Check that the obtained groups are stable by bootstrap with multicore support

boot_group_validation.mc(
  object,
  nsim = 1000,
  maxiter = 1000,
  tol = 0.01,
  init = "random",
  verbose = TRUE,
  seed = NULL
)

Arguments

object

A FCMres object, typically obtained from functions CMeans, GCMeans, SFCMeans, SGFCMeans

nsim

The number of replications to do for the bootstrap evaluation

maxiter

An integer for the maximum number of iterations

tol

The tolerance criterion used in the evaluateMatrices function for convergence assessment

init

A string indicating how the initial centres must be selected. "random" indicates that random observations are used as centres. "kpp" use a distance based method resulting in more dispersed centres at the beginning. Both of them are heuristic.

verbose

A boolean to specify if the progress bar should be displayed.

seed

An integer to control randomness, default is NULL

Value

A list of two values: group_consistency: a dataframe indicating the consistency across simulations each cluster ; group_centres: a list with a dataframe for each cluster. The values in the dataframes are the centres of the clusters at each simulation.

Details

For more details, see the documentation of the function boot_group_validation

Examples

if (FALSE) {
data(LyonIris)

#selecting the columns for the analysis
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14",
                   "Pct_65","Pct_Img","TxChom1564","Pct_brevet","NivVieMed")

#rescaling the columns
Data <- sf::st_drop_geometry(LyonIris[AnalysisFields])
for (Col in names(Data)){
  Data[[Col]] <- as.numeric(scale(Data[[Col]]))
}

Cmean <- CMeans(Data,4,1.5,500,standardize = FALSE, seed = 456,
    tol = 0.00001, verbose = FALSE)

future::plan(future::multisession(workers=2))

validation <- boot_group_validation.mc(Cmean, nsim = 1000, maxiter = 1000,
    tol = 0.01, init = "random")
## make sure any open connections are closed afterward
if (!inherits(future::plan(), "sequential")) future::plan(future::sequential)
}