clusGapDiscr0: Discrete application of clusGap - core function.

Description

Based on the implementation of the function found in the `cluster` R package.

Usage

clusGapDiscr0(
  x,
  FUNcluster,
  K.max,
  B = nrow(x),
  value.range = "DS",
  verbose = interactive(),
  distName = "hamming",
  useLog = TRUE,
  Input2Alg = "distMatr",
  ...
)

Value

a matrix with K.max rows and 4 columns, named "logW", "E.logW", "gap", and "SE.sim", where gap = E.logW - logW, and SE.sim correspond to the standard error of `gap`.

Arguments

x: A matrix object specifying category attributes in the columns and observations in the rows.
FUNcluster: a function that accepts as first argument a matrix like `x`; second argument specifies number of `k` (k=>2) clusters This function should return a list with a component named `cluster`, a vector of length `n=nrow(x)` of integers from `1:k` indicating observation cluster assignment. Make sure `FUNcluster` and `Input2Alg` agree.
K.max: Integer. Maximum number of clusters `k` to consider
B: Number of bootstrap samples. By default B = nrow(x).
value.range: String, character vector or a list of character vectors with the length matching the number of columns (nQ) of the array. A vector with all categories to consider when bootstrapping the null distribution sample (KS: Known Support option). By DEFAULT vals=NULL, meaning unique range of categories found in the data will be used when drawing the null (DS: Data Support option). If a character vector of categories is provided, these values would be used for the null distribution drawing across the array. If a list with category character vectors is provided, it has to have the same number of columns as the input array. The order of list element corresponds to the array's columns.
verbose: Integer or logical. Determines whether progress output should printed while running. By DEFAULT one bit is printed per bootstrap sample.
distName: String. Name of categorical distance to apply. Available distances: 'bhattacharyya', 'chisquare', 'cramerV', 'hamming' and 'hellinger'.
useLog: Logical. Use log function after estimating `W.k`. Following the original formulation `useLog=TRUE` by default.
Input2Alg: Specifies the kind of input provided to the algorithm function in `FUNcluster`. For algorithms that only accept a distance matrix use `'distMatr'` option (default). For algorithms that require the dataset and a prespecified distance function (e.g. `stats::dist`) use the `'distFun'` option. This case the distance function is defined internally and determined by parameter `distName`.
...: optionally further arguments for `FUNcluster()`