cumimp.combinedGradientForest: Cumulative Importance of predictors for a...

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Provide access to the cumulative importance values of predictors after combining gradientForest objects.

Usage

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## S3 method for class 'combinedGradientForest'
cumimp(x, predictor, weight = c("uniform", "species", "rsq.total", 
"rsq.mean", "site", "site.species", "site.rsq.total", "site.rsq.mean")[3], gf.name, ...)

Arguments

x

an object of class combinedGradientForest generated by combinedGradientForest.

predictor

the name of the predictor variable.

weight

type of weighting to perform across gradientForest objects. The choices are

  • uniform: each gradientForest object gets weight 1

  • species: each gradientForest object gets weight equal to the number of species

  • rsq.total: each gradientForest object gets weight equal to the total R^2 (default)

  • rsq.mean: each gradientForest object gets weight equal to the mean R^2 per species

  • site: each gradientForest object gets weight equal to the number of sites

  • site.species: each gradientForest object gets weight equal to the number of species times the number of sites

  • site.rsq.total: each gradientForest object gets weight equal to the total R^2 times the number of sites

  • site.rsq.mean: each gradientForest object gets weight equal to the mean R^2 per species times the number of sites

gf.name

name of gradientForest object required. If supplied, provide the cumulative importance for the specific gradientForest object. weight is ignored in this case.

...

further arguments passed to or from other methods.

Details

Cumulative importance curves are found for each predictor by weighting across the gradientForest objects. See the section Compositional turnover and equation (9) in Ellis et al (2012).

Value

If gf.name is missing, a list with components x and y, where x is the predictor values and y is the combined cumulative importance using the specified weighting across gradientForest objects. If gf.name is supplied, the list for the predictor from the specified gradientForest object is returned.

Author(s)

N. Ellis, CSIRO, Cleveland, Australia. <[email protected]>. S.J. Smith, DFO, Dartmouth, NS, Canada. <[email protected]>

References

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

See Also

combinedGradientForest

Examples

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data(CoMLsimulation)
preds <- colnames(Xsimulation)
specs <- colnames(Ysimulation)
f1 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6], ntree=10)
f2 <- gradientForest(data.frame(Ysimulation,Xsimulation), preds, specs[1:6+6], ntree=10)
f12 <- combinedGradientForest(west=f1,east=f2)
par(mfrow=c(1,3))
plot(cumimp(f12, "A", gf.name="west"),main="West")
plot(cumimp(f12, "A", gf.name="east"),main="East")
plot(cumimp(f12, "A", weight = "rsq.total"),main="Combined")

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