This vignette demonstrates the implementation of heterogeneous treed distributed lag mixture model (HDLMM).
Load data
Simulated data is available on GitHub. It can be loaded with the following code.
sbd_dlmtree <- get_sbd_dlmtree()Data preparation
# Response and covariates
sbd_cov <- sbd_dlmtree %>%
select(bwgaz, ChildSex, MomAge, GestAge, MomPriorBMI, Race,
Hispanic, MomEdu, SmkAny, Marital, Income,
EstDateConcept, EstMonthConcept, EstYearConcept)
# Exposure data
sbd_exp <- list(PM25 = sbd_dlmtree %>% select(starts_with("pm25_")),
TEMP = sbd_dlmtree %>% select(starts_with("temp_")),
SO2 = sbd_dlmtree %>% select(starts_with("so2_")),
CO = sbd_dlmtree %>% select(starts_with("co_")),
NO2 = sbd_dlmtree %>% select(starts_with("no2_")))
sbd_exp <- sbd_exp %>% lapply(as.matrix)Fitting the model
# Gaussian
hdlmm.fit <- dlmtree(formula = bwgaz ~ ChildSex + MomAge + MomPriorBMI +
Race + Hispanic + SmkAny + EstMonthConcept,
data = sbd_cov,
exposure.data = sbd_exp,
family = "gaussian",
dlm.type = "linear", mixture = TRUE, het = TRUE,
hdlm.modifiers = c("ChildSex", "MomAge", "MomPriorBMI", "SmkAny"),
hdlm.modifier.splits = 15,
n.burn = 2500, n.iter = 10000, n.thin = 5)#> Preparing data...
#>
#> Running HDLMM:
#> Burn-in % complete
#> [0--------25--------50--------75--------100]
#> ''''''''''''''''''''''''''''''''''''''''''
#> MCMC iterations (est time: 12 minutes)
#> [0--------25--------50--------75--------100]
#> ''''''''''''''''''''''''''''''''''''''''''
#> Compiling results...Model fit summary
hdlmm.sum <- summary(hdlmm.fit)
hdlmm.sum#> ---
#> HDLMM summary
#>
#> Model run info:
#> - bwgaz ~ ChildSex + MomAge + MomPriorBMI + Race + Hispanic + SmkAny + EstMonthConcept
#> - family: gaussian
#> - 20 trees
#> - 2500 burn-in iterations
#> - 10000 post-burn iterations
#> - 5 thinning factor
#> - 5 exposures measured at 37 time points
#> - 10 two-way interactions (no-self interactions)
#> - 0.5 modifier sparsity prior
#> - 1 exposure sparsity prior
#> - 0.95 confidence level
#>
#> Fixed effects:
#> Mean Lower Upper
#> *(Intercept) 1.469 0.843 2.205
#> ChildSexM -0.525 -1.930 0.427
#> MomAge 0.000 -0.003 0.003
#> *MomPriorBMI -0.021 -0.023 -0.018
#> RaceAsianPI 0.030 -0.098 0.148
#> RaceBlack 0.042 -0.088 0.174
#> Racewhite 0.021 -0.101 0.143
#> *HispanicNonHispanic 0.253 0.230 0.275
#> *SmkAnyY -0.396 -0.441 -0.349
#> EstMonthConcept2 0.107 -0.041 0.187
#> EstMonthConcept3 0.186 -0.105 0.308
#> EstMonthConcept4 0.267 -0.112 0.430
#> EstMonthConcept5 0.362 -0.011 0.543
#> *EstMonthConcept6 0.343 0.023 0.536
#> *EstMonthConcept7 0.372 0.149 0.553
#> *EstMonthConcept8 0.348 0.195 0.517
#> *EstMonthConcept9 0.419 0.271 0.559
#> *EstMonthConcept10 0.300 0.175 0.416
#> *EstMonthConcept11 0.199 0.094 0.301
#> EstMonthConcept12 0.037 -0.034 0.116
#> ---
#> * = CI does not contain zero
#>
#> Modifiers:
#> PIP
#> ChildSex 1.000
#> MomAge 0.954
#> MomPriorBMI 0.992
#> SmkAny 0.283
#> ---
#> PIP = Posterior inclusion probability
#>
#> residual standard errors: 0.007
#> ---
#> To obtain exposure effect estimates, use the 'shiny(fit)' function.