This program computes a Mantel correlogram for the data M, or a partial Mantel correlogram for the data M conditioned on MC, with P-values or bootstrap confidence intervals.
eco.cormantel(M, XY, MC = NULL, int = NULL, smin = 0, smax = NULL, nclass = NULL, seqvec = NULL, size = NULL, bin = c("sturges", "FD"), nsim = 99, classM = c("dist", "simil"), method = c("pearson", "spearman", "kendall"), test = c("permutation", "bootstrap"), alternative = c("auto", "two.sided", "greater", "less"), adjust = "holm", sequential = TRUE, latlon = FALSE, angle = NULL, as.deg = TRUE, ...)
| M | Distance or similarity matrix. |
|---|---|
| XY | Data frame or matrix with individual's positions (projected coordinates). |
| MC | Distance or similarity matrix (optional). |
| int | Distance interval in the units of XY. |
| smin | Minimum class distance in the units of XY. |
| smax | Maximum class distance in the units of XY. |
| nclass | Number of classes. |
| seqvec | Vector with breaks in the units of XY. |
| size | Number of individuals per class. |
| bin | Rule for constructing intervals when a partition parameter (int, nclass or size) is not given. Default is Sturge's rule (Sturges, 1926). Other option is Freedman-Diaconis method (Freedman and Diaconis, 1981). |
| nsim | Number of Monte-Carlo simulations. |
| classM | Are M and MC distance or similarity matrices? Default option is classM = "dist" (distance). For similarity, classM = "simil". An incorrect option selected will generate an inverted plot. |
| method | Correlation method used for the construction of the statistic ("pearson", "spearman" or "kendall"). Kendall's tau computation is slow. |
| test | If test = "bootstrap", the program generates a bootstrap resampling and the associated confidence intervals. If test = "permutation" (default) a permutation test is made and the P-values are computed. |
| alternative | The alternative hypothesis. If "auto" is selected (default) the program determines the alternative hypothesis. Other options are: "two.sided", "greater" and "less". |
| adjust | Correction method of P-values for multiple tests,
passed to |
| sequential | Should be performed a Holm-Bonberroni (Legendre and Legendre, 2012) adjustment of P-values? Defalult TRUE. |
| latlon | Are the coordinates in decimal degrees format? Defalut FALSE. If TRUE,
the coordinates must be in a matrix/data frame with the longitude in the first
column and latitude in the second. The position is projected onto a plane in
meters with the function |
| angle | for computation of bearing correlogram (angle between 0 and 180). Default NULL (omnidirectional). |
| as.deg | in case of bearing correlograms for multiple angles, generate an output for each lag in function of the angle? Default TRUE. |
| ... | Additional arguments passed to |
The program returns an object of class "eco.correlog" with the following slots:
> OUT analysis output
> IN input data of the analysis
> BEAKS breaks
> CARDINAL number of elements in each class
> NAMES variables names
> METHOD analysis method
> DISTMETHOD method used in the construction of breaks
> TEST test method used (bootstrap, permutation)
> NSIM number of simulations
> PADJUST P-values adjust method for permutation tests
ACCESS TO THE SLOTS The content of the slots can be accessed with the corresponding accessors, using the generic notation of EcoGenetics (<ecoslot.> + <name of the slot> + <name of the object>). See help("EcoGenetics accessors") and the Examples section below.
Freedman D., and P. Diaconis. 1981. On the histogram as a density estimator: L 2 theory. Probability theory and related fields, 57: 453-476.
Legendre P., and L. Legendre. 2012. Numerical ecology. Third English edition. Elsevier Science, Amsterdam, Netherlands.
Oden N., and R. Sokal. 1986. Directional autocorrelation: an extension of spatial correlograms to two dimensions. Systematic Zoology, 35:608-617
Rosenberg, M. 2000. The bearing correlogram: a new method of analyzing directional spatial autocorrelation. Geographical Analysis, 32: 267-278.
Sokal R. 1986. Spatial data analysis and historical processes. In: E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone, editors. Data analysis and informatics, IV. North-Holland, Amsterdam, The Netherlands, pp. 29-43.
Sturges H. 1926. The choice of a class interval. Journal of the American Statistical Association, 21: 65-66.
# NOT RUN { data(eco.test) require(ggplot2) ############################### ## Omnidirectional correlogram ############################### corm <- eco.cormantel(M = dist(eco[["P"]]), size=1000,smax=7, XY = eco[["XY"]], nsim = 99) eco.plotCorrelog(corm) corm <- eco.cormantel(M = dist(eco[["P"]]), size=1000,smax=7, XY = eco[["XY"]], nsim = 99, test = "bootstrap") eco.plotCorrelog(corm) ####################################################### ## A directional approach based in bearing correlograms ####################################################### corm_b <- eco.cormantel(M = dist(eco[["P"]]), size=1000,smax=7, XY = eco[["XY"]], nsim = 99, angle = seq(0, 170, 10)) # use eco.plotCorrelogB for this object eco.plotCorrelogB(corm_b) # plot for the first distance class, use a number between 1 and the number of classes to select the corresponding class eco.plotCorrelogB(corm_b, interactivePlot = FALSE, var = 1) # partial Mantel correlogram corm <- eco.cormantel(M = dist(eco[["P"]]), MC = dist(eco[["E"]]), size=1000, smax=7, XY = eco[["XY"]], nsim = 99) eco.plotCorrelog(corm) # standard correlogram plots support the use of ggplot2 syntax require(ggplot2) mantelplot <- eco.plotCorrelog(corm, interactivePlot = FALSE) mantelplot <- mantelplot + theme_bw() + theme(legend.position="none") mantelplot #----------------------- # ACCESSORS USE EXAMPLE #----------------------- # the slots are accesed with the generic format # (ecoslot. + name of the slot + name of the object). # See help("EcoGenetics accessors") ecoslot.OUT(corm) # slot OUT ecoslot.BREAKS(corm) # slot BREAKS # }