ecodist (version 1.00)

mantel: 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 = sys.parent(), nperm = 1000, mrank = FALSE, nboot = 500, pboot = 0.9, cboot = 0.95)

Arguments

formula

formula in R/S-Plus format describing the test to be conducted. For this test, y ~ x will perform a simple Mantel test, while y ~ x + z1 + z2 + z3 will do a partial Mantel test of the relationship between x and y given z1, z2, z3. All variables can be eit

data

an optional dataframe containing the variables in the model as columns of dissimilarities. By default the variables are taken from the current environment.

nperm

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

mrank

if this is set to FALSE (the default option), Pearson correlations will be used. If set to TRUE, the Spearman correlation (correlation ranked distances) will be used.

nboot

number of iterations to use for the bootstrapped confidence limits. If set to 0, the bootstrapping will be omitted.

pboot

the level at which to resample the data for the bootstrapping procedure.

cboot

the level of the confidence limits to estimate.

Value

• mantelr
  Mantel coefficient.
• pval1
  one-tailed p-value (null hypothesis: r <= 0).<="" description="">
• pval2
  one-tailed p-value (null hypothesis: r >= 0).
• pval3
  two-tailed p-value (null hypothesis: r = 0).
• llim
  lower confidence limit.
• ulim
  upper confidence limit.

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 using the regression residual method of Smouse et al. 1986.

References

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

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

See Also

mgram

Examples

# Example of multivariate analysis using built-in iris dataset
data(iris)
iris.md <- distance(iris[,1:4], "mahal")

# Create a model matrix for testing species differences
iris.model <- distance(as.numeric(iris[,5]), "eucl")
iris.model[iris.model > 0] <- 1

# Test whether samples within the same species are more similar than those not
mantel(iris.md ~ iris.model)

# A full example is available in the Mantel test 
# section of the main help file for \link{ecodist}.