admix (version 2.3.4)

IBM_tabul_stochasticInteg: Simulated distribution of the contrast using IBM

Description

Tabulate the distribution related to the inner convergence part of the contrast, by simulating trajectories of Gaussian processes and applying some transformations. Useful to perform the test hypothesis, by retrieving the (1-alpha)-quantile of interest. See 'Details' below and the cited paper therein for further information.

Usage

IBM_tabul_stochasticInteg(
  samples,
  admixMod,
  min_size = NULL,
  n.varCovMat = 80,
  n_sim_tab = 100,
  parallel = FALSE,
  n_cpu = 2
)

Value

A list with four elements, containing: 1) random draws of the contrast as defined in the reference given here; 2) estimated unknown component weights for the two admixture models; 3) the value of the the empirical contrast; 4) support that was used to evaluate the variance-covariance matrix of the empirical processes.

Arguments

samples

A list of the two samples under study.

admixMod

A list of two objects of class 'admix_model', with information about distributions and parameters.

min_size

(optional, NULL by default) In the k-sample case, useful to provide the minimal size among all samples (needed to take into account the correction factor for variance-covariance assessment). Otherwise, useless.

n.varCovMat

(default to 80) Number of time points at which the Gaussian processes are simulated.

n_sim_tab

(default to 100) Number of simulated Gaussian processes when tabulating the inner convergence distribution in the 'icv' testing method using the IBM estimation approach.

parallel

(default to FALSE) Boolean to indicate whether parallel computations are performed (speed-up the tabulation).

n_cpu

(default to 2) Number of cores used when paralleling computations.

Author

Xavier Milhaud

References

MilhaudPommeretSalhiVandekerkhove2024aadmix

Examples

# \donttest{
## Simulate mixture data:
mixt1 <- twoComp_mixt(n = 1200, weight = 0.4,
                      comp.dist = list("norm", "norm"),
                      comp.param = list(list("mean" = -2, "sd" = 0.5),
                                        list("mean" = 0, "sd" = 1)))
mixt2 <- twoComp_mixt(n = 1000, weight = 0.7,
                      comp.dist = list("norm", "norm"),
                      comp.param = list(list("mean" = -2, "sd" = 0.5),
                                        list("mean" = 1, "sd" = 1)))
data1 <- getmixtData(mixt1)
data2 <- getmixtData(mixt2)
## Define the admixture models:
admixMod1 <- admix_model(knownComp_dist = mixt1$comp.dist[[2]],
                         knownComp_param = mixt1$comp.param[[2]])
admixMod2 <- admix_model(knownComp_dist = mixt2$comp.dist[[2]],
                         knownComp_param = mixt2$comp.param[[2]])
IBM_tabul_stochasticInteg(samples = list(data1, data2), admixMod = list(admixMod1, admixMod2),
                          min_size=NULL, n.varCovMat=20, n_sim_tab=2, parallel=FALSE, n_cpu=2)
# }