discr.test.one_sample: Discriminability One Sample Permutation Test
In mgc: Multiscale Graph Correlation

Description Usage Arguments Value Details Author(s) References Examples

A function that performs a one-sample test for whether the discriminability differs from random chance.

discr.test.one_sample(
  X,
  Y,
  is.dist = FALSE,
  dist.xfm = mgc.distance,
  dist.params = list(method = "euclidean"),
  dist.return = NULL,
  remove.isolates = TRUE,
  nperm = 500,
  no_cores = 1
)

`X`	is interpreted as: a `[n x d]` data matrix X is a data matrix with `n` samples in `d` dimensions, if flag `is.dist=FALSE`. a `[n x n]` distance matrix X is a distance matrix. Use flag `is.dist=TRUE`.
`Y`	`[n]` a vector containing the sample ids for our `n` samples.
`is.dist`	a boolean indicating whether your `X` input is a distance matrix or not. Defaults to `FALSE`.
`dist.xfm`	if `is.dist == FALSE`, a distance function to transform `X`. If a distance function is passed, it should accept an `[n x d]` matrix of `n` samples in `d` dimensions and return a `[n x n]` distance matrix as the `$D` return argument. See mgc.distance for details.
`dist.params`	a list of trailing arguments to pass to the distance function specified in `dist.xfm`. Defaults to `list(method='euclidean')`.
`dist.return`	the return argument for the specified `dist.xfm` containing the distance matrix. Defaults to `FALSE`. `is.null(dist.return)` use the return argument directly from `dist.xfm` as the distance matrix. Should be a `[n x n]` matrix. `is.character(dist.return) \| is.integer(dist.return)` use `dist.xfm[[dist.return]]` as the distance matrix. Should be a `[n x n]` matrix.
`remove.isolates`	remove isolated samples from the dataset. Isolated samples are samples with only one instance of their class appearing in the `Y` vector. Defaults to `TRUE`.
`nperm`	the number of permutations to perform. Defaults to `500`.
`no_cores`	the number of cores to use for permutation test. Defaults to `1`.

A list containing the following:

`stat`	the discriminability of the data.
`null`	the discriminability scores under the null, computed via permutation.
`p.value`	the pvalue associated with the permutation test.

Performs a test of whether an observed discriminability is significantly different from chance, as described in Bridgeford et al. (2019). With Dhatx the sample discriminability of X:

H0: Dx = D0

and:

Ha: Dx > D0

where D0 is the discriminability that would be observed by random chance.

Eric Bridgeford

Eric W. Bridgeford, et al. "Optimal Decisions for Reference Pipelines and Datasets: Applications in Connectomics." Bioarxiv (2019).

## Not run: 
require(mgc)
n = 100; d=5

# simulation with a large difference between the classes
# meaning they are more discriminable
sim <- discr.sims.linear(n=n, d=d, K=2, signal.lshift=10)
X <- sim$X; Y <- sim$Y

# p-value is small
discr.test.one_sample(X, Y)$p.value

## End(Not run)