MPFSS: MPFSS scan procedure

In HDSpatialScan: Multivariate and Functional Spatial Scan Statistics

MPFSS scan procedure

Description

This function computes the MPFSS (Parametric Multivariate Functional scan statistic).

Usage

MPFSS(
  data,
  MC = 999,
  typeI = 0.05,
  method = c("LH", "W", "P", "R"),
  nbCPU = 1,
  variable_names = NULL,
  times = NULL,
  initialization,
  permutations
)

Arguments

data	list of numeric matrices. List of nb_sites (or nb_individuals if the observations are by individuals and not by sites) matrices of the data, the rows correspond to the variables and each column represents an observation time. The times must be equally spaced and the same for each site/individual.
MC	numeric. Number of Monte-Carlo permutations to evaluate the statistical significance of the clusters. By default: 999.
typeI	numeric. The desired type I error. A cluster will be evaluated as significant if its associated p-value is less than typeI. By default 0.05.
method	character vector. The methods to compute the significant clusters. Options: "LH", "W", "P", "R" for respectively the Lawley-Hotelling trace test statistic, The Wilks lambda test statistic, the Pillai trace test statistic and the Roy's maximum root test statistic. By default all are computed.
nbCPU	numeric. Number of CPU. If nbCPU > 1 parallelization is done. By default: 1.
variable_names	character. Names of the variables. By default NULL.
times	numeric. Times of observation of the data. By default NULL.
initialization	list. Initialization for the scan procedure (see InitScan for more details).
permutations	matrix. Indices of permutations of the data.

Value

List of objects of class ResScanOutputMultiFunct (one element by method)

References

Camille Frévent and Mohamed-Salem Ahmed and Sophie Dabo-Niang and Michaël Genin (2023). Investigating Spatial Scan Statistics for Multivariate Functional Data. Journal of the Royal Statistical Society Series C: Applied Statistics, 72(2), 450-475.

HDSpatialScan documentation built on May 31, 2023, 7:52 p.m.