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

This function can be used to find the maximum number of clusters that maximizes the congruence of the grouping procedure. Basically it consists in repeat DAPC analysis multiple times using a set of candidate maximum values (indicated in the argument max.nclust)

Examples

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