FactorHet_control {FactorHet} | R Documentation |
Control for FactorHet estimation
Description
Provides a set of control arguments to FactorHet
. Arguments
around the initialization of the model (important when K > 1
) can be
set via FactorHet_init
and arguments for the model-based
optimization tuning of regularization strength \lambda
can be found in
FactorHet_mbo_control
. The parameters can be divided into ones
governing the model priors, model estimation, and miscellaneous settings. All
arguments have default values.
Usage
FactorHet_control(
iterations = 1000,
maxit_pi = NULL,
optim_phi_controls = list(method = "lib_lbfgs"),
prior_var_phi = 4,
prior_var_beta = Inf,
gamma = 1,
repeat_beta = 1,
adaptive_weight = "B&R",
init_method = "short_EM",
return_data = FALSE,
log_method = "log_ginv",
tolerance.parameters = 1e-05,
tolerance.logposterior = 1e-05,
rare_threshold = 5,
rare_verbose = 1,
beta_method = "cpp",
beta_cg_it = 25,
lambda_scale = "N",
weight_dlist = FALSE,
do_SQUAREM = TRUE,
step_SQUAREM = NULL,
backtrack_SQUAREM = 10,
df_method = "EM",
forced_randomize = FALSE,
single_intercept = NULL,
tau_method = "nullspace",
tau_stabilization = 5,
tau_truncate = 1e+06,
debug = FALSE,
force_reset = FALSE,
calc_df = TRUE,
calc_se = TRUE,
quiet_tictoc = TRUE,
override_BR = FALSE
)
Arguments
iterations |
A numerical value setting the maximum number of iterations used in the algorithm. The default is 1000. |
maxit_pi |
An argument setting the maximum number of iterations used in
each M-Step that updates the moderators. The default is |
optim_phi_controls |
A list of options for optimizer used in updating
the moderator
parameters. A method must be provided at minimum, e.g., |
prior_var_phi |
A numerical value that encodes the variance of multivariate normal prior on moderator coefficients. Note: The moderators are not standardized internally and thus should be on broadly comparable scales to avoid differential amounts of regularization on different moderators. The default value is 4. |
prior_var_beta |
A numerical value of normal prior on each treatment
effect coefficient. The default is |
gamma |
A non-negative numerical value that determines whether sparsity-inducing prior be "spread" across groups in proportion to the average prior probability of membership. Default of 1; see Städler et al. (2010) and Goplerud et al. (2025) for more discussion. |
repeat_beta |
An integer setting the number of times to repeat the E-M
cycle for updating |
adaptive_weight |
An argument that determines the weights given to
different terms in the penalty function. The default ( |
init_method |
An argument for initializing the algorithm. One set of
options are different character values: |
return_data |
A logical value for whether the formatted data should be
returned. The default is |
log_method |
An argument for specifying whether latent overlapping
groups should be used when interactions are included. The default is
|
tolerance.parameters |
A numerical value setting the one convergence criterion: When no parameter changes by more than this amount, terminate the algorithm. Default is 1e-5. |
tolerance.logposterior |
A numerical value setting the one convergence criterion: When the log-posterior changes by less than this amount, terminate the algorithm. Default is 1e-5. |
rare_threshold |
A numerical value setting the threshold for which
interactions should be excluded. If an interaction of two factors has fewer
than |
rare_verbose |
A logical value as to whether to print information about
the rare interactions. The default is |
beta_method |
A character value for the method by which |
beta_cg_it |
A numerical value of the number of conjugate gradient steps
to use if |
lambda_scale |
A function for internally rescaling lambda to be a
function of |
weight_dlist |
A logical value for whether to weight additional
penalties following Hastie and Lim (2015). The default is |
do_SQUAREM |
A logical value for whether to perform SQUAREM to
accelerate convergence. The default is |
step_SQUAREM |
An argument specifying the step size to use for SQUAREM.
The default is |
backtrack_SQUAREM |
An integer that sets the number of backtracking steps to perform for SQUAREM. The default is 10. |
df_method |
A character value specifying the method calculating degrees
of freedom. Default of |
forced_randomize |
A logical value that indicates, in the forced-choice
setting, whether the "left" and "right" profiles should be randomized for
each task. The default is |
single_intercept |
A logical value or |
tau_method |
A character value indicating the method for dealing with
binding restrictions, i.e. numerically infinite |
tau_stabilization |
An integer value of the number of steps to perform
with |
tau_truncate |
A numerical value to either truncate |
debug |
A logical value for whether the algorithm should be debugged.
The default is |
force_reset |
A logical argument about how the nullspace is computed. If
|
calc_df |
A logical value for whether to calculate degrees of freedom of
final model. The default is |
calc_se |
A logical value for whether standard errors of final model.
The default is |
quiet_tictoc |
A logical value for whether to not print
information about the timing of the model. The default is |
override_BR |
A logical value for whether to ignore Bondell and Reich
style-weights. The default is |
Value
FactorHet_control
returns a named list containing the elements
listed in "Arguments".
References
Bondell, Howard D., and Brian J. Reich. 2009. "Simultaneous Factor Selection and Collapsing Levels in ANOVA." Biometrics 65(1): 169-177.
Goplerud, Max, Kosuke Imai, and Nicole E. Pashley. 2025. "Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis." arxiv preprint: https://arxiv.org/abs/2201.01357
Post, Justin B., and Howard D. Bondell. 2013. "Factor Selection and Structural Identification in the Interaction ANOVA Model." Biometrics 69(1):70-79.
Lim, Michael, and Trevor Hastie. 2015. "Learning Interactions via Hierarchical Group-Lasso Regularization." Journal of Computational and Graphical Statistics 24(3):627-654.
Städler, Nicolas, Peter Bühlmann, and Sara Van De Geer. 2010. "l1-penalization for Mixture Regression Models." Test 19(2):209-256.
Yan, Xiaohan and Jacob Bien. 2017. "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations." Statistical Science 32(4):531–560.
Examples
str(FactorHet_control())