gs.xval.split: Cross-Validation Data Splitter
In neurodata/graphstats: Graph Statistics

Description Usage Arguments Value Author(s) Examples

gs.xval.split A function to split a dataset into training and testing sets for cross validation.

1	gs.xval.split(graphs, Y, k = "loo", ...)

`graphs`	`[n, d, d]` or `[[n]][v, v]` the data with `n` samples of `v` vertices.
`Y`	`[n]` the labels of the samples with `K` unique labels.
`k`	the cross-validated method to perform. Defaults to `'loo'`. `'loo'` Leave-one-out cross validation `isinteger(k)` perform `k`-fold cross-validation with `k` as the number of folds.
`...`	trailing args.

sets the cross-validation sets as an object of class "XV". Each element of the list contains the following items:

`graphs.train`	the training data as a `[[n - k]][v, v]` list.
`Y.train`	the training labels as a `[n - k]` vector.
`graphs.test`	the testing data as a `[[k]][v, v]` list.
`Y.test`	the testing labels as a `[k]` vector.

Eric Bridgeford

# prepare data for 10-fold validation
library(graphstats)
data <- gs.sims.er(n=100, v=10, priors=c(1), p=c(0.5))  # 100 examples of 10x10 graphs
graphs <- data$graphs; Y <- data$Y
sets.xval.10fold <- lol.xval.split(graphs, Y, k=10)

# prepare data for loo validation
sets.xval.loo <- lol.xval.split(graphs, Y, k='loo')