社区矩阵上的多元随机森林

Question

I want to use random forest modeling to understand variable importance on community assembly - my response data is a community matrix.

library(randomForestSRC)

# simulated species matrix
species 
# site       species 1    species2     species 3
# 1             1            1            0
# 2             1            0            1
# 3             1            1            1
# 4             1            0            1
# 5             1            0            0
# 6             1            1            0
# 7             1            1            0
# 8             1            0            0
# 9             1            0            0
# 10            1            1            0


# environmental data
data
# site   elevation_m     PRECIPITATION_mm  
# 1        500                28
# 2        140                37
# 3        445                15
# 4        340                45
# 5        448                20
# 6        55                 70
# 7        320                18
# 8        200                42
# 9        420                22
# 10       180                8


# adding my species matrix into the environmental data frame
data[["species"]] <-(species)

# running the model
rf_model <- rfsrc(Multivar(species) ~.,data = data, importance = T)

but I'm getting an error message:

Error in parseFormula(formula, data, ytry) : 
  the y-outcome must be either real or a factor.

I'm guessing that the issue is the presence/absence data, but I'm not sure how to move past that. Is this a limitation of the function?

Answer 1

I think it MIGHT have to do with how you built your "data" data frame. When you used data[["species"]] <- (species) , you had a data frame inside a data frame. If you str(data) after the step I just referred to, the output is this:

> str(data)
'data.frame':   10 obs. of  4 variables:
$ site     : int  1 2 3 4 5 6 7 8 9 10
$ elevation: num  500 140 445 340 448 55 320 200 420 180
$ precip   : num  28 37 15 45 20 70 18 42 22 8
$ species  :'data.frame':      10 obs. of  4 variables: #2nd data frame
..$ site     : int  1 2 3 4 5 6 7 8 9 10
..$ species.1: num  1 1 1 1 1 1 1 1 1 1
..$ species2 : num  1 0 1 0 0 1 1 0 0 1
..$ species.3: num  0 1 1 1 0 0 0 0 0 0

If you instead build your data frame as data2 <- as.data.frame(cbind(data,species)) , then

rfsrc(Multivar(species.1,species2,species.3)~.,data = data2, importance=T)

seems to work because I don't get an error message, instead I get some reasonable looking output:

Sample size: 10
Number of trees: 1000
Forest terminal node size: 5
Average no. of terminal nodes: 2
No. of variables tried at each split: 2
Total no. of variables: 4
Total no. of responses: 3
User has requested response: species.1
Resampling used to grow trees: swr
Resample size used to grow trees: 10
Analysis: mRF-R
Family: regr+
Splitting rule: mv.mse *random*
Number of random split points: 10
% variance explained: NaN
Error rate: 0   

I don't think your method for building the data frame you wanted is the customary way, but I could be wrong. I think rfsrc() did not know how to read a nested data frame. I doubt most modeling functions do without extra customized code.

Answer 2

Here's an example, using example data from the vegan package, of automatically constructing a formula that includes all of the species names in the response:

library(vegan)
library(randomForestSRC)
data("dune.env")
data("dune")

all <- as.data.frame(cbind(dune,dune.env))
form <- formula(sprintf("Multivar(%s) ~ .",
                        paste(colnames(dune),collapse=",")))

rfsrc(form, data=all)

Suppose we want to do this with 2000 species. Here's a simulated example:

nsp <- 2000
nsamp <- 100
nenv <- 10
set.seed(101)
spmat <- matrix(rpois(nsp*nsamp, lambda=5), ncol=nsp,
                dimnames=list(NULL,paste0("sp",seq(nsp))))
envmat <- matrix(rnorm(nenv*nsamp), ncol=nenv,
                dimnames=list(NULL,paste0("env",seq(nenv))))

all2 <- as.data.frame(cbind(spmat,envmat))
form2 <- formula(sprintf("Multivar(%s) ~ .",
                        paste(colnames(spmat),collapse=",")))

rfsrc(form2, data=all2)

In this particular example we seem to explain -3% (!!) of the variance, but it doesn't crash, so that's a good thing ...