Create clusters on the unknown components related to the K populations following admixture models. Based on the K-sample test using Inversion - Best Matching (IBM) approach, see 'Details' below for further information.
Usage
admix_cluster(
samples,
admixMod,
conf_level = 0.95,
tune_penalty = TRUE,
tabul_dist = NULL,
echo = TRUE,
...
)Arguments
- samples
A list of the K (K>1) samples to be studied, all following admixture distributions.
- admixMod
A list of objects of class admix_model, containing useful information about distributions and parameters.
- conf_level
(default to 0.95) The confidence level of the k-sample tests used in the clustering procedure.
- tune_penalty
(default to TRUE) A boolean that allows to choose between a classical penalty term or an optimized penalty (embedding some tuning parameters, automatically optimized). Optimized penalty is particularly useful for low/mid-sized samples, or unbalanced sample sizes to detect alternatives to the null hypothesis (H0). It is recommended to use it.
- tabul_dist
(default to NULL) Only useful for comparisons of detected clusters at different confidence levels. A list of the tabulated distributions of the stochastic integral used in the k-sample test, each element for each cluster previously detected.
- echo
(default to TRUE) Display the remaining computation time.
- ...
Optional arguments to IBM_k_samples_test; namely 'n_sim_tab', 'parallel' and 'n_cpu'. These are crucial to speed-up the building of clusters.
Value
An object of class admix_cluster, containing 12 attributes: 1) the number of samples under study; 2) the sizes of samples; 3) the information about mixture components in each sample (distributions and parameters); 4) the number of detected clusters; 5) the list of p-values for each k-sample test at the origin of detected clusters; 6) the cluster affiliation for each sample; 7) the confidence level of statistical tests; 8) which samples in which cluster; 9) the size of clusters; 10) the estimated weights of the unknown component distributions inside each cluster (remind that estimated weights are consistent only if unknown components are tested to be identical, which is the case inside clusters); 11) the matrix of pairwise discrepancies across all samples; 12) the list of tabulated distributions used for statistical tests involved in building the clusters.
References
Milhaud X, Pommeret D, Salhi Y, Vandekerkhove P (2024). “Contamination-source based K-sample clustering.” Journal of Machine Learning Research, 25(287), 1--32. https://jmlr.org/papers/v25/23-0914.html.
Author
Xavier Milhaud xavier.milhaud.research@gmail.com
Examples
# \donttest{
## Simulate mixture data:
mixt1 <- twoComp_mixt(n = 2600, weight = 0.8,
comp.dist = list("gamma", "exp"),
comp.param = list(list("shape" = 16, "scale" = 1/4),
list("rate" = 1/3.5)))
mixt2 <- twoComp_mixt(n = 3000, weight = 0.7,
comp.dist = list("gamma", "exp"),
comp.param = list(list("shape" = 14, "scale" = 1/2),
list("rate" = 1/5)))
mixt3 <- twoComp_mixt(n = 3500, weight = 0.6,
comp.dist = list("gamma", "gamma"),
comp.param = list(list("shape" = 16, "scale" = 1/4),
list("shape" = 12, "scale" = 1/2)))
mixt4 <- twoComp_mixt(n = 4800, weight = 0.5,
comp.dist = list("gamma", "exp"),
comp.param = list(list("shape" = 14, "scale" = 1/2),
list("rate" = 1/7)))
data1 <- getmixtData(mixt1)
data2 <- getmixtData(mixt2)
data3 <- getmixtData(mixt3)
data4 <- getmixtData(mixt4)
## 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]])
admixMod3 <- admix_model(knownComp_dist = mixt3$comp.dist[[2]],
knownComp_param = mixt3$comp.param[[2]])
admixMod4 <- admix_model(knownComp_dist = mixt4$comp.dist[[2]],
knownComp_param = mixt4$comp.param[[2]])
## Clustering procedure:
admix_cluster(samples = list(data1, data2, data3, data4),
admixMod = list(admixMod1, admixMod2, admixMod3, admixMod4),
conf_level = 0.95, tune_penalty = TRUE, n_sim_tab = 30)
#>
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#> Call:
#> admix_cluster(samples = list(data1, data2, data3, data4), admixMod = list(admixMod1,
#> admixMod2, admixMod3, admixMod4), conf_level = 0.95, tune_penalty = TRUE,
#> n_sim_tab = 30)
#>
#> Number of detected clusters across the samples provided: 2.
#>
#> List of samples involved in each built cluster (in numeric format, i.e. inside c()) :
#> - Cluster #1: vector of populations c(2, 4)
#> - Cluster #2: vector of populations c(1, 3)
# }