SigDiff {SDMTools} | R Documentation |
SigDiff
computes the significance of the pairwise differences relative to
the mean and variance of all differences between the two input datasets. This is
useful for identifying regions of significant difference between two datasets
(e.g., different DEMs (Januchowski et al. 2010) or different species
distribution model predictions (Bateman et al 2010)).
ImageDiff
is a wrapper to the image.asc command in adehabitat package that
uses the result from SigDiff
to create an image mapping the regions of
significant differences (positive and negative).
NOTE: it is assumed the input data are of the same extent and cellsize.
SigDiff(x,y,pattern=TRUE) ImageDiff(tasc,sig.levels=c(0.025,0.975),tcol=terrain.colors(3),...)
x |
a vector or matrix of data |
y |
a vector or matrix of data with the same dimensions of 'x' |
pattern |
logical value defining if differences are respective to relative patterning (TRUE) or absolute values (FALSE) |
tasc |
a matrix of probability values (0 to 1) likely created by SigDiff |
sig.levels |
the significance levels to define significantly above and below. Default settings represent significance at the 0.05 level |
tcol |
a set of 3 colors for use in the image to represent significantly lower or greater, and not significant |
... |
other graphical parameters defined by image() or plot() |
SigDiff
returns a vector or matrix of the same dimensions of the input
representing the significance of the pairwise difference relative to the mean
and variance of all differences between the two inputs.
ImageDiff
returns nothing but creates an image of the areas of
significant differences
Stephanie Januchowski stephierenee@gmail.com
Januchowski, S., Pressey, B., Vanderwal, J. & Edwards, A. (2010) Characterizing errors in topographic models and estimating the financial costs of accuracy. International Journal of Geographical Information Science, In Press.
Bateman, B.L., VanDerWal, J., Williams, S.E. & Johnson, C.N. (2010) Inclusion of biotic interactions in species distribution models improves predictions under climate change: the northern bettong Bettongia tropica, its food resources and a competitor. Journal of Biogeography, In Review.
#create some simple objects of class 'asc' tasc = as.asc(matrix(1:50,nr=50,nc=50)); print(tasc) #modify the asc objects so that they are slightly different tasc1 = tasc + runif(n = 2500, min = -1, max = 1) tasc2 = tasc + rnorm(n = 2500, mean = 1, sd = 1) #create graphical representation par(mfrow=c(2,2),mar=c(1,1,4,1)) image(tasc1,main='first grid',axes=FALSE) image(tasc2,main='second grid',axes=FALSE) #get significant difference by spatial patterning out = SigDiff(tasc1,tasc2) ImageDiff(out,main="Pattern Differences",axes=FALSE) #get significant difference by spatial patterning out = SigDiff(tasc1,tasc2,pattern=FALSE) ImageDiff(out,main="Absolute Differences",axes=FALSE) legend('topleft',legend=c('-ve','ns','+ve'),title='significance', fill=terrain.colors(3),bg='white') #close the graphics window resetting the parameters dev.off()