admix (version 2.3.4)

estim_PS: Estimates in an admixture using Patra and Sen approach

Description

Estimation of both the weight and the distribution of the unknown component in an admixture model, by Patra and Sen approach. Remind that the admixture probability density function (pdf) l is given by l = p*f + (1-p)*g, where g is the known component of the two-component mixture, p is the unknown proportion of the unknown component distribution f. More information in 'Details' below concerning the estimation method.

Usage

estim_PS(
  samples,
  admixMod,
  method = c("fixed", "lwr.bnd", "cv"),
  c.n = 0.1 * log(log(length(samples))),
  folds = 10,
  reps = 1,
  cn.s = NULL,
  cn.length = 100,
  gridsize = 1200
)

Value

An object of class estim_PS, containing 10 attributes: 1) the number of samples studied (1 in this case); 2) the sample size; 3) the information about component distributions of the admixture model; 4) the estimation method 5patra and Sen here); 5) the estimated mixing weight (estimate of the unknown component proportion); 6) the estimated decontaminated CDF; 7) an object of the class 'dist.fun' (that gives the distance); 8) the tuning parameter 'c.n'; 9) the lower bound of the estimated mixing proportion (if such an option has been chosen); 10) the number of observations.

Arguments

samples

Sample to be studied.

admixMod

An object of class admix_model, containing information about the known component distribution and its parameter(s).

method

One of 'lwr.bnd', fixed' or 'cv': depending on whether compute some lower bound of the mixing proportion, the estimate based on the value of 'c.n' or use cross-validation for choosing 'c.n' (tuning parameter).

c.n

(default to NULL) A positive number for the penalization, see reference below. If NULL, equals to 0.1*log(log(n)).

folds

(optional, default to 10) Number of folds used for cross-validation.

reps

(optional, default to 1) Number of replications for cross-validation.

cn.s

(optional) A sequence of 'c.n' to be used for cross-validation (vector of values). Default is equally spaced grid of 100 values between .001 x log(log(n)) and 0.2 x log(log(n)).

cn.length

(optional, default to 100) Number of equally spaced tuning parameter (between .001 x log(log(n)) and 0.2 x log(log(n))). Values to search from.

gridsize

(default to 600) Number of equally spaced points (between 0 and 1) to evaluate the distance function. Larger values are more computationally intensive but also lead to more accurate estimates.

Author

Xavier Milhaud

References

PatraSen2016admix

Examples

## Simulate mixture data:
mixt1 <- twoComp_mixt(n = 800, weight = 0.33,
                      comp.dist = list("gamma", "exp"),
                      comp.param = list(list("shape" = 2, "scale" = 0.5),
                                        list("rate" = 0.25)))
data1 <- getmixtData(mixt1)
## Define the admixture model:
admixMod1 <- admix_model(knownComp_dist = mixt1$comp.dist[[2]],
                         knownComp_param = mixt1$comp.param[[2]])
## Estimation step:
estim_PS(samples = data1, admixMod = admixMod1, method = 'fixed')