View source: R/vglmer_regression.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. 2022a. "Fast and Accurate Estimation of Non-Nested Binomial Hierarchical Models Using Variational Inference." Bayesian Analysis. 17(2): 623-650.
Goplerud, Max. 2022b. "Re-Evaluating Machine Learning for MRP Given the Comparable Performance of (Deep) Hierarchical Models." Working Paper.
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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