# CoMLsimulation: Simulated site-species data In slarge/gradientForest: Random Forest functions for the Census of Marine Life synthesis project.

## Description

Simulated datasets for demonstrating gradient forest method.

## Usage

 `1` ```data(CoMLsimulation) ```

## Details

Xsimulation

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]

Ysimulation

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.

## References

Ellis, N., Smith, S.J., and Pitcher, C.R. (2012) Gradient Forests: calculating importance gradients on physical predictors. Ecology, 93, 156–168.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```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() ```

slarge/gradientForest documentation built on May 3, 2019, 4:05 p.m.