RoMFDI {funcharts} | R Documentation |
Robust Multivariate Functional Data Imputation (RoMFDI)
Description
It performs Robust Multivariate Functional Data Imputation (RoMFDI) as in Capezza et al. (2024).
Usage
RoMFDI(
mfdobj,
method_pca = "ROBPCA",
fev = 0.999,
n_dataset = 3,
update = TRUE,
niter_update = 10,
alpha = 0.8
)
Arguments
mfdobj |
A multivariate functional data object of class mfd. |
method_pca |
The method used in |
fev |
Number between 0 and 1 denoting the proportion of variability that must be explained by the principal components to be selected for dimension reduction after applying RoMFPCA on the observed components to impute the missing ones. Default is 0.999. |
n_dataset |
To take into account the increased noise due to single imputation, the proposed RoMFDI allows multiple imputation. Due to the presence of the stochastic component in the imputation, it is worth explicitly noting that the imputed data set is not deterministically assigned. Therefore, by performing several times the RoMFDI in the imputation step of the RoMFCC implementation, the corresponding multiple estimated RoMFPCA models could be combined by averaging the robustly estimated covariance functions, thus performing a multiple imputation strategy as suggested by Van Ginkel et al. (2007). Default is 3. |
update |
The RoMFDI performs sequential imputation of missing functional
components.
If TRUE, Robust Multivariate Functional
Principal Component Analysis (RoMFPCA) |
niter_update |
The number of times the RoMFPCA is updated during the algorithm. It applies only if update is TRUE. Default value is 10. |
alpha |
This parameter measures the fraction of outliers the
RoMFPCA algorithm should resist and is used only
if |
Value
A list with n_dataset
elements.
Each element is an mfd
object containing mfdobj
with
stochastic imputation of the missing components.
References
Capezza, C., Centofanti, F., Lepore, A., Palumbo, B. (2024) Robust Multivariate Functional Control Charts. Technometrics, doi:10.1080/00401706.2024.2327346.
Van Ginkel, J. R., Van der Ark, L. A., Sijtsma, K., and Vermunt, J. K. (2007). Two-way imputation: a bayesian method for estimating missing scores in tests and questionnaires, and an accurate approximation. Computational Statistics & Data Analysis, 51(8):4013–-4027.
Examples
## Not run:
library(funcharts)
mfdobj <- get_mfd_list(air, grid = 1:24, n_basis = 13, lambda = 1e-2)
out <- functional_filter(mfdobj)
mfdobj_imp <- RoMFDI(out$mfdobj)
## End(Not run)