estim_PS {admix} | R Documentation |
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
)
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. |
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.
Author(s)
Xavier Milhaud xavier.milhaud.research@gmail.com
References
Patra RK, Sen B (2016). “Estimation of a two-component mixture model with applications to multiple testing.” Journal of the Royal Statistical Society Series B, 78(4), 869-893.
See Also
print.estim_PS()
for printing a short version of the results from this estimation method,
and summary.estim_PS()
for more comprehensive results.
Examples
## Not run:
## 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')
## End(Not run)