Create a sparse Functional Data sample for a Gaussian Process
Functional data sample of size n, sparsely sampled from a Gaussian process
MakeSparseGP( n, rdist = runif, sparsity = 2:9, muFun = function(x) rep(0, length(x)), K = 2, lambda = rep(1, K), sigma = 0, basisType = "cos", CovFun = NULL )
n |
number of samples to generate. |
rdist |
a sampler for generating the random design time points within [0, 1]. |
sparsity |
A vector of integers. The number of observation per sample is chosen to be one of the elements in sparsity with equal chance. |
muFun |
a function that takes a vector input and output a vector of the corresponding mean (default: zero function). |
K |
scalar specifying the number of basis to be used (default: 2). |
lambda |
vector of size K specifying the variance of each components (default: rep(1,K)). |
sigma |
The standard deviation of the Gaussian noise added to each observation points. |
basisType |
string specifying the basis type used; possible options are: 'sin', 'cos' and 'fourier' (default: 'cos') (See code of 'CreateBasis' for implementation details.) |
CovFun |
an alternative specification of the covariance structure. |
TODO
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