, iris.new)
all.d <- dist(iris.full[,1:4])
is.orig <- c(rep(TRUE, nrow(iris)), rep(FALSE, nrow(iris.new)))

### addord() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.fit <- addord(iris.nmin, iris.full[,1:4], all.d, is.orig, maxit=100)
### save(iris.fit, file="ecodist/data/iris.fit.rda")
data(iris.fit)

plot(iris.fit$conf, col=iris.full$Species, pch=c(18, 4)[is.orig + 1],
    xlab="NMDS 1", ylab="NMDS 2")
title("Demo: adding points to an ordination")
legend("bottomleft", c("Training set", "Added point"), pch=c(4, 18))
legend("topright", levels(iris$Species), fill=1:3)

Example for nmds

Description

An object of class nmds for use in the example for nmds. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(iris.nmds)

Format

See nmds for current format specification.

Author(s)

Sarah Goslee

See Also

nmds

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

Example for vector fitting on ordination

Description

An object of class vf for use in the examples for nmds and vf. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(iris.vf)

Format

See vf for current format specification.

Author(s)

Sarah Goslee

See Also

nmds, vf

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# rotate the configuration to maximize variance
iris.rot <- princomp(iris.nmin)$scores

# rotation preserves distance apart in ordination space
cor(dist(iris.nmin), dist(iris.rot))

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)

Example for vector fitting on rotated ordination

Description

An object of class vf for use in the examples for nmds and vf. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(iris.vfrot)

Format

See vf for current format specification.

Author(s)

Sarah Goslee

See Also

nmds, vf

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# rotate the configuration to maximize variance
iris.rot <- princomp(iris.nmin)$scores

# rotation preserves distance apart in ordination space
cor(dist(iris.nmin), dist(iris.rot))

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

# repeat for the rotated ordination
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vfrot <- vf(iris.rot, iris[,1:4], nperm=1000)
### save(iris.vfrot, file="ecodist/data/iris.vfrot.rda")
data(iris.vfrot)

par(mfrow=c(1,2))
plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)
plot(iris.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="Rotated NMDS")
plot(iris.vfrot)

Lower-triangular matrix

Description

Convert a symmetric distance matrix to a column order lower triangular matrix.

Usage

  lower(m)

Arguments

Details

Converts a symmetric matrix, for example a dissimilarity matrix, into a column order lower-triangular matrix. This may be useful to format the input for certain clustering and ordination functions. Note that lower() used on a 1x1 matrix will return the single element, which may not be the correct behavior in all cases, while full() used on a single element will return a 2x2 matrix.

Value

column order lower triangular matrix.

Author(s)

Sarah Goslee

See Also

full, fixdmat

Examples

x.vec <- seq_len(6)
x.vec

# Make an R-style column order symmetric matrix
full(x.vec)

# Extract the lower triangle from a symmetric matrix
# in column order
lower(full(x.vec))

# Convert to or from a row order symmetric matrix
fixdmat(x.vec)
lower(fixdmat(x.vec))

fixdmat(c(1, 2, 4, 3, 5, 6))

Mantel test

Description

Simple and partial Mantel tests, with options for ranked data, permutation tests, and bootstrapped confidence limits.

Usage

mantel(formula = formula(data), data, nperm = 1000,
    mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95)

Arguments

Details

If only one independent variable is given, the simple Mantel r (r12) is calculated. If more than one independent variable is given, the partial Mantel r (ryx|x1 ...) is calculated by permuting one of the original dissimilarity matrices. The bootstrapping is actually resampling without replacement, because duplication of samples is not useful in a dissimilarity context (the dissimilarity of a sample with itself is zero). Resampling within dissimilarity values is inappropriate, just as for permutation.

Value

Author(s)

Sarah Goslee

References

Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27:209-220.

Smouse, P.E., J.C. Long and R.R. Sokal. 1986. Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Systematic Zoology 35:62 7-632.

Goslee, S.C. and Urban, D.L. 2007. The ecodist package for dissimilarity-based analysis of ecological data. Journal of Statistical Software 22(7):1-19.

Goslee, S.C. 2010. Correlation analysis of dissimilarity matrices. Plant Ecology 206(2):279-286.

See Also

mgram, mgroup

Examples

data(graze)

grasses <- graze[, colnames(graze) %in% c("DAGL", "LOAR10", "LOPE", "POPR")]
legumes <- graze[, colnames(graze) %in% c("LOCO6", "TRPR2", "TRRE3")]

grasses.bc <- bcdist(grasses)
legumes.bc <- bcdist(legumes)

space.d <- dist(graze$sitelocation)
forest.d <- dist(graze$forestpct)

# Mantel test: is the difference in forest cover between sites
# related to the difference in grass composition between sites?
mantel(grasses.bc ~ forest.d)

# Mantel test: is the geographic distance between sites
# related to the difference in grass composition between sites?
mantel(grasses.bc ~ space.d)

# Partial Mantel test: is the difference in forest cover between sites
# related to the difference in grass composition once the
# linear effects of geographic distance are removed?
mantel(grasses.bc ~ forest.d + space.d)


# Mantel test: is the difference in forest cover between sites
# related to the difference in legume composition between sites?
mantel(legumes.bc ~ forest.d)

# Mantel test: is the geographic distance between sites
# related to the difference in legume composition between sites?
mantel(legumes.bc ~ space.d)

# Partial Mantel test: is the difference in forest cover between sites
# related to the difference in legume composition once the
# linear effects of geographic distance are removed?
mantel(legumes.bc ~ forest.d + space.d)


# Is there nonlinear pattern in the relationship with geographic distance?
par(mfrow=c(2, 1))
plot(mgram(grasses.bc, space.d, nclass=8))
plot(mgram(legumes.bc, space.d, nclass=8))

Mantel correlogram

Description

Calculates simple Mantel correlograms.

Usage

mgram(species.d, space.d, breaks, nclass, stepsize, equiprobable = FALSE, nperm = 1000,
    mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95,
    alternative = "two.sided", trace = FALSE)

Arguments

Details

This function calculates Mantel correlograms, and tests the hypothesis that the mean compositional dissimilarity within a distance class differs from the mean of all the other distance classes combined. The Mantel correlogram is essentially a multivariate autocorrelation function. The Mantel r represents the dissimilarity in variable composition (often species composition) at a particular lag distance, and significance is tested in reference to all distance classes.

Value

Returns an object of class mgram, which is a list with two elements. mgram is a matrix with one row for each distance class and 6 columns:

resids is NA for objects calculated by mgram().

Author(s)

Sarah Goslee

References

Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107-138.

See Also

mantel, plot.mgram, pmgram

Examples

# generate a simple surface
x <- matrix(1:10, nrow=10, ncol=10, byrow=FALSE)
y <- matrix(1:10, nrow=10, ncol=10, byrow=TRUE)
z <- x + 3*y
image(z)

# analyze the pattern of z across space
space <- cbind(as.vector(x), as.vector(y))
z <- as.vector(z)
space.d <- distance(space, "eucl")
z.d <- distance(z, "eucl")
z.mgram <- mgram(z.d, space.d, nperm=0)
plot(z.mgram)

#

data(graze)
space.d <- dist(graze$sitelocation)
forest.d <- dist(graze$forestpct)

grasses <- graze[, colnames(graze) %in% c("DAGL", "LOAR10", "LOPE", "POPR")]
legumes <- graze[, colnames(graze) %in% c("LOCO6", "TRPR2", "TRRE3")]

grasses.bc <- bcdist(grasses)
legumes.bc <- bcdist(legumes)

# Does the relationship of composition with distance vary for
# grasses and legumes?
par(mfrow=c(2, 1))
plot(mgram(grasses.bc, space.d, nclass=8))
plot(mgram(legumes.bc, space.d, nclass=8))

Mantel test for groups

Description

Mantel test across one or more group contrasts.

Usage

mgroup(edist, groups, nperm = 1000, mrank = FALSE)

Arguments

Details

mgroup returns the Mantel correlations for group contrast matrices computed from cluster groups across a range of clustering levels.

Value

Author(s)

Sarah Goslee

References

Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107-138.

See Also

mantel

Examples

# Using a model matrix to test group membership

data(iris)
iris.d <- dist(iris[,1:4])
mgroup(iris.d, iris[,5])

# clustering-based example

data(graze)
graze.d <- dist(graze[, -c(1:2)])
graze.hclust <- hclust(graze.d)

clust.groups <- data.frame(
	k2 = cutree(graze.hclust, k = 2),
	k4 = cutree(graze.hclust, k = 4),
	k6 = cutree(graze.hclust, k = 6),
	k8 = cutree(graze.hclust, k = 8))

mgroup(graze.d, clust.groups, nperm=1000)

Find minimum stress configuration

Description

Finds minimum stress configuration from output of nmds()

Usage

## S3 method for class 'nmds'
min(..., na.rm = FALSE, dims = 2)
nmds.min(x, dims = 2)

Arguments

Details

For back-compatibility, the nmds.min function remains.

Value

Configuration of minimum stress ordination (dataframe of coordinates). The stress and r^2 for the minimum stress configuration are stored as attributes.

Author(s)

Sarah Goslee

See Also

nmds

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

Non-metric multidimensional scaling

Description

Non-metric multidimensional scaling.

Usage

nmds(dmat, mindim = 1, maxdim = 2, nits = 10, iconf, epsilon = 1e-12,
    maxit = 500, trace = FALSE)

Arguments

Details

The goal of NMDS is to find a configuration in a given number of dimensions which preserves rank-order dissimilarities as closely as possible. The number of dimensions must be specified in advance. Because NMDS is prone to finding local minima, several random starts must be used. Stress is used as the measure of goodness of fit. A lower stress indicates a better match between dissimilarity and ordination. As of ecodist 1.9, the stress calculation used is the same as in MASS:isoMDS. In previous versions it was monotonically related, so the same configurations were produced, but the absolute value was different.

Value

The first results are for the lowest number of dimensions (total number is (mindim - maxdim + 1) * nits).

Author(s)

Sarah Goslee

References

Kruskal, J.B. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29:1-27.

Minchin, P.R. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 96:89-108.

See Also

plot.nmds, min.nmds, vf, addord

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# rotate the configuration to maximize variance
iris.rot <- princomp(iris.nmin)$scores

# rotation preserves distance apart in ordination space
cor(dist(iris.nmin), dist(iris.rot))

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

# repeat for the rotated ordination
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vfrot <- vf(iris.rot, iris[,1:4], nperm=1000)
### save(iris.vfrot, file="ecodist/data/iris.vfrot.rda")
data(iris.vfrot)

par(mfrow=c(1,2))
plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)
plot(iris.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species),
    main="Rotated NMDS")
plot(iris.vfrot)


# generate new data points to add to the ordination
# this might be new samples, or a second dataset

iris.new <- structure(list(Sepal.Length = c(4.6, 4.9, 5.4, 5.2, 6, 6.5, 6, 
6.8, 7.3), Sepal.Width = c(3.2, 3.5, 3.6, 2.3, 2.8, 3, 2.7, 3.1, 
3.2), Petal.Length = c(1.2, 1.5, 1.5, 3.5, 4.1, 4.2, 4.8, 5, 
5.7), Petal.Width = c(0.26, 0.26, 0.26, 1.2, 1.3, 1.4, 1.8, 2, 
2), Species = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("setosa", 
"versicolor", "virginica"), class = "factor")), .Names = c("Sepal.Length", 
"Sepal.Width", "Petal.Length", "Petal.Width", "Species"), class = "data.frame",
row.names = c(NA, -9L))

# provide a dist object containing original and new data
# provide a logical vector indicating which samples were used to
# construct the original configuration

iris.full <- rbind(iris, iris.new)
all.d <- dist(iris.full[,1:4])
is.orig <- c(rep(TRUE, nrow(iris)), rep(FALSE, nrow(iris.new)))

### addord() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.fit <- addord(iris.nmin, iris.full[,1:4], all.d, is.orig, maxit=100)
### save(iris.fit, file="ecodist/data/iris.fit.rda")
data(iris.fit)

plot(iris.fit$conf, col=iris.full$Species, pch=c(18, 4)[is.orig + 1],
    xlab="NMDS 1", ylab="NMDS 2")
title("Demo: adding points to an ordination")
legend("bottomleft", c("Training set", "Added point"), pch=c(4, 18))
legend("topright", levels(iris$Species), fill=1:3)

Graph extension of dissimilarities

Description

Uses the shortest path connecting sites to estimate the distance between samples with pairwise distances greater than maxv.

Usage

pathdist(v, maxv = 1)

Arguments

Details

Pairwise samples with no species will have distances greater than a cutoff. A distance-weighted graph connecting these samples by way of intermediate samples with some species in common can be used to interpolate distances by adding up the path length connecting those samples. This function will fail if there are completely disconnected subsets.

Value

Returns a lower-triangular distance matrix.

Author(s)

Sarah Goslee

See Also

dist, distance

Examples

	# samples 1 and 2, and 3 and 4, have no species in common
	x <- matrix(c(	1, 0, 1, 0,
			0, 1, 0, 1,
			1, 0, 0, 0,
			0, 1, 1, 1,
			1, 1, 1, 0,
			1, 0, 1, 1,
			0, 0, 1, 1), ncol = 4, byrow = TRUE)

	# the maximum Jaccard distance is 1
	# regardless of how different the samples are
	x.jd <- dist(x, "binary")

	# estimate the true distance between those pairs
	# by following the shorted path along connected sites
	pathdist(x.jd)

Principal coordinates analysis

Description

Principal coordinates analysis (classical scaling).

Usage

pco(x, negvals = "zero", dround = 0)

Arguments

Details

PCO (classical scaling, metric multidimensional scaling) is very similar to principal components analysis, but allows the use of any dissimilarity metric.

Value

Author(s)

Sarah Goslee

See Also

princomp, nmds

Examples

data(iris)
iris.d <- dist(iris[,1:4])
iris.pco <- pco(iris.d)

# scatterplot of the first two dimensions
plot(iris.pco$vectors[,1:2], col=as.numeric(iris$Species),
  pch=as.numeric(iris$Species), main="PCO", xlab="PCO 1", ylab="PCO 2")

Plot a Mantel correlogram

Description

Plot a Mantel correlogram from an object of S3 class mgram, using solid symbols for significant values.

Usage

## S3 method for class 'mgram'
plot(x, pval = 0.05, xlab = "Distance", ylab = NULL, ...)

Arguments

Value

draws a plot (graphics device must be active).

Author(s)

Sarah Goslee

See Also

mgram

Examples

# generate a simple surface
x <- matrix(1:10, nrow=10, ncol=10, byrow=FALSE)
y <- matrix(1:10, nrow=10, ncol=10, byrow=TRUE)
z <- x + 3*y
image(z)

# analyze the pattern of z across space
space <- cbind(as.vector(x), as.vector(y))
z <- as.vector(z)
space.d <- distance(space, "eucl")
z.d <- distance(z, "eucl")
z.mgram <- mgram(z.d, space.d, nperm=0)
plot(z.mgram)

#

data(graze)
space.d <- dist(graze$sitelocation)
forest.d <- dist(graze$forestpct)

grasses <- graze[, colnames(graze) %in% c("DAGL", "LOAR10", "LOPE", "POPR")]
legumes <- graze[, colnames(graze) %in% c("LOCO6", "TRPR2", "TRRE3")]

grasses.bc <- bcdist(grasses)
legumes.bc <- bcdist(legumes)

# Does the relationship of composition with distance vary for
# grasses and legumes?
par(mfrow=c(2, 1))
plot(mgram(grasses.bc, space.d, nclass=8))
plot(mgram(legumes.bc, space.d, nclass=8))

Plot information about NMDS ordination

Description

Graphical display of stress and r2 for NMDS ordination along number of dimensions.

Usage

## S3 method for class 'nmds'
plot(x, plot = TRUE, xlab = "Dimensions", ...)

Arguments

Value

Produces a two-panel plot with stress and r2 for ordination by number of dimensions. Points show the mean value; lines indicate minimum and maximum.

Author(s)

Dean Urban

See Also

nmds

Examples

data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

Plots fitted vectors onto an ordination diagram

Description

Add vector fitting arrows to an existing ordination plot.

Usage

## S3 method for class 'vf'
plot(x, pval = NULL, r = NULL, cex = 0.8, ascale = 0.9, ...)

Arguments

Value

Adds arrows to an existing ordination plot. Only arrows with a p-value less than pval are added. By default, all variables are shown.

Author(s)

Sarah Goslee

See Also

vf

Examples

# Example of multivariate analysis using built-in iris dataset
data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)
plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)

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)

Arguments

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.

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:

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

Author(s)

Sarah Goslee

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)

Relativize a compositional data matrix.

Description

Relativizes the range of each column of a data frame or matrix x to 0-1. If globalmin and/or globalmax are provided, those are used to scale the columns, for instance to scale a subset to match a larger sample. If they are NA, the minimum and maximum values for each column are used.

Usage

relrange(x, globalmin = NA, globalmax = NA)

Arguments

Details

Relativizes the data using the minimum and maximum values. If globalmin and global max are not used, the range will be 0-1 for each variable. This can be useful for putting disparate variables to the same magnitude while keeping all non-negative values.

Value

Returns an object of the same class as x (matrix or data frame) with the columns rescaled.

Author(s)

Sarah Goslee

See Also

scale

Examples

	x <- matrix(1:15, ncol = 3)

	# uses min and max of the data
	relrange(x)

	# uses min and max determined by other knowledge of the variables
	relrange(x, globalmin = c(0, 0, 0), globalmax = c(6, 10, 20))

Residuals of a Mantel correlogram

Description

Extracts residuals from an S3 object of class mgram (only relevant for objects created by pmgram{}).

Usage

## S3 method for class 'mgram'
residuals(object, ...)

Arguments

Value

vector of residuals.

Author(s)

Sarah Goslee

See Also

pmgram, mgram

Examples

#### Partial pmgram example

# generate a simple surface
# with complex nonlinear spatial pattern

x <- matrix(1:10, nrow=10, ncol=10, byrow=FALSE)
y <- matrix(1:10, nrow=10, ncol=10, 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

# 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 of z12 given z1
z.z1 <- pmgram(z12.d, space.d, z2.d, nperm=0, resids=TRUE)
summary(residuals(z.z1))

Rotate a 2D ordination.

Description

Rotates a two-dimensional ordination configuration to place the direction indicated along the horizontal axis.

Usage

rotate2d(ord, x)

Arguments

Details

The configuration ord is rotated so that the vector defined by c(0, 0), and x is along the horizontal axis. This can be useful for placing a specific variable, for instance from vf(), in a consistent direction across multiple ordinations. Doing so can facilitate interpretation.

Value

A rotated data frame of coordinates of the same size as ord and in the same order. If ord was produced by vf(), the complete vf object is returned.

Author(s)

Sarah Goslee

See Also

vf, nmds

Examples

# Example of multivariate analysis using built-in iris dataset
data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)

# rotate configuration so Sepal Width is along the horizontal axis

iris.nmin.rot <- rotate2d(iris.nmin, iris.vf[2, 1:2])
iris.vf.rot <- rotate2d(iris.vf, iris.vf[2, 1:2])

plot(iris.nmin.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf.rot)

Vector fitting

Description

Fits ancillary variables to an ordination configuration.

Usage

vf(ord, vars, nperm = 100)

Arguments

Details

Vector fitting finds the maximum correlation of the individual variables with a configuration of samples in ordination space.

Value

an object of class vf, which is a data frame with the first 2 columns containing the scores for every variable in each of the 2 dimensions of the ordination space. r is the maximum correlation of the variable with the ordination space, and pval is the result of the permutation test.

Author(s)

Sarah Goslee

References

Jongman, R.H.G., C.J.F. ter Braak and O.F.R. van Tongeren. 1995. Data analysis in community and landscape ecology. Cambridge University Press, New York.

See Also

plot.vf

Examples

# Example of multivariate analysis using built-in iris dataset
data(iris)
iris.d <- dist(iris[,1:4])

### nmds() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.nmds <- nmds(iris.d, nits=20, mindim=1, maxdim=4)
### save(iris.nmds, file="ecodist/data/iris.nmds.rda")
data(iris.nmds)

# examine fit by number of dimensions
plot(iris.nmds)

# choose the best two-dimensional solution to work with
iris.nmin <- min(iris.nmds, dims=2)

# fit the data to the ordination as vectors
### vf() is timeconsuming, so this was generated
### in advance and saved.
### set.seed(1234)
### iris.vf <- vf(iris.nmin, iris[,1:4], nperm=1000)
### save(iris.vf, file="ecodist/data/iris.vf.rda")
data(iris.vf)

plot(iris.nmin, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf)

# rotate configuration so Sepal Width is along the horizontal axis

iris.nmin.rot <- rotate2d(iris.nmin, iris.vf[2, 1:2])
iris.vf.rot <- rotate2d(iris.vf, iris.vf[2, 1:2])

plot(iris.nmin.rot, col=as.numeric(iris$Species), pch=as.numeric(iris$Species), main="NMDS")
plot(iris.vf.rot)

Cross-distance between two datasets.

Description

Pairwise dissimilarity calculation between rows of one dataset and rows of another, for instance across different sampling periods for the same set of sites.

Usage

xdistance(x, y, method = "euclidean")

Arguments

Details

This function will calculate rowwise dissimilarities between any pair of matrices or data frames with the same number of columns. Note that the cross-dissimilarity functions are for research purposes, and are not well-tested.

Value

A non-symmetric and possibly not square matrix of dissimilarities of class xdist, where result <- xdistance(x, y) produces a matrix with result[a, b] containing the dissimilarity between x[a, ] and y[b, ].

Author(s)

Sarah Goslee

See Also

distance, xmantel, xmgram

Examples

data(graze)

### EXAMPLE 1: Square matrices

# take two subsets of sites with different dominant grass abundances
# use cut-offs that produce equal numbers of sites
dom1 <- subset(graze, POPR > 50 & DAGL < 20) #  8 sites
dom2 <- subset(graze, POPR < 50 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd)
xmantel(dom.xd ~ forest.xd + sitelocation.xd)

plot(xmgram(dom.xd, sitelocation.xd))


### EXAMPLE 2: Non-square matrices

# take two subsets of sites with different dominant grass abundances
# this produces a non-square matrix

dom1 <- subset(graze, POPR > 45 & DAGL < 20) # 13 sites
dom2 <- subset(graze, POPR < 45 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd, dims=c(13, 8))
xmantel(dom.xd ~ forest.xd + sitelocation.xd, dims=c(13, 8))

plot(xmgram(dom.xd, sitelocation.xd))

Cross-Mantel test

Description

Simple and partial cross-Mantel tests, with options for ranked data and permutation tests.

Usage

xmantel(formula = formula(data), data, dims = NA,
   nperm = 1000, mrank = FALSE)

Arguments

Details

If only one independent variable is given, the simple Mantel r (r12) is calculated. If more than one independent variable is given, the partial Mantel r (ryx|x1 ...) is calculated by permuting one of the original dissimilarity matrices. Note that the cross-dissimilarity functions are for research purposes, and are not well-tested.

Value

Author(s)

Sarah Goslee

See Also

xdistance, xmgram

Examples

data(graze)

### EXAMPLE 1: Square matrices

# take two subsets of sites with different dominant grass abundances
# use cut-offs that produce equal numbers of sites
dom1 <- subset(graze, POPR > 50 & DAGL < 20) #  8 sites
dom2 <- subset(graze, POPR < 50 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd)
xmantel(dom.xd ~ forest.xd + sitelocation.xd)

plot(xmgram(dom.xd, sitelocation.xd))


### EXAMPLE 2: Non-square matrices

# take two subsets of sites with different dominant grass abundances
# this produces a non-square matrix

dom1 <- subset(graze, POPR > 45 & DAGL < 20) # 13 sites
dom2 <- subset(graze, POPR < 45 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd, dims=c(13, 8))
xmantel(dom.xd ~ forest.xd + sitelocation.xd, dims=c(13, 8))

plot(xmgram(dom.xd, sitelocation.xd))

Cross-Mantel correlogram

Description

Calculates simple Mantel correlograms from cross-distance matrices.

Usage

xmgram(species.xd, space.xd, breaks, nclass, stepsize, equiprobable = FALSE, nperm = 1000,
    mrank = FALSE, alternative = "two.sided", trace = FALSE)

Arguments

Details

This function calculates cross-Mantel correlograms. The Mantel correlogram is essentially a multivariate autocorrelation function. The Mantel r represents the dissimilarity in variable composition (often species composition) at a particular lag distance. Note that the cross-dissimilarity functions are for research purposes, and are not well-tested.

Value

Returns an object of class mgram, which is a list with two elements. mgram is a matrix with one row for each distance class and 6 columns:

resids is NA for objects calculated by mgram().

Author(s)

Sarah Goslee

References

Legendre, P. and M. Fortin. 1989. Spatial pattern and ecological analysis. Vegetatio 80:107-138.

See Also

xdistance xmantel, plot.mgram

Examples

# Need to develop a cross-dissimilarity example
data(graze)

### EXAMPLE 1: Square matrices

# take two subsets of sites with different dominant grass abundances
# use cut-offs that produce equal numbers of sites
dom1 <- subset(graze, POPR > 50 & DAGL < 20) #  8 sites
dom2 <- subset(graze, POPR < 50 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd)
xmantel(dom.xd ~ forest.xd + sitelocation.xd)

plot(xmgram(dom.xd, sitelocation.xd))


### EXAMPLE 2: Non-square matrices

# take two subsets of sites with different dominant grass abundances
# this produces a non-square matrix

dom1 <- subset(graze, POPR > 45 & DAGL < 20) # 13 sites
dom2 <- subset(graze, POPR < 45 & DAGL > 20) #  8 sites

# first two columns are site info
dom.xd <- xdistance(dom1[, -c(1,2)], dom2[, -c(1,2)], "bray")

# environmental and spatial distances; preserve rownames
forest.xd <- xdistance(dom1[, "forestpct", drop=FALSE], 
    dom2[, "forestpct", drop=FALSE])
sitelocation.xd <- xdistance(dom1[, "sitelocation", drop=FALSE], 
    dom2[, "sitelocation", drop=FALSE])

# permutes rows and columns of full nonsymmetric matrix
xmantel(dom.xd ~ forest.xd, dims=c(13, 8))
xmantel(dom.xd ~ forest.xd + sitelocation.xd, dims=c(13, 8))

plot(xmgram(dom.xd, sitelocation.xd))

Example for pmgram

Description

An object of class mgram for use in the example for pmgram. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(z.no)

Format

See pmgram for current format specification.

Author(s)

Sarah Goslee

See Also

pmgram, z.z1,

Examples

#### 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
### pgram() 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")
plot(z.no)

# take partial correlogram of z12 given z1
### pgram() 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")
plot(z.z1)

Example for pmgram

Description

An object of class mgram for use in the example for pmgram. Many of the functions in ecodist take a long time to run, so prepared examples have been included.

Usage

data(z.z1)

Format

See pmgram for current format specification.

Author(s)

Sarah Goslee

See Also

pmgram, z.no,

Examples

#### 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
### pgram() 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")
plot(z.no)

# take partial correlogram of z12 given z1
### pgram() 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")
plot(z.z1)