Description Usage Arguments Value Author(s) References Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | 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.
Leandro Roser learoser@gmail.com
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ## 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
## End(Not run)
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