adj_coexposure: Adjusting for expected changes in co-exposure (TDLMM)

Description

Estimates the marginal effects of an exposure while accounting for expected changes in co-occurring exposures at the same time point. Values of co-occurring exposures are modeled nonlinearly using a spline model with predictions made at the lower an upper values for the exposure of interest.

Usage

adj_coexposure(
  exposure.data,
  object,
  contrast_perc = c(0.25, 0.75),
  contrast_exp = list(),
  conf.level = 0.95,
  keep.mcmc = FALSE,
  verbose = TRUE
)

Value

A list with the following components (or posterior samples if keep.mcmc = TRUE):

Name: vector of exposure names
Time: integer vector of lags
Effect: posterior mean of marginal effects
SE: standard error of the estimate
Lower: lower bound of credible interval of the marginal effect estimate
Upper: upper bound of credible interval of the marginal effect estimate
cEffect: cumulative marginal effects
cLower: lower bound of credible interval of the cumulative marginal effect
cUpper: upper bound of credible interval of the cumulative marginal effect
CW: boolean vector indicating critical window

Arguments

exposure.data: Named list of exposure matrices used as input to TDLMM.
object: Model output for TDLMM from dlmtree() function.
contrast_perc: 2-length vector of percentiles or named list corresponding to lower and upper exposure percentiles of interest. Names must equal list names in 'exposure.data'.
contrast_exp: Named list consisting lower and upper exposure values. This takes precedence over contrast_perc if both inputs are used.
conf.level: Confidence level used for estimating credible intervals. Default is 0.95.
keep.mcmc: If TRUE, return posterior samples.
verbose: TRUE (default) or FALSE: print output

Details

adj_coexposure