lfactor: Define a Common Latent Factor for the 'dynamite' Model.

In santikka/dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Define a Common Latent Factor for the dynamite Model.

Description

This function can be used as part of a dynamiteformula() to define a common latent factor component. The latent factor is modeled as a spline similarly as a time-varying intercept, but instead of having equal effect on each group, there is an additional loading variable for each group so that in the linear predictor we have a term \lambda_i \psi_t for each group i.

Usage

lfactor(
  responses = NULL,
  nonzero_lambda = TRUE,
  correlated = TRUE,
  noncentered_psi = FALSE,
  flip_sign = TRUE
)

Arguments

responses	[character()] Names of the responses that the factor should affect. Default is all responses defined with obs except categorical responses, which do not (yet) support the factor component.
nonzero_lambda	[logical()] If TRUE (the default), assumes that the mean of factor loadings is nonzero or not. Should be a logical vector matching the length of responses or a single logical value in case responses is NULL. See details.
correlated	[logical()] If TRUE (the default), the latent factors are assumed to be correlated between channels.
noncentered_psi	[logical(1)] If TRUE, uses a noncentered parametrization for spline coefficients of all the factors. The number of knots is based splines() call. Default is FALSE.
flip_sign	[logical(1)] If TRUE (default), try to avoid multimodality due to sign-switching by defining the sign of \lambda and \psi based on the mean of \omega_1,\ldots, \omega_D coefficients. This only affects channels with nonzero_lambda = FALSE. If the true mean of \omegas is close to zero, this might not help, in which case it is better to set flip_sign = FALSE and post-process the samples in other ways (or use only one chain and/or suitable initial values). This argument is common to all factors.

Value

An object of class latent_factor.

See Also

Model formula construction dynamite(), dynamiteformula(), lags(), random_spec(), splines()

Examples

data.table::setDTthreads(1) # For CRAN
# three channel model with common factor affecting for responses x and y
obs(y ~ 1, family = "gaussian") +
  obs(x ~ 1, family = "poisson") +
  obs(z ~ 1, family = "gaussian") +
  lfactor(
    responses = c("y", "x"), nonzero_lambda = c(TRUE, FALSE),
    correlated = TRUE, noncentered_psi = FALSE
  )

santikka/dynamite documentation built on April 17, 2025, 11:47 a.m.