View source: R/control_functions.R
vglmer_control | R Documentation |
This function controls various estimation options for vglmer
.
vglmer_control(
iterations = 1000,
prior_variance = "hw",
factorization_method = c("strong", "partial", "weak"),
parameter_expansion = "translation",
do_SQUAREM = TRUE,
tolerance_elbo = 1e-08,
tolerance_parameters = 1e-05,
force_whole = TRUE,
print_prog = NULL,
do_timing = FALSE,
verbose_time = FALSE,
return_data = FALSE,
linpred_method = "joint",
vi_r_method = "VEM",
verify_columns = FALSE,
debug_param = FALSE,
debug_ELBO = FALSE,
debug_px = FALSE,
quiet = TRUE,
quiet_rho = TRUE,
px_method = "dynamic",
px_numerical_it = 10,
hw_inner = 10,
init = "EM_FE"
)
iterations |
Default of 1000; this sets the maximum number of iterations used in estimation. |
prior_variance |
Prior distribution on the random effect variance
Estimation may fail if an improper prior ( |
factorization_method |
Factorization assumption for the variational
approximation. Default of |
parameter_expansion |
Default of |
do_SQUAREM |
Default ( |
tolerance_elbo |
Default ( |
tolerance_parameters |
Default ( |
force_whole |
Default ( |
print_prog |
Default ( |
do_timing |
Default ( |
verbose_time |
Default ( |
return_data |
Default ( |
linpred_method |
Default ( |
vi_r_method |
Default ( |
verify_columns |
Default ( |
debug_param |
Default ( |
debug_ELBO |
Default ( |
debug_px |
Default ( |
quiet |
Default ( |
quiet_rho |
Default ( |
px_method |
When code |
px_numerical_it |
Default of 10; if L-BFGS_B is needed for a parameter expansion, this sets the number of steps used. |
hw_inner |
If |
init |
Default ( |
This function returns a named list with class vglmer_control
.
It is passed to vglmer
in the argument control
. This argument
only accepts objects created using vglmer_control
.
Goplerud, Max. 2022. "Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference." Bayesian Analysis. 17(2): 623-650.
Goplerud, Max. 2024. "Re-Evaluating Machine Learning for MRP Given the Comparable Performance of (Deep) Hierarchical Models." American Political Science Review. 118(1): 529-536.
Huang, Alan, and Matthew P. Wand. 2013. "Simple Marginally Noninformative Prior Distributions for Covariance Matrices." Bayesian Analysis. 8(2):439-452.
Varadhan, Ravi, and Christophe Roland. 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of any EM Algorithm." Scandinavian Journal of Statistics. 35(2): 335-353.
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