Function reference • dlmtree

Model fitting

dlmtree(): Fit tree structured distributed lag models

Model summary

Obtain results from the model fit

combine.models(): Combines information from DLMs of single exposure

combine.models.tdlmm(): Combines information from DLMs of mixture exposures.

summary(<hdlm>): Creates a summary object of class 'hdlm'

summary(<hdlmm>): Creates a summary object of class 'hdlmm'

summary(<monotone>): Creates a summary object of class 'monotone'

summary(<tdlm>): Creates a summary object of class 'tdlm'

summary(<tdlmm>): Creates a summary object of class 'tdlmm'

summary(<tdlnm>): Creates a summary object of class 'tdlnm'

estDLM(): Calculates subgroup-specific lag effects for heterogeneous models

ppRange(): Makes a 'pretty' output of a group of numbers

predict(<hdlm>): Calculates predicted response for HDLM

predict(<hdlmm>): Calculates predicted response for HDLMM

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

print(<hdlm>): Print a hdlm Object

print(<hdlmm>): Print a hdlmm Object

print(<monotone>): Print a monotone Object

print(<summary.hdlm>): Prints an overview with summary of model class 'hdlm'

print(<summary.hdlmm>): Prints an overview with summary of model class 'hdlmm'

print(<summary.monotone>): Prints an overview with summary of model class 'monotone'

print(<summary.tdlm>): Prints an overview with summary of model class 'tdlm'

print(<summary.tdlmm>): Prints an overview with summary of model class 'tdlmm'

print(<summary.tdlnm>): Prints an overview with summary of model class 'tdlnm'

print(<tdlm>): Print a tdlm Object

print(<tdlmm>): Print a tdlmm Object

print(<tdlnm>): Print a tdlnm Object

Visualization

Visualize distributed lag functions

plot(<summary.monotone>): Returns variety of plots for model summary of class 'monotone'

plot(<summary.tdlm>): Plots a distributed lag function for model summary of 'tdlm'

plot(<summary.tdlmm>): Plots DLMMs for model summary of class 'tdlmm'

plot(<summary.tdlnm>): Returns variety of plots for model summary of class 'tdlnm'

HDLM & HDLMM

Useful functions for HDLM & HDLMM

pip(): Calculates posterior inclusion probabilities (PIPs) for modifiers in HDLM & HDLMM

shiny(): shiny

shiny(<hdlm>): Executes a 'shiny' app for HDLM.

shiny(<hdlmm>): Executes a 'shiny' app for HDLMM.

splitpoints(): Determines split points for continuous modifiers

Simulation

Simulate datasets

sim.hdlmm(): Creates simulated data for HDLM & HDLMM

sim.tdlmm(): Creates simulated data for TDLM & TDLMM

sim.tdlnm(): Creates simulated data for TDLNM

Data

Built-in datasets

coExp: Randomly sampled exposure from Colorado counties

exposureCov: Exposure covariance structure

pm25Exposures: PM2.5 Exposure data

zinbCo: Time-series exposure data for ZINB simulated data

get_sbd_dlmtree(): Download simulated data for dlmtree articles

Cpp source code & Misc.

cppIntersection(): fast set intersection tool assumes sorted vectors A and B

dlmEst(): Calculates the distributed lag effect with DLM matrix for linear models.

dlmtreeGPFixedGaussian(): dlmtree model with fixed Gaussian process approach

dlmtreeGPGaussian(): dlmtree model with Gaussian process approach

dlmtreeHDLMGaussian(): dlmtree model with shared HDLM approach

dlmtreeHDLMMGaussian(): dlmtree model with HDLMM approach

dlmtreeTDLMFixedGaussian(): dlmtree model with fixed Gaussian approach

dlmtreeTDLMNestedGaussian(): dlmtree model with nested Gaussian approach

dlmtreeTDLM_cpp(): dlmtree model with nested HDLM approach

dlnmEst(): Calculates the distributed lag effect with DLM matrix for non-linear models.

dlnmPLEst(): Calculates the distributed lag effect with DLM matrix for non-linear models.

drawTree(): Draws a new tree structure

mixEst(): Calculates the lagged interaction effects with MIX matrix for linear models.

monotdlnm_Cpp(): dlmtree model with monotone tdlnm approach

ppRange(): Makes a 'pretty' output of a group of numbers

rcpp_pgdraw(): Multiple draw polya gamma latent variable for var c[i] with size b[i]

rtmvnorm(): Truncated multivariate normal sampler, mean mu, cov sigma, truncated (0, Inf)

ruleIdx(): Calculates the weights for each modifier rule

scaleModelMatrix(): Centers and scales a matrix

splitPIP(): Calculates the posterior inclusion probability (PIP).

tdlmm(): Treed Distributed Lag Mixture Models (Deprecated)

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

tdlmm_Cpp(): dlmtree model with tdlmm approach

tdlnm_Cpp(): dlmtree model with tdlnm approach

zeroToInfNormCDF(): Integrates (0,inf) over multivariate normal