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