## Print the results of statistical test for equality of unknown component distributions in admixture models

### Description

Print the decision (as well as other useful information) of the statistical test with null hypothesis corresponding to the equality of unknown component distributions in admixture models. More precisely, given two (or more) admixture models with cumulative distribution functions (CDF) L1 and L2, where Li = pi*Fi + (1-pi)*Gi i=1,2 and Gi are the known CDFs, the function performs the test: H0: F1 = F2 versus H1: F1 != F2.

### Usage

## S3 method for class 'admix_test'
print(x, ...)


### Arguments

 x An object of class 'admix_test' (see ?admix_test). ... further arguments passed to or from other methods.

### Author(s)

Xavier Milhaud xavier.milhaud.research@gmail.com

### Examples

##### On a simulated example, with 1 sample (gaussianity test):
list.comp <- list(f1 = "norm", g1 = "norm")
list.param <- list(f1 = list(mean = 0, sd = 1), g1 = list(mean = 2, sd = 0.7))
## Simulate data:
sim1 <- rsimmix(n = 300, unknownComp_weight = 0.85, comp.dist = list(list.comp$f1,list.comp$g1),
comp.param = list(list.param$f1, list.param$g1))\$mixt.data
## Perform the test hypothesis:
list.comp <- list(f1 = NULL, g1 = "norm")
list.param <- list(f1 = NULL, g1 = list(mean = 2, sd = 0.7))
gaussTest <- admix_test(samples = list(sim1), sym.f = TRUE, test.method = 'Poly', sim_U = NULL,
n_sim_tab = 50, comp.dist = list.comp, comp.param = list.param,
support = "Real", conf.level = 0.95, parallel = FALSE, n_cpu = 2)
print(gaussTest)