R: Define a Common Latent Factor for the Dynamite Model.

lfactor {dynamite}

R Documentation

Define a Common Latent Factor for the Dynamite Model.

Description

This function can be used as part of 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 `\omega`s 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.

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
  )