spaa-package {spaa}

SPecies Association Analysis

Description

Miscellaneous functions for analysis of species association and niche overlap.

Details

Package:	spaa
Type:	Package
Version:	0.2.1
Date:	2013-8-23
License:	GPL-2
LazyLoad:	yes

Author(s)

Author: Jinlong Zhang [e-mail]

Maintainer: Jinlong Zhang [e-mail]

Examples

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)