plotUncertainty: Plot I-splines With Error Bands Using Bootstrapping.

Description

This function estimates uncertainty in the fitted I-splines by fitting many GDMs using a subsample of the data. 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 I-spline coefficients and (2) a rug plot indicating how sites used in model fitting are distributed along each gradient. Function result optionally can be saved to disk as a csv for custom plotting, etc. The result output table will have 6 columns per predictor, three each for the x and y values containing the lower bound, full model, and upper bound.

Usage

plotUncertainty(spTable, sampleSites, bsIters, geo=FALSE,
splines=NULL, knots=NULL, splineCol="blue", errCol="grey80",
plot.linewidth=2.0, plot.layout=c(2,2), parallel=FALSE, cores=2, save=FALSE,
fileName="gdm.plotUncertainy.csv")

Value

plotUncertainty returns NULL. Saves a csv to disk if save=TRUE.

Arguments

spTable: A site-pair table, same as used to fit a gdm.
sampleSites: The fraction (0-1) of sites to retain 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: Perform the uncertainty assessment using multiple cores? Default = 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.
save: Save the function result (e.g., for custom plotting)? Default=FALSE.
fileName: Name of the csv file to save the data frame that contains the function result. Default = gdm.plotUncertainy.csv. Ignored if save=FALSE.

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.

Examples

Run this code

##set up site-pair table using the southwest data set
sppData <- southwest[c(1,2,13,14)]
envTab <- southwest[c(2:ncol(southwest))]
sitePairTab <- formatsitepair(sppData, 2, XColumn="Long", YColumn="Lat",
                              sppColumn="species", siteColumn="site", predData=envTab)

##plot GDM uncertainty using one core
#not run
#plotUncertainty(sitePairTab, sampleSites=0.70, bsIters=5, geo=TRUE, plot.layout=c(3,3))

##plot GDM uncertainty in parallel
#not run
#plotUncertainty(sitePairTab, sampleSites=0.70, bsIters=50, geo=TRUE, plot.layout=c(3,3),
                 #parallel=T, cores=10)