# SPecies Association Analysis Miscellaneous functions for analysis of species association and niche overlap. package ## Details ||| |:--|:--| |Package:|spaa| |Type:|Package| |Version:|0.2.1| |Date:|2013-8-23| |License:|GPL-2| |LazyLoad:|yes| ## Author(s) Author: Jinlong Zhang jinlongzhang01@gmail.com Maintainer: Jinlong Zhang jinlongzhang01@gmail.com ## Examples ```r data(testdata) testdata data(splist) splist ## adding information ## add genera from dataframe B to dataframe A. add.col(inputA = testdata, inputB = splist, add = "genera", according = "species") ## add family from dataframe B to dataframe A. add.col(inputA = testdata, inputB = splist, add = "family", according = "species") ### data tranformation (spmatrix <- data2mat(testdata)) #Species association sp.assoc(spmatrix) # Species association between each pair of species (result <- sp.pair(spmatrix)) # simple network with positive lines in red and negative lines # in blue plotnetwork(result$Pearson) title("Pearson Correlation Network") # The lower matrix plot illustrating Pearson's correlation # between each pair of species Note the triangle didn't appeared # in the plots, but have been added to the legend. This is due # to the distribution of data. Be carefull in seletion of intervals. plotlowertri(result$Pearson, int = 0.5, cex=3, interval = 4, pchlist = c(19, 17, 15, 1, 5), size = TRUE) title("Pearson Correlation Lower Matrix Plot") ## plot lower matrix ## Using BCI data for lower matrix plot library(vegan) data(BCI) ## select the top 30 species according to relative frequeny. sub <- sub.sp.matrix(BCI, common = 30) ## Set the digits to 1 plotlowertri(cor(sub), size = TRUE, cex = 3, digits = 1) #### Niche width and niche overlap data(datasample) niche.overlap.boot(datasample[,1:3], method = "levins") niche.overlap(datasample, method = "levins") niche.width(datasample[,1:3], method = "shannon") ##example turnover() plotlab1 <- XYname(4,6) xxx <- 1:240 dim(xxx) <- c(24, 10) rownames(xxx) <- plotlab1 ### Distance between each pair of plots ddd <- dist(xxx) ### label matrix labmat1 <- lab.mat(plotlab1) yyy <- turnover(labmat1, ddd) ## geodist() example ## Paris L1 = deg2dec(-2,20,14) phi1 = deg2dec(48, 50, 11) ## Washington DC L2 = deg2dec(77,03,56) phi2 = deg2dec(38,55,17) ##High precision Great Circle distance geodist(L1, phi1, L2, phi2) ```

spaa-package