View source: R/SVC_selection.R
SVC_selection_control | R Documentation |
Function to set up control parameters for
SVC_selection
. The underlying Gaussian Process-based
SVC model is defined in SVC_mle
. SVC_selection
then jointly selects fixed and random effects of the GP-based
SVC model using a penalized maximum likelihood estimation (PMLE).
In this function, one can set the parameters for the PMLE and
its optimization procedures (Dambon et al., 2022).
SVC_selection_control( IC.type = c("BIC", "cAIC_BW", "cAIC_VB"), method = c("grid", "MBO"), r.lambda = c(1e-10, 10), n.lambda = 10L, n.init = 10L, n.iter = 10L, CD.conv = list(N = 20L, delta = 1e-06, logLik = TRUE), hessian = FALSE, adaptive = FALSE, parallel = NULL, optim.args = list() )
IC.type |
( |
method |
( |
r.lambda |
( |
n.lambda |
( |
n.init |
( |
n.iter |
( |
CD.conv |
( |
hessian |
( |
adaptive |
( |
parallel |
( |
optim.args |
( |
A list of control parameters for SVC selection.
Jakob Dambon
Bischl, B., Richter, J., Bossek, J., Horn, D., Thomas, J., Lang, M. (2017). mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions, ArXiv preprint https://arxiv.org/abs/1703.03373
Dambon, J. A., Sigrist, F., Furrer, R. (2022). Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models, International Journal of Geographical Information Science doi: 10.1080/13658816.2022.2097684
# Initializing parameters and switching logLik to FALSE selection_control <- SVC_selection_control( CD.conv = list(N = 20L, delta = 1e-06, logLik = FALSE) ) # or selection_control <- SVC_selection_control() selection_control$CD.conv$logLik <- FALSE
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