Fit the BDLIM model with 1 pattern of modification
Usage
bdlim1(
y,
exposure,
covars,
group,
id = NULL,
w_free,
b_free,
df,
nits,
nburn = round(nits/2),
nthin = 1,
progress = TRUE
)Arguments
- y
A vector of outcomes
- exposure
A matrix of exposures with one row for each individual
- covars
A matrix or data.frame of covariates This should not include the grouping factor (see group below). This may include factor variables.
- group
A vector of group memberships. This should be a factor variable.
- id
An optional vector of individual IDs if there are repeated measures or other groupings that a random intercept should be included for. This must be a factor variable.
- w_free
Logical indicating if the weight functions are shared by all groups (FALSE) or group-specific (TRUE).
- b_free
Logical indicating if the effect sizes are shared by all groups (FALSE) or group-specific (TRUE).
- df
Degrees of freedom for the weight functions
- nits
Number of MCMC iterations.
- nburn
Number of MCMC iterations to be discarded as burn in. The default is half if the MCMC iterations. This is only used for WAIC in this function but is passed to summary and plot functions and used there.
- nthin
Thinning factors for the MCMC. This is only used for WAIC in this function but is passed to summary and plot functions and used there.
- progress
Logical indicating if a progress bar should be shown during MCMC iterations. Default is TRUE.
Examples
# \donttest{
# run BDLIM with modification by ChildSex
fit_sex <- bdlim1(
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),
w_free = TRUE,
b_free = TRUE,
df = 5,
nits = 5000
)
#> 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.
# show model fit results
fit_sex
#>
#> Call:
#> bdlim1(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),
#> w_free = TRUE, b_free = TRUE, df = 5, nits = 5000)
#>
#> Modification pattern WAIC (lower is better fit):
#> [1] 1635.946
#summarize results
sfit_sex <- summary(fit_sex)
sfit_sex
#> $WAIC
#> [1] 1635.946
#>
#> $call
#> bdlim1(y = sbd_bdlim$bwgaz, exposure = sbd_bdlim[, paste0("pm25_",
#> 1:37)], covars = sbd_bdlim[, c("MomPriorBMI", "MomAge", "race",
#> "Hispanic", "EstMonthConcept", "EstYearConcept")], group = as.factor(sbd_bdlim$ChildSex),
#> w_free = TRUE, b_free = TRUE, df = 5, nits = 5000)
#>
#> $cumulative
#> group mean median sd q2.5 q97.5 pr_gr0
#> 1 F -0.01659887 -0.008101259 0.06340898 -0.1663946 0.1041476 0.4092
#> 2 M -0.44644829 -0.446032044 0.08075686 -0.6050052 -0.2913208 0.0000
#>
#> $dlfun
#> group time mean median sd q2.5
#> 1 F 1 -1.052026e-03 -4.672335e-04 0.006945532 -0.017169762
#> 2 F 2 -1.454176e-03 -6.125987e-04 0.005813573 -0.015260430
#> 3 F 3 -1.841534e-03 -9.002886e-04 0.004902968 -0.013839039
#> 4 F 4 -2.199304e-03 -1.175154e-03 0.004316360 -0.013067588
#> 5 F 5 -2.512695e-03 -1.383335e-03 0.004105080 -0.012766886
#> 6 F 6 -2.766912e-03 -1.588444e-03 0.004203872 -0.013078916
#> 7 F 7 -2.947162e-03 -1.745993e-03 0.004457625 -0.013889241
#> 8 F 8 -3.038652e-03 -1.822151e-03 0.004708793 -0.014623508
#> 9 F 9 -3.026589e-03 -1.803530e-03 0.004840934 -0.015026672
#> 10 F 10 -2.896179e-03 -1.730318e-03 0.004780001 -0.014660044
#> 11 F 11 -2.639023e-03 -1.542873e-03 0.004506884 -0.013697115
#> 12 F 12 -2.272300e-03 -1.290740e-03 0.004102922 -0.012663446
#> 13 F 13 -1.819584e-03 -9.671438e-04 0.003698716 -0.011351115
#> 14 F 14 -1.304448e-03 -5.672994e-04 0.003443547 -0.009846487
#> 15 F 15 -7.504654e-04 -2.861791e-04 0.003446271 -0.008869294
#> 16 F 16 -1.812090e-04 -5.349073e-05 0.003694214 -0.008223040
#> 17 F 17 3.797475e-04 1.595203e-04 0.004063119 -0.007937347
#> 18 F 18 9.088308e-04 3.532864e-04 0.004403702 -0.007533810
#> 19 F 19 1.382467e-03 5.752706e-04 0.004595043 -0.007007080
#> 20 F 20 1.780775e-03 8.300829e-04 0.004579472 -0.006272247
#> 21 F 21 2.098635e-03 9.709865e-04 0.004421444 -0.005248766
#> 22 F 22 2.334618e-03 1.078554e-03 0.004230698 -0.004174902
#> 23 F 23 2.487297e-03 1.318076e-03 0.004117177 -0.003456489
#> 24 F 24 2.555243e-03 1.380393e-03 0.004151695 -0.003523977
#> 25 F 25 2.537028e-03 1.267247e-03 0.004329434 -0.003895168
#> 26 F 26 2.431223e-03 1.140399e-03 0.004573604 -0.004513954
#> 27 F 27 2.236402e-03 9.202318e-04 0.004774230 -0.005229660
#> 28 F 28 1.951135e-03 8.261624e-04 0.004823840 -0.006181906
#> 29 F 29 1.575933e-03 6.798815e-04 0.004655686 -0.006711857
#> 30 F 30 1.119066e-03 4.732509e-04 0.004315352 -0.007033764
#> 31 F 31 5.907412e-04 2.316557e-04 0.003933101 -0.007279238
#> 32 F 32 1.167391e-06 2.867246e-05 0.003727496 -0.007988556
#> 33 F 33 -6.394474e-04 -2.775857e-04 0.003955534 -0.009828875
#> 34 F 34 -1.320895e-03 -6.966621e-04 0.004726194 -0.012603572
#> 35 F 35 -2.032967e-03 -1.035356e-03 0.005938780 -0.016888351
#> 36 F 36 -2.765456e-03 -1.324488e-03 0.007436675 -0.020033202
#> 37 F 37 -3.508153e-03 -1.572714e-03 0.009097614 -0.025224609
#> 38 M 1 3.506959e-02 3.560911e-02 0.013324360 0.009217883
#> 39 M 2 2.717817e-02 2.733657e-02 0.010495968 0.006779778
#> 40 M 3 1.939675e-02 1.912672e-02 0.007972324 0.004840850
#> 41 M 4 1.183535e-02 1.138034e-02 0.006046064 0.001524898
#> 42 M 5 4.603994e-03 4.210704e-03 0.005123678 -0.005431811
#> 43 M 6 -2.187322e-03 -1.908000e-03 0.005324930 -0.013618226
#> 44 M 7 -8.428582e-03 -7.565676e-03 0.006137670 -0.022218039
#> 45 M 8 -1.400978e-02 -1.318510e-02 0.006981694 -0.029373396
#> 46 M 9 -1.882089e-02 -1.813338e-02 0.007525166 -0.035094940
#> 47 M 10 -2.275192e-02 -2.222624e-02 0.007602830 -0.038617203
#> 48 M 11 -2.572599e-02 -2.535874e-02 0.007189740 -0.040788602
#> 49 M 12 -2.779880e-02 -2.747103e-02 0.006497414 -0.041377465
#> 50 M 13 -2.905920e-02 -2.880552e-02 0.005867241 -0.041450571
#> 51 M 14 -2.959603e-02 -2.947422e-02 0.005682817 -0.040769317
#> 52 M 15 -2.949813e-02 -2.979270e-02 0.006136777 -0.040879838
#> 53 M 16 -2.885436e-02 -2.944013e-02 0.007052133 -0.041622736
#> 54 M 17 -2.775354e-02 -2.845132e-02 0.008075537 -0.041868700
#> 55 M 18 -2.628453e-02 -2.701614e-02 0.008888492 -0.041588746
#> 56 M 19 -2.453617e-02 -2.517865e-02 0.009246788 -0.040293012
#> 57 M 20 -2.259436e-02 -2.322268e-02 0.009015663 -0.038086168
#> 58 M 21 -2.053330e-02 -2.110579e-02 0.008301524 -0.034846434
#> 59 M 22 -1.842421e-02 -1.905748e-02 0.007310001 -0.031546994
#> 60 M 23 -1.633834e-02 -1.686861e-02 0.006304190 -0.027756668
#> 61 M 24 -1.434693e-02 -1.454389e-02 0.005596304 -0.025221878
#> 62 M 25 -1.252123e-02 -1.231002e-02 0.005431259 -0.024262258
#> 63 M 26 -1.093248e-02 -1.035102e-02 0.005769083 -0.024087817
#> 64 M 27 -9.651916e-03 -9.049393e-03 0.006300247 -0.023882881
#> 65 M 28 -8.750789e-03 -8.185475e-03 0.006684534 -0.023315989
#> 66 M 29 -8.280119e-03 -8.006024e-03 0.006712183 -0.022686263
#> 67 M 30 -8.210051e-03 -8.206980e-03 0.006438616 -0.021465104
#> 68 M 31 -8.490510e-03 -8.534591e-03 0.006091246 -0.021099879
#> 69 M 32 -9.071419e-03 -8.852221e-03 0.006036809 -0.021604698
#> 70 M 33 -9.902705e-03 -9.030429e-03 0.006659338 -0.023946566
#> 71 M 34 -1.093429e-02 -1.007640e-02 0.008088621 -0.027121946
#> 72 M 35 -1.211610e-02 -1.183281e-02 0.010172056 -0.031680510
#> 73 M 36 -1.339807e-02 -1.385342e-02 0.012689195 -0.036930754
#> 74 M 37 -1.473010e-02 -1.568874e-02 0.015460495 -0.042440062
#> q97.5 pr_gr0
#> 1 0.0137785504 0.4364
#> 2 0.0101205782 0.4084
#> 3 0.0072223875 0.3588
#> 4 0.0050116059 0.3096
#> 5 0.0036258752 0.2776
#> 6 0.0031067792 0.2688
#> 7 0.0033034563 0.2664
#> 8 0.0038073690 0.2744
#> 9 0.0043542129 0.2792
#> 10 0.0044433254 0.2844
#> 11 0.0041831262 0.2876
#> 12 0.0040850829 0.2940
#> 13 0.0042573542 0.3088
#> 14 0.0050073216 0.3664
#> 15 0.0059725688 0.4232
#> 16 0.0080253732 0.4812
#> 17 0.0099516297 0.5348
#> 18 0.0113248346 0.5768
#> 19 0.0124323586 0.6016
#> 20 0.0127734131 0.6356
#> 21 0.0129269494 0.6536
#> 22 0.0127829496 0.6844
#> 23 0.0129092047 0.7096
#> 24 0.0131704977 0.7208
#> 25 0.0134958172 0.7092
#> 26 0.0138258029 0.6832
#> 27 0.0136778158 0.6588
#> 28 0.0132848907 0.6352
#> 29 0.0119253626 0.6116
#> 30 0.0110156481 0.5920
#> 31 0.0095034296 0.5576
#> 32 0.0081442204 0.5084
#> 33 0.0077241049 0.4376
#> 34 0.0086166855 0.3924
#> 35 0.0098762832 0.3740
#> 36 0.0111676775 0.3656
#> 37 0.0125458330 0.3700
#> 38 0.0601457084 0.9920
#> 39 0.0471733050 0.9904
#> 40 0.0349468758 0.9904
#> 41 0.0238347597 0.9832
#> 42 0.0141971351 0.8096
#> 43 0.0071740392 0.3836
#> 44 0.0019438032 0.0692
#> 45 -0.0022758734 0.0048
#> 46 -0.0061032724 0.0000
#> 47 -0.0098043092 0.0000
#> 48 -0.0132117393 0.0000
#> 49 -0.0164250674 0.0000
#> 50 -0.0183861066 0.0000
#> 51 -0.0187208207 0.0000
#> 52 -0.0168256976 0.0000
#> 53 -0.0127856937 0.0000
#> 54 -0.0085203342 0.0000
#> 55 -0.0044702949 0.0008
#> 56 -0.0013698963 0.0184
#> 57 0.0007628790 0.0296
#> 58 0.0010106265 0.0324
#> 59 0.0004667659 0.0284
#> 60 -0.0010938698 0.0100
#> 61 -0.0027300865 0.0032
#> 62 -0.0026180007 0.0068
#> 63 -0.0006818064 0.0204
#> 64 0.0010821260 0.0444
#> 65 0.0035812152 0.0636
#> 66 0.0044943942 0.0776
#> 67 0.0034272243 0.0828
#> 68 0.0029008501 0.0788
#> 69 0.0020197370 0.0760
#> 70 0.0019637762 0.0660
#> 71 0.0025333586 0.0812
#> 72 0.0049628548 0.1328
#> 73 0.0098369756 0.1596
#> 74 0.0154072335 0.1940
#>
#> $regcoef
#> name mean median sd q2.5
#> 1 interceptF 5.6583604865 5.587622037 6.324598745 -6.899013907
#> 2 interceptM 6.1139550664 6.113260967 6.328272199 -6.343814379
#> 3 MomPriorBMI -0.0160120545 -0.016071038 0.003164924 -0.022310237
#> 4 MomAge 0.0009648609 0.001019695 0.003001433 -0.005132967
#> 5 raceAsianPI -0.0193478000 -0.022201013 0.176731906 -0.370170412
#> 6 raceBlack -0.1009289662 -0.106402046 0.182161721 -0.461531498
#> 7 racewhite -0.0492555570 -0.052519489 0.169597474 -0.383898780
#> 8 HispanicNonHispanic 0.2561880862 0.256460145 0.040328323 0.177703903
#> 9 EstMonthConcept2 -0.1456010127 -0.145619513 0.094047066 -0.334729657
#> 10 EstMonthConcept3 -0.1018310104 -0.100479865 0.097587152 -0.293708791
#> 11 EstMonthConcept4 -0.1777050692 -0.177051413 0.093697149 -0.360857515
#> 12 EstMonthConcept5 -0.0997951260 -0.099628993 0.087543279 -0.271519613
#> 13 EstMonthConcept6 -0.1854240922 -0.184277602 0.083848860 -0.353776238
#> 14 EstMonthConcept7 -0.0449401834 -0.044617866 0.085212533 -0.207681656
#> 15 EstMonthConcept8 0.1527704643 0.155778943 0.096301796 -0.045010557
#> 16 EstMonthConcept9 0.3135320710 0.311492996 0.092667590 0.124903697
#> 17 EstMonthConcept10 0.4140296008 0.416152617 0.092321730 0.230822948
#> 18 EstMonthConcept11 0.2225742542 0.223369810 0.086595098 0.048908501
#> 19 EstMonthConcept12 0.0766681663 0.078415748 0.084882644 -0.091700980
#> 20 EstYearConcept -0.0021152456 -0.002081239 0.003139907 -0.008022602
#> 21 EF -0.0071990641 -0.007927092 0.030968421 -0.066053192
#> 22 EM -0.1253561744 -0.125066491 0.020340973 -0.165376227
#> q97.5 pr_gr0
#> 1 17.510728855 0.8172
#> 2 18.136105017 0.8348
#> 3 -0.009919573 0.0000
#> 4 0.006838140 0.6360
#> 5 0.336220510 0.4588
#> 6 0.263360542 0.2780
#> 7 0.290153392 0.3756
#> 8 0.335402888 1.0000
#> 9 0.039281343 0.0616
#> 10 0.084253984 0.1540
#> 11 0.008351850 0.0300
#> 12 0.078444107 0.1276
#> 13 -0.024539907 0.0148
#> 14 0.119829195 0.3036
#> 15 0.331562633 0.9372
#> 16 0.490990030 0.9996
#> 17 0.589078559 1.0000
#> 18 0.388623796 0.9932
#> 19 0.241981946 0.8176
#> 20 0.004053558 0.2536
#> 21 0.053590813 0.4092
#> 22 -0.086271056 0.0000
#>
#> $sigma
#> name mean median sd q2.5 q97.5
#> 1 sigma 0.5417691 0.5412167 0.5412167 0.5193125 0.5680018
#>
#> $names_groups
#> [1] "F" "M"
#>
#> $n
#> [1] 1000
#>
#> $family
#> [1] "gaussian"
#>
#> attr(,"class")
#> [1] "summary.bdlim1"
# graph the estimated distributed lag functions for each group
plot(sfit_sex)
# run BDLIM with no modification
# here a single group is put in for group
# the group must be a factor
# w_free and b_free must be FALSE because modification is not allowed with only one group
fit_onegroup <- bdlim1(
y = sbd_bdlim$bwgaz,
exposure = sbd_bdlim[,paste0("pm25_",1:37)],
covars = sbd_bdlim[,c("MomPriorBMI","MomAge","race","Hispanic",
"EstMonthConcept","EstYearConcept")],
group = as.factor(rep("A",nrow(sbd_bdlim))),
w_free = FALSE,
b_free = FALSE,
df = 5,
nits = 5000
)
#> 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.
# show model fit results
fit_onegroup
#>
#> Call:
#> bdlim1(y = sbd_bdlim$bwgaz, exposure = sbd_bdlim[, paste0("pm25_",
#> 1:37)], covars = sbd_bdlim[, c("MomPriorBMI", "MomAge", "race",
#> "Hispanic", "EstMonthConcept", "EstYearConcept")], group = as.factor(rep("A",
#> nrow(sbd_bdlim))), w_free = FALSE, b_free = FALSE, df = 5,
#> nits = 5000)
#>
#> Modification pattern WAIC (lower is better fit):
#> [1] 3107.178
#summarize results
sfit_onegroup <- summary(fit_onegroup)
sfit_onegroup
#> $WAIC
#> [1] 3107.178
#>
#> $call
#> bdlim1(y = sbd_bdlim$bwgaz, exposure = sbd_bdlim[, paste0("pm25_",
#> 1:37)], covars = sbd_bdlim[, c("MomPriorBMI", "MomAge", "race",
#> "Hispanic", "EstMonthConcept", "EstYearConcept")], group = as.factor(rep("A",
#> nrow(sbd_bdlim))), w_free = FALSE, b_free = FALSE, df = 5,
#> nits = 5000)
#>
#> $cumulative
#> group mean median sd q2.5 q97.5 pr_gr0
#> 1 all -0.3184172 -0.2999182 0.1211001 -0.5883231 -0.1218997 4e-04
#>
#> $dlfun
#> group time mean median sd q2.5 q97.5
#> 1 all 1 0.0500382595 4.881206e-02 0.022207052 0.011613206 0.096126618
#> 2 all 2 0.0346912312 3.335700e-02 0.017517656 0.004557178 0.071554993
#> 3 all 3 0.0197123518 1.832476e-02 0.013514048 -0.004200306 0.048599097
#> 4 all 4 0.0054697700 3.963891e-03 0.010796710 -0.013658232 0.027673962
#> 5 all 5 -0.0076683653 -8.319528e-03 0.009989203 -0.025869513 0.009564599
#> 6 all 6 -0.0193339054 -1.918118e-02 0.010922399 -0.040530305 0.002047322
#> 7 all 7 -0.0291587014 -2.883579e-02 0.012625649 -0.053700418 -0.002592253
#> 8 all 8 -0.0367746045 -3.684963e-02 0.014261932 -0.064688187 -0.005970188
#> 9 all 9 -0.0418134658 -4.213683e-02 0.015352280 -0.071410512 -0.008329336
#> 10 all 10 -0.0439071366 -4.426036e-02 0.015629878 -0.073227275 -0.008452523
#> 11 all 11 -0.0428591841 -4.324180e-02 0.015011729 -0.070958326 -0.007930858
#> 12 all 12 -0.0391600399 -3.958648e-02 0.013742986 -0.064834161 -0.006933076
#> 13 all 13 -0.0334718516 -3.399006e-02 0.012211333 -0.055536263 -0.004465121
#> 14 all 14 -0.0264567669 -2.678186e-02 0.010886362 -0.046331291 -0.002632212
#> 15 all 15 -0.0187769334 -1.837139e-02 0.010233091 -0.038813423 0.001017049
#> 16 all 16 -0.0110944987 -1.040573e-02 0.010447301 -0.032515716 0.008103206
#> 17 all 17 -0.0040716106 -3.305002e-03 0.011266913 -0.026657806 0.016144226
#> 18 all 18 0.0016295834 2.157317e-03 0.012180471 -0.022810673 0.023880063
#> 19 all 19 0.0053469357 5.427787e-03 0.012712560 -0.019431562 0.028749455
#> 20 all 20 0.0066114503 6.262668e-03 0.012594828 -0.017980731 0.030579148
#> 21 all 21 0.0057267391 5.599276e-03 0.011973142 -0.017109163 0.028532293
#> 22 all 22 0.0031895655 3.058885e-03 0.011176477 -0.018478996 0.024616046
#> 23 all 23 -0.0005033068 8.476286e-06 0.010587823 -0.021504470 0.019473547
#> 24 all 24 -0.0048551144 -4.026258e-03 0.010515645 -0.026411537 0.015197758
#> 25 all 25 -0.0093690937 -8.950353e-03 0.011014021 -0.033360643 0.011133385
#> 26 all 26 -0.0135484811 -1.355319e-02 0.011843925 -0.039786160 0.007234839
#> 27 all 27 -0.0168965130 -1.706692e-02 0.012628487 -0.044034198 0.005304430
#> 28 all 28 -0.0189164260 -1.916774e-02 0.013005751 -0.046521779 0.003902172
#> 29 all 29 -0.0192280446 -1.945358e-02 0.012748719 -0.045148092 0.003422266
#> 30 all 30 -0.0179175453 -1.801236e-02 0.011962561 -0.041931878 0.003504038
#> 31 all 31 -0.0151876930 -1.521611e-02 0.010983868 -0.037592579 0.004415636
#> 32 all 32 -0.0112412523 -1.066910e-02 0.010381292 -0.033239951 0.006755703
#> 33 all 33 -0.0062809880 -5.258727e-03 0.010858401 -0.028830063 0.011305257
#> 34 all 34 -0.0005096648 3.365352e-04 0.012789285 -0.025693307 0.020886317
#> 35 all 35 0.0058699525 4.738214e-03 0.015967644 -0.023880468 0.036091154
#> 36 all 36 0.0126550994 1.046530e-02 0.019972894 -0.023996326 0.053492815
#> 37 all 37 0.0196430109 1.660745e-02 0.024455090 -0.024183566 0.071417709
#> pr_gr0
#> 1 0.9996
#> 2 0.9912
#> 3 0.9488
#> 4 0.6588
#> 5 0.2616
#> 6 0.0400
#> 7 0.0180
#> 8 0.0076
#> 9 0.0044
#> 10 0.0036
#> 11 0.0004
#> 12 0.0004
#> 13 0.0028
#> 14 0.0156
#> 15 0.0324
#> 16 0.1284
#> 17 0.3828
#> 18 0.5476
#> 19 0.6536
#> 20 0.6888
#> 21 0.6984
#> 22 0.6208
#> 23 0.5004
#> 24 0.3248
#> 25 0.2096
#> 26 0.1324
#> 27 0.0912
#> 28 0.0816
#> 29 0.0776
#> 30 0.0684
#> 31 0.0852
#> 32 0.1468
#> 33 0.3356
#> 34 0.5080
#> 35 0.5848
#> 36 0.7280
#> 37 0.7816
#>
#> $regcoef
#> name mean median sd q2.5
#> 1 interceptall 1.0053057396 0.9435615139 9.573831761 -17.736332990
#> 2 MomPriorBMI -0.0128161693 -0.0129383531 0.006572935 -0.025863997
#> 3 MomAge 0.0009698571 0.0010281618 0.006197181 -0.011324524
#> 4 raceAmInd 0.5026742600 0.5398510361 4.868252953 -8.862496874
#> 5 raceAsianPI 0.0429214006 0.0865122848 4.866348429 -9.264796472
#> 6 raceBlack 0.2175654220 0.2565301529 4.869786729 -9.101890733
#> 7 racewhite 0.2292053242 0.2569217638 4.868821949 -9.138486637
#> 8 HispanicNonHispanic 0.0725745586 0.0718154210 0.081680606 -0.084263798
#> 9 EstMonthConcept2 -0.7461284880 -0.7427810169 0.198844017 -1.145290955
#> 10 EstMonthConcept3 -0.2720966546 -0.2679461394 0.217610207 -0.678609185
#> 11 EstMonthConcept4 -0.4464398574 -0.4464582795 0.210262992 -0.859705497
#> 12 EstMonthConcept5 -0.0858711074 -0.0852191828 0.194611140 -0.471755043
#> 13 EstMonthConcept6 -0.2620705854 -0.2615817318 0.177207298 -0.613941527
#> 14 EstMonthConcept7 -0.2051188290 -0.2055368345 0.191203928 -0.571722882
#> 15 EstMonthConcept8 -0.1381595182 -0.1363281539 0.218015968 -0.568022089
#> 16 EstMonthConcept9 0.3281570639 0.3320009654 0.224159857 -0.116283282
#> 17 EstMonthConcept10 0.3038605003 0.3040384922 0.230278691 -0.145621451
#> 18 EstMonthConcept11 0.2331417588 0.2349690566 0.201591038 -0.162076054
#> 19 EstMonthConcept12 0.0852621251 0.0854955511 0.181189263 -0.271787215
#> 20 EstYearConcept 0.0005335146 0.0005022075 0.005228678 -0.009429765
#> 21 E -0.1543707830 -0.1536570077 0.042581780 -0.238957120
#> q97.5 pr_gr0
#> 1 1.964364e+01 0.5440
#> 2 8.200152e-05 0.0260
#> 3 1.291893e-02 0.5604
#> 4 9.727777e+00 0.5432
#> 5 9.235665e+00 0.5060
#> 6 9.463881e+00 0.5216
#> 7 9.404285e+00 0.5228
#> 8 2.293378e-01 0.8076
#> 9 -3.752693e-01 0.0000
#> 10 1.514936e-01 0.1040
#> 11 -3.309960e-02 0.0204
#> 12 2.938202e-01 0.3164
#> 13 7.448283e-02 0.0660
#> 14 1.794025e-01 0.1348
#> 15 2.772439e-01 0.2600
#> 16 7.584762e-01 0.9296
#> 17 7.589926e-01 0.9064
#> 18 6.262672e-01 0.8776
#> 19 4.408074e-01 0.6808
#> 20 1.133638e-02 0.5376
#> 21 -7.258479e-02 0.0004
#>
#> $sigma
#> name mean median sd q2.5 q97.5
#> 1 sigma 1.130948 1.130797 1.130797 1.083273 1.180284
#>
#> $names_groups
#> [1] "all"
#>
#> $n
#> [1] 1000
#>
#> $family
#> [1] "gaussian"
#>
#> attr(,"class")
#> [1] "summary.bdlim1"
# graph the estimated distributed lag functions for the one group
plot(sfit_onegroup)
# extract the weight function
getw(fit_onegroup)
#> group time mean median sd q2.5 q97.5
#> 1 all 1 -0.359485615 -3.193542e-01 0.09320389 -0.486868657 -0.11628218
#> 2 all 2 -0.248508811 -2.197547e-01 0.08274725 -0.369406649 -0.03860241
#> 3 all 3 -0.140191837 -1.215588e-01 0.07455418 -0.256723728 0.04011572
#> 4 all 4 -0.037194524 -2.832983e-02 0.06906203 -0.151186341 0.11313086
#> 5 all 5 0.057823295 5.867378e-02 0.06622443 -0.079078505 0.18109032
#> 6 all 6 0.142201792 1.335474e-01 0.06541334 -0.021704641 0.23941328
#> 7 all 7 0.213281134 1.987575e-01 0.06561237 0.026901834 0.28224569
#> 8 all 8 0.268401492 2.487827e-01 0.06575566 0.064325897 0.32053391
#> 9 all 9 0.304903034 2.829796e-01 0.06499787 0.086795763 0.34593800
#> 10 all 10 0.320125929 2.947633e-01 0.06289996 0.095974967 0.35214299
#> 11 all 11 0.312650800 2.894514e-01 0.05977628 0.085890897 0.33772504
#> 12 all 12 0.286020076 2.648541e-01 0.05707412 0.061414608 0.31389734
#> 13 all 13 0.245016642 2.249314e-01 0.05645559 0.049042646 0.29637017
#> 14 all 14 0.194423379 1.760188e-01 0.05887702 0.035690199 0.27106243
#> 15 all 15 0.139023173 1.237310e-01 0.06400534 -0.009621893 0.25204837
#> 16 all 16 0.083598905 6.928450e-02 0.07050247 -0.056689939 0.22555003
#> 17 all 17 0.032933459 2.301008e-02 0.07673887 -0.106974084 0.19543603
#> 18 all 18 -0.008190282 -1.489122e-02 0.08123185 -0.147559771 0.16425595
#> 19 all 19 -0.034989434 -3.775948e-02 0.08279368 -0.167190145 0.14398441
#> 20 all 20 -0.044075666 -4.255423e-02 0.08094492 -0.165187064 0.14005449
#> 21 all 21 -0.037638855 -3.944759e-02 0.07688592 -0.151987357 0.13786654
#> 22 all 22 -0.019263427 -2.314427e-02 0.07239135 -0.138931762 0.14199544
#> 23 all 23 0.007466188 5.979354e-05 0.06923249 -0.111890766 0.16770828
#> 24 all 24 0.038965564 3.190698e-02 0.06864256 -0.086903267 0.18648698
#> 25 all 25 0.071650273 6.177844e-02 0.07071390 -0.064156009 0.20922252
#> 26 all 26 0.101935886 9.410214e-02 0.07435055 -0.048976368 0.22690575
#> 27 all 27 0.126237976 1.188004e-01 0.07786068 -0.034910186 0.24560344
#> 28 all 28 0.140972114 1.299904e-01 0.07959038 -0.025807256 0.25512657
#> 29 all 29 0.143394841 1.332764e-01 0.07855530 -0.026378542 0.26462352
#> 30 all 30 0.134126565 1.237778e-01 0.07545557 -0.028685282 0.25728340
#> 31 all 31 0.114628661 1.015200e-01 0.07214021 -0.030700923 0.24597010
#> 32 all 32 0.086362504 6.905205e-02 0.07130293 -0.043637373 0.24503268
#> 33 all 33 0.050789470 3.555361e-02 0.07573195 -0.066329371 0.24145273
#> 34 all 34 0.009370934 -3.710891e-03 0.08678352 -0.125412969 0.22143340
#> 35 all 35 -0.036431728 -3.346339e-02 0.10381079 -0.202542507 0.20227620
#> 36 all 36 -0.085157141 -7.441205e-02 0.12520007 -0.287759664 0.17495002
#> 37 all 37 -0.135343930 -1.132658e-01 0.14935613 -0.376285682 0.16188012
#> pr_gr0
#> 1 0.0000
#> 2 0.0084
#> 3 0.0508
#> 4 0.3408
#> 5 0.7380
#> 6 0.9596
#> 7 0.9816
#> 8 0.9928
#> 9 0.9960
#> 10 0.9968
#> 11 1.0000
#> 12 1.0000
#> 13 0.9976
#> 14 0.9848
#> 15 0.9680
#> 16 0.8720
#> 17 0.6176
#> 18 0.4528
#> 19 0.3468
#> 20 0.3116
#> 21 0.3020
#> 22 0.3796
#> 23 0.5000
#> 24 0.6756
#> 25 0.7908
#> 26 0.8680
#> 27 0.9092
#> 28 0.9188
#> 29 0.9228
#> 30 0.9320
#> 31 0.9152
#> 32 0.8536
#> 33 0.6640
#> 34 0.4916
#> 35 0.4148
#> 36 0.2716
#> 37 0.2180
# }