Importing data is done by reading the DataReg.txt file that contains the TTGE presence-absence data for the 87 pools. The corresponding data is stored in the br1 dataframe. Columns of zeroes are removed and the resulting table is stored in dataframe br1sz. Farm codes are read from file FarmReg.txt, and pool numbers for each farm are read from file FarmRegNum.txt. The farms factor is build from these two objects. It is used to compute the between-farms BGA and plot Figure 3 of the paper.
The regions factor is read from file Regions.txt. It is used to build the repreg factor, which is needed to compute the between-regions permutation test.
#
# BGA on geographical effect
#
br1 <- read.table("DataReg.txt", h=T)
br1sz <- br1[,colSums(br1)!=0]
fnum <- scan("FarmRegNum.txt")
fnames <- scan("FarmReg.txt", what="character")
farms <- as.factor(rep(fnames, fnum))
regions <- as.factor(scan("Regions.txt", what="character"))
repreg <- rep(regions, fnum)
The PCA and the BGA are then computed using the dudi.pca and bca functions of the ade4 package. The corresponding figure (Figure 3 of the paper) is drawn using the s.class funtion and is presented below.
#
# PCA and BGA computation and plot
#
acp1 <- dudi.pca(br1sz, scale=F, scan=F)
betfarms <- bca(acp1, farms, scan=F)
s.class(betfarms$ls, farms, cell=0, cpoint=0)
The permutation test is done with the randtest function:
#
# Between-regions permutation test
#
betregs <- bca(acp1, repreg, scan=F, nf=1)
print(randtest(betregs))
You can click on the "Do it again !" button to execute the R code contained in the text field under the figure. All the computations are redone automatically and the factor map is plotted.
Importing data is done by reading the DataHS.txt file that contains the TTGE presence-absence data for the 65 pools (13 farms x 5 pools for each farm). The corresponding data is stored in the pasr1 dataframe. Columns of zeroes are removed and the resulting table is stored in dataframe pasr1sz. Farm codes are read from file FarmHS.txt. The number of pools is equal to 5 for all the farms. The farmsPASR factor is build from these two objects. It is used to compute the between-farms BGA and plot Figure 4 of the paper.
#
# BGA on habitat-specific effect
#
pasr1 <- read.table("DataHS.txt",h=T)
pasr1sz <- pasr1[,colSums(pasr1)!=0]
fnamesPASR <- scan("FarmHS.txt", what="character")
farmsPASR <- as.factor(rep(fnamesPASR, 5))
The PCA and the BGA are then computed using the dudi.pca and bca functions of the ade4 package. The corresponding figure (Figure 4 of the paper) is drawn using the s.class funtion and is presented below.
#
# PCA and BGA computation and plot
#
acpPASR <- dudi.pca(pasr1sz, scan=F)
betPASR <- bca(acpPASR, farmsPASR, scan=F)
s.class(dfxy = betPASR$ls, fac = farmsPASR, cellipse = 0, cpoint = 0)
The permutation test is done with the randtest function, after computing a new BGA with the fPASR factor, which is read from file CodePAS.txt.
#
# Between-habitat permutation test
#
fPASR <- as.factor(scan("CodePAS.txt", what="character"))
betPASRv <- bca(acpPASR, fPASR, scan=F, nf=1)
print(randtest(betPASRv))
You can click on the "Do it again !" button to execute the R code contained in the text field under the figure. All the computations are redone automatically and the factor map is plotted.
Importing data is done by reading the DataRegHS.txt file that contains the TTGE presence-absence data for the 127 pools (26 farms x 5 pools for each farm less 3 pools missing in two farms). The corresponding data is stored in the glob dataframe. Columns of zeroes are removed and the resulting table is stored in dataframe globsz. Farm codes are read from file FarmRegHS.txt, and pool numbers for each farm are read from file FarmRegHSNum.txt. The farmsG factor is build from these two objects. It is used to compute the between-farms BGA and plot Figure 5 of the paper. The typeG factor is read from file TypeRegHS.txt. It is used to build the ftypeG factor, which is used itself to compute the permutation test.
#
# Inter-region and inter-habitat comparisons
#
glob <- read.table("DataRegHS.txt", h=T)
globsz <- glob[,colSums(glob)!=0]
fnamesG <- scan("FarmRegHS.txt", what="character")
fnumG <- scan("FarmRegHSNum.txt", what="character")
farmsG <- as.factor(rep(fnamesG, fnumG))
typeG <- scan("TypeRegHS.txt", what="character")
typeG <- as.factor(typeG)
ftypeG <- rep(typeG, fnumG)
The PCA and the BGA are then computed using the dudi.pca and bca functions of the ade4 package. The corresponding figure (Figure 5 of the paper) is drawn using the s.class funtion and is presented below.
#
# PCA and BGA computation and plot
#
acpG <- dudi.pca(globsz, scale=F, scan=F)
betfarmsG <- bca(acpG, farmsG, scan=F)
bettypeG <- bca(acpG, ftypeG, scan=F)
s.class(dfxy = bettypeG$ls, fac = farmsG, cellipse = 0, cpoint = 0)
s.class(dfxy = bettypeG$ls, fac = ftypeG, add.plot=T, cstar=0, cpoint=0, clabel=0, axesell=F)
The permutation test is done with the randtest function.
#
# Permutation test
#
print(randtest(bettypeG))
You can click on the "Do it again !" button to execute the R code contained in the text field under the figure. All the computations are redone automatically and the factor map is plotted.