imputer {DNAmf} | R Documentation |
Imputation step in stochastic EM for the non-nested DNA Model
Description
The function performs the imputation step of the stochastic EM algorithm for the DNA model when the design is not nested.
The function generates pseudo outputs \widetilde{\mathbf{y}}_l
at pseudo inputs \widetilde{\mathcal{X}}_l
.
Usage
imputer(XX, yy, kernel=kernel, t, pred1, fit2)
Arguments
XX |
A list of design sets for all fidelity levels, containing |
yy |
A list of current observed and pseudo-responses, containing |
kernel |
A character specifying the kernel type to be used. Choices are |
t |
A vector of tuning parameters for each fidelity level. |
pred1 |
Predictive results for the lowest fidelity level |
fit2 |
A fitted model object for higher fidelity levels |
Details
For non-nested designs, pseudo-input locations \widetilde{\mathcal{X}}_l
are constructed using the internal makenested
function.
The imputer
function then imputes the corresponding pseudo outputs
\widetilde{\mathbf{y}}_l = f_l(\widetilde{\mathcal{X}}_l)
by drawing samples from the conditional normal distribution,
given fixed parameter estimates and previous-level outputs Y_{-L}^{*(m-1)}
,
at the m
-th iteration of the EM algorithm.
For further details, see Heo, Boutelet, and Sung (2025+, <arXiv:2506.08328>).
Value
An updated yy
list containing:
-
y_star
: An updated pseudo-complete outputs\mathbf{y}^*_l
. -
y_list
: An original outputs\mathbf{y}_l
. -
y_tilde
: A newly imputed pseudo outputs\widetilde{\mathbf{y}}_l
.