Plot I-splines With Error Bands Using Bootstrapping.

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")

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.

Value

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

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, subsample.sitepair

Examples

##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)
#> Warning: No abundance column was specified, so the biological data are assumed to be presences.
#> Aggregation function missing: defaulting to length

##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)