Systolic Blood Pressure
modelsBP = lmer(adjSBP~Age * IncidIPV * Sex * Race + BMIcat + PovStat + SmokeEver + (Age|HNDid) + (1|subclass),IncidIPVbp)
(st = step(modelsBP))
Final Model by backwards elimination
modelsBP = lmer(adjSBP ~ Age + IncidIPV + Sex + BMIcat + (Age | HNDid) + (1|subclass) + Age:IncidIPV + Age:Sex + IncidIPV:Sex + Age:IncidIPV:Sex,IncidIPVbp)
summary(modelsBP)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ Age + IncidIPV + Sex + BMIcat + (Age | HNDid) + (1 | subclass) + Age:IncidIPV + Age:Sex + IncidIPV:Sex + Age:IncidIPV:Sex
## Data: IncidIPVbp
##
## REML criterion at convergence: 1122
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 1.62e+02 12.7345
## Age 5.16e-03 0.0718 1.00
## subclass (Intercept) 1.63e+01 4.0418
## Residual 1.29e+02 11.3540
## Number of obs: 138, groups: HNDid, 71; subclass, 46
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 125.227 4.853 35.000 25.81 < 2e-16
## Age 1.614 0.443 92.600 3.64 0.00044
## IncidIPV1 -0.915 7.962 41.500 -0.11 0.90908
## SexMen -13.028 6.125 38.000 -2.13 0.03997
## BMIcatObese 8.952 3.560 109.100 2.51 0.01337
## Age:IncidIPV1 -0.780 0.807 97.700 -0.97 0.33640
## Age:SexMen -1.513 0.616 105.400 -2.46 0.01560
## IncidIPV1:SexMen 21.123 11.342 39.300 1.86 0.07005
## Age:IncidIPV1:SexMen 2.420 1.119 106.100 2.16 0.03284
##
## Correlation of Fixed Effects:
## (Intr) Age InIPV1 SexMen BMIctO Ag:IIPV1 Ag:SxM IIPV1:
## Age 0.549
## IncidIPV1 -0.386 -0.348
## SexMen -0.560 -0.443 0.397
## BMIcatObese -0.459 0.055 -0.174 -0.104
## Ag:IncdIPV1 -0.248 -0.537 0.760 0.262 -0.121
## Age:SexMen -0.361 -0.698 0.266 0.696 -0.080 0.405
## IncdIPV1:SM 0.241 0.248 -0.729 -0.567 0.198 -0.552 -0.397
## Ag:IIPV1:SM 0.156 0.389 -0.567 -0.406 0.138 -0.741 -0.574 0.720
confint(modelsBP,level = 0.95)
## 2.5 % 97.5 %
## .sig01 NA NA
## .sig02 NA NA
## .sig03 NA 1.4473
## .sig04 NA NA
## .sigma 9.1886 13.8228
## (Intercept) 115.3870 134.3579
## Age 0.7454 2.4774
## IncidIPV1 -15.9458 14.8299
## SexMen -24.5774 -0.8278
## BMIcatObese 2.1380 16.3370
## Age:IncidIPV1 -2.3454 0.8343
## Age:SexMen -2.7327 -0.2825
## IncidIPV1:SexMen -1.7689 42.6970
## Age:IncidIPV1:SexMen 0.1286 4.6738
Age * IncidIPV * Sex Interaction Plot (sBP)
Diastolic Blood Pressure
modeldBP = lmer(adjDBP~Age * IncidIPV * Sex * Race + PovStat + BMIcat + SmokeEver + (Age|HNDid) + (1|subclass),IncidIPVbp)
(st = step(modeldBP))
Final Model by backwards elimination
modeldBP = lmer(adjDBP ~ Age + IncidIPV + Sex + BMIcat + (1 | HNDid) + (1|subclass) + Age:IncidIPV + Age:Sex + IncidIPV:Sex + Age:IncidIPV:Sex,data = IncidIPVbp)
summary(modeldBP)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ Age + IncidIPV + Sex + BMIcat + (1 | HNDid) + (1 | subclass) + Age:IncidIPV + Age:Sex + IncidIPV:Sex + Age:IncidIPV:Sex
## Data: IncidIPVbp
##
## REML criterion at convergence: 981.7
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 42.33 6.51
## subclass (Intercept) 4.17 2.04
## Residual 48.54 6.97
## Number of obs: 138, groups: HNDid, 71; subclass, 46
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 73.692 2.655 77.600 27.76 <2e-16
## Age 0.632 0.250 102.000 2.53 0.0129
## IncidIPV1 -3.567 4.409 86.100 -0.81 0.4208
## SexMen -3.999 3.380 83.800 -1.18 0.2401
## BMIcatObese 4.682 2.041 106.000 2.29 0.0238
## Age:IncidIPV1 -0.779 0.460 109.000 -1.69 0.0930
## Age:SexMen -0.559 0.351 108.200 -1.59 0.1139
## IncidIPV1:SexMen 16.389 6.246 85.600 2.62 0.0103
## Age:IncidIPV1:SexMen 1.994 0.635 109.400 3.14 0.0022
##
## Correlation of Fixed Effects:
## (Intr) Age InIPV1 SexMen BMIctO Ag:IIPV1 Ag:SxM IIPV1:
## Age 0.520
## IncidIPV1 -0.370 -0.338
## SexMen -0.545 -0.431 0.387
## BMIcatObese -0.469 0.075 -0.184 -0.113
## Ag:IncdIPV1 -0.232 -0.536 0.756 0.250 -0.129
## Age:SexMen -0.340 -0.697 0.254 0.689 -0.091 0.396
## IncdIPV1:SM 0.229 0.246 -0.732 -0.567 0.209 -0.551 -0.393
## Ag:IIPV1:SM 0.145 0.392 -0.564 -0.401 0.144 -0.740 -0.572 0.712
confint(modeldBP,level = 0.95)
## 2.5 % 97.5 %
## .sig01 3.7642 7.9765
## .sig02 NA 4.4378
## .sigma 5.6831 8.5298
## (Intercept) 68.5251 78.6692
## Age 0.1532 1.0999
## IncidIPV1 -11.8294 4.8221
## SexMen -10.3181 2.5275
## BMIcatObese 0.8294 8.8371
## Age:IncidIPV1 -1.6414 0.1135
## Age:SexMen -1.2180 0.1370
## IncidIPV1:SexMen 4.4775 28.0798
## Age:IncidIPV1:SexMen 0.7482 3.1864
Age * IncidIPV * Sex Interaction Plot (dBP)
m1sBP = lmer(adjSBP~Age * LifeIPV * Sex * Race + BMIcat + PovStat + SmokeEver + (Age|HNDid) + (1|subclass),LifeIPVbp)
(st = step(m1sBP))
Final Model by backwards elimination
m1sBP = lmer(adjSBP ~Age + LifeIPV + Sex + Race + BMIcat + PovStat + Age:LifeIPV + Age:Sex + LifeIPV:Sex + Age:LifeIPV:Sex + (Age|HNDid) + (1|subclass),data = LifeIPVbp)
summary(m1sBP)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ Age + LifeIPV + Sex + Race + BMIcat + PovStat + Age:LifeIPV + Age:Sex + LifeIPV:Sex + Age:LifeIPV:Sex + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 5526
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 1.63e+02 1.28e+01
## Age 2.93e-02 1.71e-01 1.00
## subclass (Intercept) 1.34e-08 1.16e-04
## Residual 1.33e+02 1.15e+01
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 112.100 2.341 378.000 47.88 < 2e-16
## Age 0.830 0.149 472.000 5.57 4.4e-08
## LifeIPV1 -1.244 2.213 280.000 -0.56 0.5745
## SexMen -2.119 2.087 277.000 -1.01 0.3110
## RaceAfrAm 4.493 1.785 313.000 2.52 0.0123
## BMIcatObese 9.046 1.659 602.000 5.45 7.2e-08
## PovStatBelow 3.633 1.717 317.000 2.12 0.0352
## Age:LifeIPV1 -0.248 0.224 473.000 -1.11 0.2685
## Age:SexMen -0.432 0.220 485.000 -1.96 0.0501
## LifeIPV1:SexMen 14.555 4.806 287.000 3.03 0.0027
## Age:LifeIPV1:SexMen 1.365 0.511 500.000 2.67 0.0078
##
## Correlation of Fixed Effects:
## (Intr) Age LfIPV1 SexMen RcAfrA BMIctO PvSttB Ag:LIPV1
## Age 0.161
## LifeIPV1 -0.458 -0.217
## SexMen -0.451 -0.208 0.444
## RaceAfrAm -0.496 -0.012 0.195 -0.052
## BMIcatObese -0.498 0.017 -0.072 0.049 -0.089
## PovStatBelw -0.260 0.113 -0.085 0.065 -0.017 0.017
## Age:LifIPV1 -0.094 -0.667 0.291 0.135 0.033 -0.058 -0.083
## Age:SexMen -0.084 -0.679 0.141 0.275 -0.032 -0.009 -0.096 0.453
## LfIPV1:SxMn 0.174 0.088 -0.439 -0.431 -0.009 0.070 -0.066 -0.125
## Ag:LIPV1:SM 0.000 0.288 -0.115 -0.120 0.044 0.060 0.001 -0.436
## Ag:SxM LIPV1:
## Age
## LifeIPV1
## SexMen
## RaceAfrAm
## BMIcatObese
## PovStatBelw
## Age:LifIPV1
## Age:SexMen
## LfIPV1:SxMn -0.116
## Ag:LIPV1:SM -0.428 0.410
confint(m1sBP,level = 0.95)
## 2.5 % 97.5 %
## .sig01 11.095208 1.407e+01
## .sig02 NA NA
## .sig03 0.003107 4.447e-01
## .sig04 NA NA
## .sigma 11.487317 1.172e+01
## (Intercept) 107.520234 1.167e+02
## Age 0.538663 1.119e+00
## LifeIPV1 -5.562934 3.046e+00
## SexMen -6.173995 1.931e+00
## RaceAfrAm 0.977252 7.994e+00
## BMIcatObese 5.841359 1.232e+01
## PovStatBelow 0.303346 6.957e+00
## Age:LifeIPV1 -0.682404 1.921e-01
## Age:SexMen -0.858397 -1.486e-04
## LifeIPV1:SexMen 5.227454 2.391e+01
## Age:LifeIPV1:SexMen 0.356442 2.354e+00
Age * LifeIPV * Sex Interaction Plot (sBP)
Diastolic Blood Pressure
m1dBP = lmer(adjDBP~Age * LifeIPV * Sex * Race + PovStat + BMIcat + SmokeEver + (Age|HNDid) + (1|subclass),LifeIPVbp)
(st = step(m1dBP))
Final Model by backwards elimination
m1dBP = lmer(adjDBP~Age + LifeIPV + Sex + Race + PovStat + BMIcat + Age:LifeIPV + Age:Sex + LifeIPV:Sex + Age:LifeIPV:Sex + (Age|HNDid) + (1|subclass), LifeIPVbp)
summary(m1dBP)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ Age + LifeIPV + Sex + Race + PovStat + BMIcat + Age:LifeIPV + Age:Sex + LifeIPV:Sex + Age:LifeIPV:Sex + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 4887
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 3.38e+01 5.8133
## Age 7.28e-04 0.0270 -1.00
## subclass (Intercept) 5.72e-04 0.0239
## Residual 6.38e+01 7.9866
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 64.0079 1.2794 359.0000 50.03 < 2e-16
## Age -0.0262 0.0835 416.0000 -0.31 0.7535
## LifeIPV1 1.1155 1.1695 306.0000 0.95 0.3409
## SexMen 2.3086 1.1020 304.0000 2.09 0.0370
## RaceAfrAm 3.2575 0.9821 321.0000 3.32 0.0010
## PovStatBelow 2.8070 0.9415 320.0000 2.98 0.0031
## BMIcatObese 5.3263 0.9808 536.0000 5.43 8.5e-08
## Age:LifeIPV1 -0.0370 0.1254 413.0000 -0.30 0.7680
## Age:SexMen 0.1214 0.1235 421.0000 0.98 0.3261
## LifeIPV1:SexMen 4.4797 2.5417 302.0000 1.76 0.0790
## Age:LifeIPV1:SexMen 0.6703 0.2987 428.0000 2.24 0.0253
##
## Correlation of Fixed Effects:
## (Intr) Age LfIPV1 SexMen RcAfrA PvSttB BMIctO Ag:LIPV1
## Age 0.036
## LifeIPV1 -0.435 -0.094
## SexMen -0.438 -0.081 0.439
## RaceAfrAm -0.476 -0.001 0.204 -0.057
## PovStatBelw -0.251 0.100 -0.092 0.068 -0.042
## BMIcatObese -0.531 0.025 -0.083 0.055 -0.109 0.022
## Age:LifIPV1 -0.009 -0.667 0.108 0.050 0.024 -0.070 -0.062
## Age:SexMen -0.011 -0.679 0.062 0.097 -0.016 -0.089 -0.014 0.452
## LfIPV1:SxMn 0.158 0.033 -0.435 -0.430 -0.001 -0.070 0.074 -0.043
## Ag:LIPV1:SM -0.036 0.277 -0.032 -0.043 0.053 -0.009 0.052 -0.418
## Ag:SxM LIPV1:
## Age
## LifeIPV1
## SexMen
## RaceAfrAm
## PovStatBelw
## BMIcatObese
## Age:LifIPV1
## Age:SexMen
## LfIPV1:SxMn -0.039
## Ag:LIPV1:SM -0.411 0.282
confint(m1dBP,level = 0.95)
## 2.5 % 97.5 %
## .sig01 4.6108 6.6108
## .sig02 NA NA
## .sig03 NA 0.2439
## .sig04 NA NA
## .sigma 7.3808 8.6231
## (Intercept) 61.5163 66.4912
## Age -0.1914 0.1385
## LifeIPV1 -1.1564 3.3839
## SexMen 0.1737 4.4449
## RaceAfrAm 1.3523 5.1595
## PovStatBelow 0.9848 4.6284
## BMIcatObese 3.4306 7.2472
## Age:LifeIPV1 -0.2804 0.2120
## Age:SexMen -0.1180 0.3641
## LifeIPV1:SexMen -0.4531 9.4176
## Age:LifeIPV1:SexMen 0.0774 1.2481
Age * LifeIPV * Sex Interaction Plot (dBP)
