dlmtree: Fit tree structured distributed lag models

In dlmtree: Bayesian Treed Distributed Lag Models

Fit tree structured distributed lag models

Description

The 'dlmtree' function accommodates various response variable types, including continuous, binary, and zero-inflated count values. The function is designed to handle both single exposure and exposure mixtures. For a single exposure, users are offered options to model non-linear effects (tdlnm), linear effects (tdlm), or heterogeneous subgroup/individualized effects (hdlm). In the case of exposure mixtures, the function supports lagged interactions (tdlmm), and heterogeneous subgroup/individualized effects (hdlmm) allowing for a comprehensive exploration of mixture exposure heterogeneity. Additionally, users can fine-tune parameters to impose effect shrinkage and perform exposure selection, enhancing the adaptability and precision of the modeling process. For more detailed documentation, visit: dlmtree website.

Usage

dlmtree(
  formula,
  data,
  exposure.data,
  dlm.type = "linear",
  family = "gaussian",
  mixture = FALSE,
  het = FALSE,
  control.mcmc = list(),
  control.hyper = list(),
  control.family = list(),
  control.tdlnm = list(),
  control.het = list(),
  control.mix = list(),
  control.monotone = list(),
  control.diagnose = list()
)

Arguments

formula	object of class formula, a symbolic description of the fixed effect model to be fitted, e.g. y ~ a + b.
data	data frame containing variables used in the formula.
exposure.data	numerical matrix of exposure data with same length as data, for a mixture setting (tdlmm, hdlmm): named list containing equally sized numerical matrices of exposure data having same length as data.
dlm.type	dlm model specification: "linear" (default), "nonlinear", "monotone".
family	'gaussian' for continuous response, 'logit' for binomial, 'zinb' for zero-inflated negative binomial.
mixture	flag for mixture, set to TRUE for tdlmm and hdlmm (default: FALSE).
het	flag for heterogeneity, set to TRUE for hdlm and hdlmm (default: FALSE).
control.mcmc	list of MCMC control parameters. This is passed to dlmtree.control.mcmc.
control.hyper	list of hyperparameter control parameters. This is passed to dlmtree.control.hyper
control.family	list of family control parameters. This is passed to dlmtree.control.family
control.tdlnm	list of TDLNM control parameters. This is passed to dlmtree.control.tdlnm
control.het	list of control parameters for heterogeneous models. This is passed to dlmtree.control.het
control.mix	list of mixture control parameters. This is passed to dlmtree.control.mix
control.monotone	list of control parameters for monotone model. This is passed to dlmtree.control.monotone
control.diagnose	list of control parameters for diagnostics. This is passed to dlmtree.control.diagnose

Details

dlmtree

Model is recommended to be run for at minimum 5000 burn-in iterations followed by 15000 sampling iterations with a thinning factor of 5. Convergence can be checked by re-running the model and validating consistency of results. Examples are provided below for the syntax for running different types of models. For more examples, visit: dlmtree website.

Value

object of one of the classes: tdlm, tdlmm, tdlnm, hdlm, hdlmm, monotone

Examples

  # The first three examples are for one lagged exposure


  # treed distributed lag model (TDLM)
  # binary outcome with logit link

  D <- sim.tdlmm(sim = "A", mean.p = 0.5, n = 1000)
  tdlm.fit <- dlmtree(y ~ .,
                      data = D$dat,
                      exposure.data = D$exposures[[1]],
                      dlm.type = "linear",
                      family = "logit",
                      control.family = list(binomial.size = 1))

  # summarize results
  tdlm.sum <- summary(tdlm.fit)
  tdlm.sum

  # plot results
  plot(tdlm.sum)



  # Treed distributed lag nonlinear model (TDLNM)
  # Gaussian regression model
  D <- sim.tdlnm(sim = "A", error.to.signal = 1)
  tdlnm.fit <- dlmtree(formula = y ~ .,
                       data = D$dat,
                       exposure.data = D$exposures,
                       dlm.type = "nonlinear",
                       family = "gaussian")

  # summarize results
  tdlnm.sum <- summary(tdlnm.fit)
  tdlnm.sum

  # plot results
  plot(tdlnm.sum)



  # Heterogeneous TDLM (HDLM), similar to first example but with heterogeneous exposure response
  D <- sim.hdlmm(sim = "B", n = 1000)
  hdlm.fit <- dlmtree(y ~ .,
                      data = D$dat,
                      exposure.data = D$exposures,
                      dlm.type = "linear",
                      family = "gaussian",
                      het = TRUE)

  # summarize results
  hdlm.sum <- summary(hdlm.fit)
  hdlm.sum

  # shiny app for HDLM
  if (interactive()) {
    shiny(hdlm.fit)
  }



  # The next two examples are for a mixture (or multivariate) exposure


  # Treed distributed lag mixture model (TDLMM)
  # Model for mixutre (or multivariate) lagged exposures
  # with a homogenious exposure-time-response function
  D <- sim.tdlmm(sim = "B", error = 25, n = 1000)
  tdlmm.fit <- dlmtree(y ~ .,
                       data = D$dat, exposure.data = D$exposures,
                       control.mix = list(interactions = "noself"),
                       dlm.type = "linear", family = "gaussian",
                       mixture = TRUE)

  # summarize results
  tdlmm.sum <- summary(tdlmm.fit)

  # plot the marginal exposure-response for one exposure
  plot(tdlmm.sum, exposure1 = "e1")

  # plot exposure-response surface
  plot(tdlmm.sum, exposure1 = "e1", exposure2 = "e2")



  # heterogeneous version of TDLMM
  D <- sim.hdlmm(sim = "D", n = 1000)
  hdlmm.fit <- dlmtree(y ~ .,
                       data = D$dat,
                       exposure.data = D$exposures,
                       dlm.type = "linear",
                       family = "gaussian",
                       mixture = TRUE,
                       het = TRUE)

  # summarize results
  hdlmm.sum <- summary(hdlmm.fit)
  hdlmm.sum

  # summarize results
  if (interactive()) {
    shiny(hdlmm.fit)
  }

dlmtree documentation built on June 8, 2025, 12:39 p.m.