Kernel estimation of the density in a two-components mixture model where one component are a standard Gaussian density. Here we suppose that the density to estimate lives in \(R^+\).
FastKerFdr_unsigned(
X,
p0 = NULL,
plotting = FALSE,
NbKnot = 1e+05,
tol = 1e-05,
max_iter = 10000
)A list with the following elements:
p0 | vector of the estimated proportions of \(H_0\) hypotheses for each of p-value serie. |
tau | the vector of \(H_1\) posteriors. |
f1 | a numeric vector, each coordinate \(i\)
corresponding to the evaluation of the \(H_1\) density at point \(x_i\),
where \(x_i\) is the \(i\)th item in X. |
F1 | a numeric vector, each coordinate \(i\)
corresponding to the evaluation of the \(H_1\) cdf at point \(x_i\),
where \(x_i\) is the \(i\)th item in X. |
a vector of probit-transformed p-values (corresponding to a p-value serie)
a priori proportion of \(H_0\) hypotheses
boolean, should some diagnostic graphs be plotted. (Default is FALSE.)
The (maximum) number of knot for the kde procedure. (Default is 1e5.)
a tolerance value for convergence. (Default is 1e-5.)
the maximum number of iterations allowed for the algorithm to converge or complete its process.(Default is 1e4.)