Plot variance of I-splines using bootstrapping.

Description

This function estimates uncertainty in the I-splines using bootstrapping. The function can run in parallel on multicore machines to reduce computation time (recommended for large number of iterations). I-spline plots with error bands (+/- one standard deviation) are produced showing (1) the variance of Ispline coefficients and (2) a rug plot indicating how sites used in model fitting are distributed along each gradient.

Usage

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plotUncertainty(spTable, leaveOut, bsIters, geo=FALSE, splines=NULL, knots=NULL, 
                splineCol="blue", errCol="grey80", plot.linewidth=2.0, plot.layout=c(2,2),
                parallel=FALSE, cores=2)

Arguments

spTable

A site-pair table, same as used to fit a gdm.

leaveOut

The fraction (0-1) of sites to remove from the full site-pair table when subsampling.

bsIters

The number of bootstrap iterations to perform.

geo

Same as the gdm geo argument.

splines

Same as the gdm splines argument.

knots

Same as the gdm knots argument.

splineCol

The color of the plotted mean spline. The default is "blue".

errCol

The color of shading for the error bands (+/- one standard deviation around the mean line). The default is "grey80".

plot.linewidth

The line width of the plotted mean spline line. The default is 2.

plot.layout

Same as the plot.gdm plot.layout argument.

parallel

Whether or not to run the uncertainty estimation in parallel. The parallel processing is done using a foreach loop and it is highly recommended when the bsIters argument is hundreds or more. When is argument is set to FALSE, the processes are completed using lapply. The default is FALSE.

cores

When the parallel argument is set to TRUE, the number of cores to be registered for the foreach loop. Must be <= the number of cores in the machine running the function.

Value

plotUncertainty returns NULL.

References

Shryock, D. F., C. A. Havrilla, L. A. DeFalco, T. C. Esque, N. A. Custer, and T. E. Wood. 2015. Landscape genomics of Sphaeralcea ambigua in the Mojave Desert: a multivariate, spatially-explicit approach to guide ecological restoration. Conservation Genetics 16:1303-1317.

See Also

plot.gdm, formatsitepair

Examples

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##sets up site-pair table
load(system.file("./data/gdm.RData", package="gdm"))
sppData <- gdmExpData[c(1,2,13,14)]
envTab <- gdmExpData[c(2:ncol(gdmExpData))]
sitePairTab <- formatsitepair(sppData, 2, XColumn="Long", YColumn="Lat", 
                              sppColumn="species", siteColumn="site", predData=envTab)

##plot GDM uncertainty using one core
#not run
#plotUncertainty(sitePairTab, leaveOut=0.30, bsIters=5, geo=TRUE, plot.layout=c(3,3))

##plot GDM uncertainty in parallel
#not run
#plotUncertainty(sitePairTab, leaveOut=0.30, bsIters=50, geo=TRUE, plot.layout=c(3,3),
#parallel=T, cores=10)