gaussian_PoFD: Gaussian Partially Observed Functional Data

In aefdz/depthPoFDA: Partially Observed Integrated Functional Depth

Description

Generates samples of partially observed gaussian functions following different censoring regimes.

Usage

gaussian_PoFD(n, p, type, observability, ninterval)

Arguments

n	total number of functional observations
p	total number of points observed for each function
type	type of partially observed data. Options are "sparse", "interval" and "common". See Elías et al (2020).
observability	mean observed proportion of the domain where each function is observed.
ninterval	if type = "interval", n_interval is an integer with the number of observed intervals 1, 2, 3... Large values of this parameter requires a large parameter p to guarantee the observability level.

Value

a list containing two elements 1) a functional sample and 2) the same sample of functions but partially observed following one of the schemes descrived in the argument type.

References

Elías, Antonio, Jiménez, Raúl, Paganoni, Anna M. and Sangalli, Laura M. (2020). Integrated Depths for Partially ObservedFunctional Data.

Examples

gaussian_pofd <- gaussian_PoFD(n=100, p=200, type="sparse", observability=0.5)

aefdz/depthPoFDA documentation built on Jan. 8, 2021, 10:16 p.m.