Description Usage Arguments Details Value Author(s) References See Also Examples
Function lagSelect provides multiple community correlograms of varying lag sizes to assist in optimizing lag size and number.
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sampleData |
A matrix or dataframe with samples as rows and species or populations as columns |
sampleLocation |
A matrix or dataframe of xyz, xy (surface) plane and the z (depth) plane, geographical coordinates for objects in |
sampleTime |
A numeric, date, or POSIX format vector of sample collection times for the objects in |
LocationNames |
A character vector of location names for the objects in |
lagmin |
Minimum lag size to compute in the units of distance (for options 1 and 3) or time (for options 2 and 4) |
lagmax |
Maximum lag size to compute in the units of distance (for options 1 and 3) or time (for options 2 and 4) |
by |
Number to increment tested lag sizes by |
option |
A switch specifying type of correlogram to be determined (spatial, temporal, or a combination of both). Options include: 1 = spatial analysis only (provide |
numTests |
Number of permutations used to calculate significance. Default = 99. |
plot |
A switch specifying whether to plot the |
anisotropic |
A switch specifying whether an anisotropic analysis should be performed. Default = F. The user is advised to consider whether an anisotropic analsysis is appropriate for their particular dataset and specify a value for |
... |
Other parameters passed to |
Optimization of lag size is critical for geostatistical analyses (Goovaerts, 1997). This function provides correlograms over a range of lag distances within user specified minimum and maximum distances, calculated using commcorrelogram().
Some general rules of thumb exist for selection of lag size and number (Journel and Huijbregts, 1978; Legendre and Fortin, 1989):
1. The lag distance must be larger than the smallest sampling distance.
2. A minimum of 30 sample pairs per lag distance is recommended.
3. The maximum distance class should be no more than 2/3 the total sampling site distance.
It is helpful when using this function to use a small value for numTests, to improve speed and reduce computational intensity.
Returns a list of objects of class community.correlogram, each with different lag size used to compute them.
Plots of community correlogram metrics and significance values are created for each lag size tested when plot = T.
J. Malia Andrus, Timothy Kuehlhorn, Luis F. Rodriguez, Angela D. Kent, and Julie L. Zilles
Maintainer: J. Malia Andrus <jmaliaandrus@gmail.com>
Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford, England: Oxford University Press.
Journel, A. G. and C. J. Huijbregts. 1978. Mining Geostatistics. San Diego, CA: Academic Press.
Legendre, P. and M. J. Fortin. 1989. Spatial Pattern and Ecological Analysis. Vegetatio 80(2): 107-138.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ## Not run:
#spatial lag selection
data(mite)
data(mite.xy)
lagSelect(sampleData=mite,sampleLocation=cbind(mite.xy,z=0),lagmin=0.1
,lagmax=1,by=0.1,numTests=9)
#temporal lag selection
data(pyrifos)
pyrifos.levels<-data.frame(ditch=gl(12,1,length=132),
dose=factor(rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6),11)),
week= as.numeric(as.character(gl(11, 12,
labels=c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24)))))
lagSelect(sampleData=pyrifos,sampleTime=pyrifos.levels$week,
option=2,lagmin=1,lagmax=6,by=1,numTests=9)
## End(Not run)
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