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.
1 2 |
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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