dewlap_neural: Habitat classification with neural networks
In rscherrer/sagreicolor:

Description Usage Arguments Value Author(s)

This function trains neural networks to recognize differences between habitats. Each neural network is trained on a random sample of half of the data, and tested against the other half. The success of the classification is compared to a null expectation generated from a permuted dataset where no differences exist between habitats. The 5% best performing machines are studied more in depth to identify what were the most important variables in discriminating between habitats.

1	dewlap_neural(specdata, vars, nRepet = 1000, seed)

`specdata`	A data frame containing at least columns for the dependent variables, as well as a column "habitat".
`vars`	A character or integer vector. The names, or indices, of the dependent variables in `specdata`.
`nRepet`	The number of neural networks to train (same number for empirical and permuted datasets).
`seed`	Seed for random number gnerators

A list of two data frames: one contains success and p-values for randomized and empirical data, the other contains the importance variables in the 5% best classifiers.

Raphael Scherrer

rscherrer/sagreicolor documentation built on Dec. 26, 2018, 1:15 p.m.