Skip to contents

In admixture such that the probability density function (pdf) follows l = p*f + (1-p)*g, where p is the unknown weight and f is the unknown component distribution: transforms g of the two-component mixture ditribution to a Uniform distribution. Useful to use Patra and Sen estimator for the estimation of the unknown weight p.


knownComp_to_uniform(data, comp.dist, comp.param)



Observations of the sample under study, following an admixture distribution.


A list with two elements corresponding to component distributions (specified with R native names for these distributions) involved in the admixture model. Unknown elements must be specified as 'NULL' objects, e.g. when 'f' is unknown: list(f=NULL, g='norm').


A list with two elements corresponding to the parameters of the component distributions, each element being a list itself. The names used in this list must correspond to the native R argument names for these distributions. Unknown elements must be specified as 'NULL' objects, e.g. if 'f' is unknown: list(f=NULL, g=list(mean=0,sd=1)).


The transformed data, i.e. the transformed mixture distribution where the known component g now follows a Uniform(0,1) distribution.


Xavier Milhaud


## Simulate data:
list.comp <- list(f1 = 'norm', g1 = 'norm')
list.param <- list(f1 = list(mean = 3, sd = 0.5),
                   g1 = list(mean = 0, sd = 1))
sample1 <- rsimmix(n=1500, unknownComp_weight=0.5, comp.dist = list(list.comp$f1,list.comp$g1),
plot_mixt_density(samples = list(sample1[['']]), support = 'continuous')

## Transform the known component into a Uniform(0,1) distribution:
list.comp <- list(f1 = NULL, g1 = 'norm')
list.param <- list(f1 = NULL, g1 = list(mean = 0, sd = 1))
transformed_data <- knownComp_to_uniform(data = sample1[['']],
                                         comp.dist = list.comp, comp.param = list.param)
plot_mixt_density(samples = list(transformed_data), support = 'continuous')