Summary for bdlim4
Usage
# S3 method for class 'bdlim4'
summary(object, model = NULL, ...)Arguments
- object
An object of class bdlim4.
- model
Pattern of heterogeneity to be printed. If not specified (default) the best fitting model will be used. Options are "n", "b", "w" and "bw" where b indicates the effect sizes are subgroup specific and w indicates the weight functions are subgroups specific.
- ...
Other arguments
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
#summarize results
summary(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)
#>
#>
#> Model fit statistics:
#> *bw* b w n
#> 0.4996 0.3844 0.1160 0.0000
#>
#>
#> Estimated cumulative effects:
#> group mean median sd q2.5 q97.5 pr_gr0
#> F -0.02574013 -0.0141989 0.06471390 -0.1788092 0.08806467 0.3716
#> M -0.42047665 -0.4227657 0.08540835 -0.5802526 -0.25443798 0.0000
#>
#>
#> Estimated covariate regression coefficients:
#> name mean median sd q2.5
#> interceptF 5.4956901504 5.5131742728 6.225907253 -6.069828635
#> interceptM 5.7511172599 5.7746479312 6.241127427 -5.798546129
#> MomPriorBMI -0.0158699568 -0.0158649194 0.003232411 -0.022228681
#> MomAge 0.0009659302 0.0008932097 0.003032075 -0.004911252
#> raceAsianPI -0.0199434668 -0.0181101248 0.175089697 -0.357798083
#> raceBlack -0.0991464804 -0.0949920461 0.177560226 -0.446804805
#> racewhite -0.0476064347 -0.0494088911 0.168567121 -0.373773868
#> HispanicNonHispanic 0.2547864919 0.2551325803 0.040145538 0.174223669
#> EstMonthConcept2 -0.1506012439 -0.1494170680 0.093143520 -0.331991111
#> EstMonthConcept3 -0.0968118840 -0.0974961153 0.094304974 -0.282924316
#> EstMonthConcept4 -0.1645228802 -0.1647442612 0.094561712 -0.347734644
#> EstMonthConcept5 -0.0959539771 -0.0948136173 0.087269942 -0.271690430
#> EstMonthConcept6 -0.1899461086 -0.1902518805 0.085174830 -0.351707274
#> EstMonthConcept7 -0.0423646523 -0.0406939152 0.087471787 -0.220100411
#> EstMonthConcept8 0.1563733265 0.1570712511 0.094954546 -0.029907539
#> EstMonthConcept9 0.3264577509 0.3269257577 0.092522148 0.139164924
#> EstMonthConcept10 0.4309725852 0.4310896380 0.097967817 0.241758642
#> EstMonthConcept11 0.2319901880 0.2331953825 0.091278767 0.056889957
#> EstMonthConcept12 0.0774482225 0.0771713162 0.084802641 -0.086599251
#> EstYearConcept -0.0020128394 -0.0020212516 0.003094413 -0.007897943
#> q97.5 pr_gr0
#> 17.335923194 0.8060
#> 17.883532336 0.8156
#> -0.009465102 0.0000
#> 0.006961149 0.6304
#> 0.312221265 0.4580
#> 0.235917716 0.2864
#> 0.284769346 0.3840
#> 0.331870326 1.0000
#> 0.026801954 0.0484
#> 0.089959195 0.1504
#> 0.018387487 0.0424
#> 0.069874427 0.1372
#> -0.025910683 0.0136
#> 0.123702602 0.3144
#> 0.338401431 0.9488
#> 0.506162984 1.0000
#> 0.620592672 1.0000
#> 0.406851458 0.9940
#> 0.238193123 0.8144
#> 0.003751797 0.2620
#>
#>
#> BDLIM fit on 1000 observations. Estimated residual standard deviation is 0.542 (0.518,0.567). WAIC is 1635.992.
#>
#> Use `plot(); for the summary.bdlim4 object to view estimated distributed lag functions. The `dlfun' object in the summary object contains estimates of the lag functions.
# obtain estimates of the distributed lag function
# these are note displayed when printed but available for use
sfit_sex <- summary(fit_sex)
head(sfit_sex$dlfun)
#> group time mean median sd q2.5 q97.5
#> 1 F 1 -0.0001673604 2.510728e-06 0.008302003 -0.01908769 0.016732541
#> 2 F 2 -0.0008812720 -1.883290e-04 0.006741782 -0.01691720 0.012252661
#> 3 F 3 -0.0015744514 -4.553809e-04 0.005428840 -0.01496301 0.008148939
#> 4 F 4 -0.0022261663 -1.186064e-03 0.004532524 -0.01366719 0.005134569
#> 5 F 5 -0.0028156845 -1.841573e-03 0.004197893 -0.01328088 0.003487627
#> 6 F 6 -0.0033222738 -2.084135e-03 0.004379035 -0.01381010 0.002896471
#> pr_gr0
#> 1 0.5016
#> 2 0.4712
#> 3 0.4240
#> 4 0.3220
#> 5 0.2540
#> 6 0.2348
# can summarize with a specific model
sfit_hisp_n <- summary(fit_sex, model="n") # no modification
sfit_hisp_b <- summary(fit_sex, model="b") # subgroup-specific effects (beta)
sfit_hisp_w <- summary(fit_sex, model="w") # subgroup-specific weight function
sfit_hisp_bw <- summary(fit_sex, model="bw") # both subgroup-specific
# }