mantel.correlog {vegan} | R Documentation |

Function `mantel.correlog`

computes a multivariate
Mantel correlogram. Proposed by Sokal (1986) and Oden and Sokal
(1986), the method is also described in Legendre and Legendre (1998,
pp. 736-738).

mantel.correlog(D.eco, D.geo=NULL, XY=NULL, n.class=0, break.pts=NULL, cutoff=TRUE, r.type="pearson", nperm=999, mult="holm", progressive=TRUE) ## S3 method for class 'mantel.correlog': plot(x, alpha=0.05, ...)

`D.eco` |
An ecological distance matrix, with class
either `dist` or `matrix` . |

`D.geo` |
A geographic distance matrix, with class either
`dist` or `matrix` . Provide either `D.geo` or
`XY` . Default: `D.geo=NULL` . |

`XY` |
A file of Cartesian geographic coordinates of the
points. Default: `XY=NULL` . |

`n.class` |
Number of classes. If `n.class=0` , the Sturge
equation will be used unless break points are provided. |

`break.pts` |
Vector containing the break points of the distance
distribution. Default: `break.pts=NULL` . |

`cutoff` |
For the second half of the distance classes,
`cutoff = TRUE` limits the correlogram to the distance classes
that include all points. If `cutoff = FALSE` , the correlogram
includes all distance classes. |

`r.type` |
Type of correlation in calculation of the Mantel
statistic. Default: `r.type="pearson"` . Other choices are
`r.type="spearman"` and `r.type="kendall"` , as in functions
`cor` and `mantel` . |

`nperm` |
Number of permutations for the tests of
significance. Default: `nperm=999` . For large data files,
permutation tests are rather slow. |

`mult` |
Correct P-values for multiple testing. The correction
methods are `"holm"` (default), `"hochberg"` ,
`"sidak"` , and other methods available in the
`p.adjust` function: `"bonferroni"` (best known, but
not recommended because it is overly conservative), `"hommel"` ,
`"BH"` , `"BY"` , `"fdr"` , and `"none"` . |

`progressive` |
Default: `progressive=TRUE` for progressive
correction of multiple-testing, as described in Legendre and Legendre
(1998, p. 721). Test of the first distance class: no correction;
second distance class: correct for 2 simultaneous tests; distance
class k: correct for k simultaneous tests. `progressive=FALSE` :
correct all tests for `n.class` simultaneous tests. |

`x` |
Output of `mantel.correlog` . |

`alpha` |
Significance level for the points drawn with black
symbols in the correlogram. Default: `alpha=0.05` . |

`...` |
Other parameters passed from other functions. |

A correlogram is a graph in which spatial correlation values
are plotted, on the ordinate, as a function of the geographic distance
classes among the study sites along the abscissa. In a Mantel
correlogram, a Mantel correlation (Mantel 1967) is computed between a
multivariate (e.g. multi-species) distance matrix of the user's choice
and a design matrix representing each of the geographic distance
classes in turn. The Mantel statistic is tested through a
permutational Mantel test performed by `vegan`

's
`mantel`

function.

When a correction for multiple testing is applied, more permutations are necessary than in the no-correction case, to obtain significant p-values in the higher correlogram classes.

The `print.mantel.correlog`

function prints out the
correlogram. See examples.

`mantel.res ` |
A table with the distance classes as rows and the
class indices, number of distances per class, Mantel statistics
(computed using Pearson's r, Spearman's r, or Kendall's tau), and
p-values as columns. A positive Mantel statistic indicates positive
spatial correlation. An additional column with p-values corrected for
multiple testing is added unless `mult="none"` . |

`n.class ` |
The n umber of distance classes. |

`break.pts ` |
The break points provided by the user or computed by the program. |

`mult ` |
The name of the correction for multiple testing. No
correction: `mult="none"` . |

`progressive ` |
A logical (`TRUE` , `FALSE` ) value
indicating whether or not a progressive correction for multiple
testing was requested. |

`n.tests ` |
The number of distance classes for which Mantel tests have been computed and tested for significance. |

`call ` |
The function call. |

Pierre Legendre, Universite de Montreal

Legendre, P. and L. Legendre. 1998. Numerical ecology, 2nd English edition. Elsevier Science BV, Amsterdam.

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

Oden, N. L. and R. R. Sokal. 1986. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Syst. Zool. 35: 608-617.

Sokal, R. R. 1986. Spatial data analysis and historical processes. 29-43 in: E. Diday et al. [eds.] Data analysis and informatics, IV. North-Holland, Amsterdam.

# Mite data from "vegan" data(mite) data(mite.xy) mite.hel <- decostand(mite, "hellinger") mite.hel.D <- dist(mite.hel) mite.correlog <- mantel.correlog(mite.hel.D, XY=mite.xy, nperm=99) summary(mite.correlog) mite.correlog plot(mite.correlog) mite.correlog2 <- mantel.correlog(mite.hel.D, XY=mite.xy, cutoff=FALSE, r.type="spearman", nperm=99) summary(mite.correlog2) mite.correlog2 plot(mite.correlog2) ## Mite correlogram after spatially detrending the mite data mite.h.det <- resid(lm(as.matrix(mite.hel.D) ~ ., data=mite.xy)) mite.correlog3 <- mantel.correlog(mite.h.det, XY=mite.xy, nperm=99) mite.correlog3 plot(mite.correlog3)

[Package *vegan* version 1.16-32 Index]