Plot for Summary of BDLIM (summary.bdlim1 version)
Source:R/plot.summary.bdlim4.R
plot.summary.bdlim1.RdPlot for Summary of BDLIM (summary.bdlim1 version)
Usage
# S3 method for class 'summary.bdlim1'
plot(x, ...)Examples
# \donttest{
# run BDLIM with modification by ChildSex
fit_sex <- bdlim4(
y = sbd_bdlim$bwgaz,
exposure = sbd_bdlim[,paste0("pm25_",1:37)],
covars = sbd_bdlim[,c("MomPriorBMI","MomAge","race","Hispanic",
"EstMonthConcept","EstYearConcept")],
group = as.factor(sbd_bdlim$ChildSex),
df = 5,
nits = 5000,
parallel = FALSE
)
#> fitting bw
#> Start MCMC for bdlim1 with w_free=TRUE and b_free=TRUE.
#>
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#> End MCMC for bdlim1 with w_free=TRUE and b_free=TRUE.
#> fitting b
#> Start MCMC for bdlim1 with w_free=FALSE and b_free=TRUE.
#>
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#> End MCMC for bdlim1 with w_free=FALSE and b_free=TRUE.
#> fitting w
#> Start MCMC for bdlim1 with w_free=TRUE and b_free=FALSE.
#>
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#> End MCMC for bdlim1 with w_free=TRUE and b_free=FALSE.
#> fitting n
#> Start MCMC for bdlim1 with w_free=FALSE and b_free=FALSE.
#>
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#> End MCMC for bdlim1 with w_free=FALSE and b_free=FALSE.
#> postprocessing
#> postprocessing
# show model comparison results
fit_sex
#>
#> Call:
#> bdlim4(y = sbd_bdlim$bwgaz, exposure = sbd_bdlim[, paste0("pm25_",
#> 1:37)], covars = sbd_bdlim[, c("MomPriorBMI", "MomAge", "race",
#> "Hispanic", "EstMonthConcept", "EstYearConcept")], group = as.factor(sbd_bdlim$ChildSex),
#> df = 5, nits = 5000, parallel = FALSE)
#>
#> Modification pattern probabilities (higher is better fit):
#> *bw* b w n
#> 0.5496 0.3132 0.1368 0.0004
#>
#> Modification pattern WAIC (lower is better fit):
#> *bw* b w n
#> 1 1634.104 1636.739 1642.893 1657.911
#>
#> Note: Model probabilities tend to favor more complex models compared to WAIC. It is recomended to use model probabilities for selecting the modification pattern and WAIC to compare degrees of freedom for the weight function.
#summarize results
sfit_sex <- summary(fit_sex)
# graph the estimated distributed lag functions for each group
plot(sfit_sex)
# can save plot as an object and modify with ggplot2
library(ggplot2)
plt <- plot(sfit_sex)
plt + ggtitle("My plot with BDLIM") +
ylab("Estimated expected difference in\nBWGAZ per 1 ug/m3 increase in exposure")
# the summary file has the data to make this plot
head(sfit_sex$dlfun)
#> group time mean median sd q2.5 q97.5
#> 1 F 1 -0.001152533 -0.0004830367 0.007858031 -0.01867634 0.014510026
#> 2 F 2 -0.001692679 -0.0006739279 0.006458771 -0.01681525 0.010329910
#> 3 F 3 -0.002214812 -0.0010282846 0.005297166 -0.01529206 0.006942385
#> 4 F 4 -0.002700922 -0.0015770190 0.004513791 -0.01371344 0.004513781
#> 5 F 5 -0.003132997 -0.0021112600 0.004211428 -0.01315121 0.002855874
#> 6 F 6 -0.003493024 -0.0025204499 0.004336811 -0.01343929 0.002435076
#> pr_gr0
#> 1 0.4436
#> 2 0.4156
#> 3 0.3460
#> 4 0.2840
#> 5 0.2448
#> 6 0.2148
# }