tdlnm function

Treed Distributed Lag Non-Linear Models (Deprecated)

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'.

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

Returns

object of class 'tdlnm' or 'tdlm'

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.