Estimate the maximum number of groups in DAPC analysis

Estimate the maximum number of groups in DAPC analysis

Usage

find_max_nclust(
  x,
  threshold,
  max.nclust,
  nperm = 100,
  method = "kmeans",
  stat = "BIC",
  criterion = "diffNgroup",
  subset = 100,
  confidence.level = c(0.7, 0.8, 0.9, 0.95, 0.99)
)

Arguments

x

A data.frame or matrix object containing eigenvectors by sites.

threshold

Scalar. The number of eigenvectors used to perform classification.

max.nclust

A vector containing values of the maximum number of groups to be evaluated.

nperm

Scalar. Number of times classification will be performed.

method

Character, one of c("kmeans","ward"). This will be used in find.clusters function. See find.clusters of adegenet package. Default is "kmeans"

stat

Character, one of c("BIC", "AIC", or "WSS"). This will be used in find.clusters function. See find.clusters of adegenet package. Default is "BIC".

criterion

Character one of c("diffNgroup", "min","goesup", "smoothNgoesup", or "goodfit"). This will be used in find.clusters function. Default is "diffNgroup". See find.clusters of adegenet package.

subset

Scalar. The number of cells used in the analysis. It is particularly important whenever the total number of cells is large (> 1000).

confidence.level

A vector containing values with threshold confidence level used to estimate congruence in the classification pattern.

Value

Matrix containing congruence values ranging between 0-1 for each max.nclust value (see Arguments) and confidence level.

Details

Additional details...

Examples

if (FALSE) { # \dontrun{
data(regions)
evovectors <- regions$PCPS$vectors # eigenvectors by site
find_max_nclust(x = evovectors, threshold = 3, max.nclust = 10)
} # }