Computes the cut-off values for the identification of the outliers based on the squared ICS distances. It uses simulations under a multivariate standard normal model for a specific data setup and scatters combination.
dist_simu_test(
object,
S1 = NULL,
S2 = NULL,
S1_args = list(),
S2_args = list(),
index,
m = 10000,
level = 0.025,
n_cores = NULL,
iseed = NULL,
pkg = "ICSOutlier",
q_type = 7,
...
)A vector with the values of the (1-level)th quantile.
object of class "ICS" where both S1 and S2 are specified as functions.
The sample size and the dimension of interest are also obtained from the object.
The invariant coordinate are required to be centered.
an object of class "ICS_scatter" or a function that
contains the location vector and scatter matrix as location and scatter components.
an object of class "ICS_scatter" or a function that
contains the location vector and scatter matrix as location and scatter components.
a list containing additional arguments for S1.
a list containing additional arguments for S2.
integer vector specifying which components are used to compute the ics_distances().
number of simulations. Note that extreme quantiles are of interest and hence m should be large.
the (1-level(s))th quantile(s) used to choose the cut-off value(s). Usually just one number between 0 and 1. However a vector is also possible.
number of cores to be used. If NULL or 1, no parallel computing is used. Otherwise makeCluster with type = "PSOCK" is used.
If parallel computation is used the seed passed on to clusterSetRNGStream. Default is NULL which means no fixed seed is used.
When using parallel computing, a character vector listing all the packages which need to be loaded on the different cores via require. Must be at least "ICSOutlier" and must contain the packages needed to compute the scatter matrices.
specifies the quantile algorithm used in quantile.
further arguments passed on to the function quantile.
Aurore Archimbaud and Klaus Nordhausen
The function extracts basically the dimension of the data from the "ICS" object and simulates m times, from a multivariate standard normal distribution, the squared ICS distances with the components specified in index. The resulting value is then the mean of the m correponding quantiles of these distances at level 1-level.
Note that depending on the data size and scatters used this can take a while and so it is more efficient to parallelize computations.
Note that the function is seldomly called directly by the user but internally by ICS_outlier().
Archimbaud, A., Nordhausen, K. and Ruiz-Gazen, A. (2018), ICS for multivariate outlier detection with application to quality control. Computational Statistics & Data Analysis, 128:184-199. ISSN 0167-9473. tools:::Rd_expr_doi("10.1016/j.csda.2018.06.011").
ICS(), ics_distances()