tdlnm: Treed Distributed Lag Non-Linear Models (Deprecated)
In dlmtree: Bayesian Treed Distributed Lag Models

tdlnm

R Documentation

Treed Distributed Lag Non-Linear Models (Deprecated)

Description

TDLNM is a method for estimating Distributed Lag Linear and Non-Linear Models (DLMs/DLNMs). It operates by building an ensemble of regression trees, which each partition the exposure-time- response surface and make estimates at each sector. Trees from the ensemble each contribute a partial estimate of the exposure-time surface, while controlling for a model given by 'formula'.

Usage

tdlnm(
  formula,
  data,
  exposure.data,
  exposure.splits = 20,
  exposure.se = sd(exposure.data)/2,
  n.trees = 20,
  n.burn = 1000,
  n.iter = 2000,
  n.thin = 5,
  family = "gaussian",
  binomial.size = 1,
  formula.zi = NULL,
  tree.params = c(0.95, 2),
  step.prob = c(0.25, 0.25),
  monotone = FALSE,
  monotone.gamma0 = rep(0, ncol(exposure.data)),
  monotone.sigma = diag(ncol(exposure.data)) * 1.502^2,
  monotone.tree.time.params = c(0.95, 2),
  monotone.tree.exp.params = c(0.95, 2),
  monotone.time.kappa = NULL,
  shrinkage = ifelse(monotone, FALSE, TRUE),
  subset = NULL,
  lowmem = FALSE,
  verbose = TRUE,
  diagnostics = FALSE,
  initial.params = NULL,
  debug = FALSE,
  ...
)

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 complete exposure data with same length as data
`exposure.splits`	scalar indicating the number of splits (divided evenly across quantiles of the exposure data) or list with two components: 'type' = 'values' or 'quantiles', and 'split.vals' = a numerical vector indicating the corresponding exposure values or quantiles for splits. Setting exposure.splits equal to 0 will change the model to a distributed lag model, which assumes a linear effect of exposure.
`exposure.se`	numerical matrix of exposure standard errors with same size as exposure.data or a scalar smoothing factor representing a uniform smoothing factor applied to each exposure measurement, defaults to sd(exposure.data)/2
`n.trees`	integer for number of trees in ensemble, default = 20
`n.burn`	integer for length of burn-in, >=2000 recommended
`n.iter`	integer for number of iterations to run model after burn-in >=5000 recommended
`n.thin`	integer thinning factor, i.e. keep every fifth iteration
`family`	'gaussian' for continuous response, or 'logit' for binomial response with logit link
`binomial.size`	integer type scalar (if all equal, default = 1) or vector defining binomial size for 'logit' family
`formula.zi`	object of class formula, a symbolic description of the ZI model to be fitted, e.g. y ~ a + b. This only applies to ZINB where covariates for ZI model is different from NB model. This is same as the main formula by default
`tree.params`	numerical vector of alpha and beta hyperparameters controlling tree depth (see Bayesian CART, 1998), default: alpha = 0.95, beta = 2
`step.prob`	numerical vector for probability of 1) grow/prune, and 2) change, defaults to (0.25, 0.25) or equal probability of each step for tree updates
`monotone`	FALSE (default) or TRUE: estimate monotone effects
`monotone.gamma0`	———UPDATE———
`monotone.sigma`	———UPDATE———
`monotone.tree.time.params`	———UPDATE———
`monotone.tree.exp.params`	———UPDATE———
`monotone.time.kappa`	———UPDATE———
`shrinkage`	int, 1 (default) turn on tree-specific shrinkage priors, 0 turn off
`subset`	integer vector to analyze only a subset of data and exposures
`lowmem`	FALSE (default) or TRUE: turn on memory saver for DLNM, slower computation time
`verbose`	TRUE (default) or FALSE: print progress bar output
`diagnostics`	TRUE or FALSE (default) keep model diagnostic such as terminal nodes, acceptance details, etc.
`initial.params`	initial parameters for fixed effects model, FALSE = none (default), "glm" = generate using GLM, or user defined, length must equal number of parameters in fixed effects model
`debug`	if TRUE, outputs debugging messages
`...`	NA

Details

tdlnm

Model is recommended to be run for at minimum 5000 burn-in iterations followed by 15000 sampling iterations with a thinning factor of 10. Convergence can be checked by re-running the model and validating consistency of results.