CoMLsimulation {gradientForest} | R Documentation |
Simulated datasets for demonstrating gradient forest method.
data(CoMLsimulation)
A data frame with 100 observations on 10 variables.
A, B
influential variables, each generated uniformly on [0-1]
C-J
uninfluential variables, each generated uniformly on [0-1]
A matrix of simulated species counts on the 100 sites in Xsimulation. The species
a1-a3
respond to variable A
, b1-b4
respond to variable B
and
species ab1-ab5
respond jointly to both A
and B
.
The species data are generated as Poissons with intensity shaped like normal
curves along A
and B
gradients.
Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.
data(CoMLsimulation) names_a <- paste("a",1:3,sep="") names_b <- paste("b",1:4,sep="") par(mfrow=c(2,1),mar=c(3,4,3,1)) matplot(Xsimulation$A,Ysimulation[,names_a],main=substitute(a[1-3]),xlab="A", ylab="Abundance",axes=FALSE,col=c("black","orange","blue","green")) mtext("A",side=1,line=1) axis(2) box() matplot(Xsimulation$B,Ysimulation[,names_b],main=substitute(b[1-4]),xlab="B", ylab="Abundance",axes=FALSE,col=c("black","orange","blue","green")) mtext("B",side=1,line=1) axis(2) box()