Description Usage Arguments Value Details Author(s) References Examples
A function for computing the discriminability from a distance matrix and a set of associated labels.
1 2 3 4 5 6 7 8 9 | discr.stat(
X,
Y,
is.dist = FALSE,
dist.xfm = mgc.distance,
dist.params = list(method = "euclidean"),
dist.return = NULL,
remove.isolates = TRUE
)
|
X |
is interpreted as:
|
Y |
|
is.dist |
a boolean indicating whether your |
dist.xfm |
if |
dist.params |
a list of trailing arguments to pass to the distance function specified in |
dist.return |
the return argument for the specified
|
remove.isolates |
remove isolated samples from the dataset. Isolated samples are samples with only
one instance of their class appearing in the |
A list containing the following:
|
the discriminability statistic. |
|
the rdfs for each sample. |
For more details see the help vignette:
vignette("discriminability", package = "mgc")
Eric Bridgeford
Eric W. Bridgeford, et al. "Optimal Decisions for Reference Pipelines and Datasets: Applications in Connectomics." Bioarxiv (2019).
1 2 3 | sim <- discr.sims.linear(100, 10, K=2)
X <- sim$X; Y <- sim$Y
discr.stat(X, Y)$discr
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.