TDLMM is a method for estimating a Treed Distributed Lag Mixture Model. It operates by building an ensemble of pairs of regression trees. Each tree in a tree-pair partitions the time span of the exposure data and estimates a piecewise constant distributed lag effect. The two trees are then intersected to create an interaction surface for estimating the interaction between two exposures. Exposures are selected for each tree stochastically and each exposure or interaction has a unique shrinkage variance component. This allows for exposure variable selection in addition to the estimation of the distributed lag mixture model.
Usage
tdlmm(
formula,
data,
exposure.data,
n.trees = 20,
n.burn = 2000,
n.iter = 5000,
n.thin = 5,
family = "gaussian",
binomial.size = 1,
formula.zi = NULL,
keep_XZ = FALSE,
mixture.interactions = "noself",
tree.params = c(0.95, 2),
step.prob = c(0.25, 0.25, 0.25),
mix.prior = 1,
shrinkage = "exposures",
subset = NULL,
verbose = TRUE,
diagnostics = FALSE,
initial.params = NULL,
...
)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
named list containing equally sized numerical matrices of exposure data with same, having same length as data
- n.trees
integer for number of trees in ensemble
- n.burn
integer for length of burn-in
- n.iter
integer for number of iterations to run model after burn-in
- n.thin
integer thinning factor, i.e. keep every tenth iteration
- family
'gaussian' for continuous response, 'logit' for binomial response with logit link, or 'zinb' for zero-inflated negative binomial 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
- keep_XZ
FALSE (default) or TRUE: keep the model scale exposure and covariate data
- mixture.interactions
'noself' (default) which estimates interactions only between two different exposures, 'all' which also allows interactions within the same exposure, or 'none' which eliminates all interactions and estimates only main effects of each exposure
- 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, 2) change, 3) switch exposure, defaults to (0.25, 0.25, 0.25) or equal probability of each step for tree updates
- mix.prior
positive scalar hyperparameter for sparsity of exposures
- shrinkage
character "all" (default), "trees", "exposures", "none", turns on horseshoe-like shrinkage priors for different parts of model
- subset
integer vector to analyze only a subset of data and exposures
- verbose
TRUE (default) or FALSE: print 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
- ...
NA