SBP Final ModelSystolic Blood Pressure
## Loading required package: Matrix
## KernSmooth 2.23 loaded
## Copyright M. P. Wand 1997-2009
##
## Attaching package: 'lmerTest'
##
## The following object is masked from 'package:lme4':
##
## lmer
##
## The following object is masked from 'package:stats':
##
## step
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (Age + 0 | HNDid) was eliminated because of standard deviation being equal to 0
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ (Age + LifeIPV + Sex + PovStat + BMI)^5 + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 5476
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 135.647 11.647
## Age 0.141 0.375 1.00
## subclass (Intercept) 0.067 0.259
## Residual 131.726 11.477
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 84.30062 7.23458 330.00000
## Age 0.76647 0.78226 478.00000
## LifeIPV1 20.43028 11.82415 360.00000
## SexMen 13.58134 11.35261 339.00000
## PovStatBelow 9.18406 11.55388 311.00000
## BMI 1.24292 0.23228 335.00000
## Age:LifeIPV1 0.98646 1.23511 537.00000
## Age:SexMen -1.15352 1.22338 517.00000
## Age:PovStatBelow -0.64255 1.17148 486.00000
## Age:BMI 0.00755 0.02434 502.00000
## LifeIPV1:SexMen 5.76155 40.10221 510.00000
## LifeIPV1:PovStatBelow -25.78643 17.22118 350.00000
## LifeIPV1:BMI -0.70293 0.36650 377.00000
## SexMen:PovStatBelow -4.69607 20.18798 379.00000
## SexMen:BMI -0.52364 0.38052 346.00000
## PovStatBelow:BMI -0.29223 0.35108 324.00000
## Age:LifeIPV1:SexMen -0.07269 4.07873 457.00000
## Age:LifeIPV1:PovStatBelow 1.78810 1.83532 517.00000
## Age:LifeIPV1:BMI -0.03611 0.03861 547.00000
## Age:SexMen:PovStatBelow 1.95447 2.14982 560.00000
## Age:SexMen:BMI 0.01404 0.04090 527.00000
## Age:PovStatBelow:BMI 0.01236 0.03632 502.00000
## LifeIPV1:SexMen:PovStatBelow 18.40333 47.39050 498.00000
## LifeIPV1:SexMen:BMI 0.04776 1.56465 498.00000
## LifeIPV1:PovStatBelow:BMI 0.87929 0.52734 366.00000
## SexMen:PovStatBelow:BMI 0.43388 0.68103 395.00000
## Age:LifeIPV1:SexMen:PovStatBelow 4.95068 5.43666 573.00000
## Age:LifeIPV1:SexMen:BMI 0.02806 0.14701 387.00000
## Age:LifeIPV1:PovStatBelow:BMI -0.06901 0.05670 517.00000
## Age:SexMen:PovStatBelow:BMI -0.02910 0.07190 559.00000
## LifeIPV1:SexMen:PovStatBelow:BMI -0.54690 1.76225 511.00000
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -0.14612 0.18539 524.00000
## t value Pr(>|t|)
## (Intercept) 11.65 < 2e-16
## Age 0.98 0.328
## LifeIPV1 1.73 0.085
## SexMen 1.20 0.232
## PovStatBelow 0.79 0.427
## BMI 5.35 1.6e-07
## Age:LifeIPV1 0.80 0.425
## Age:SexMen -0.94 0.346
## Age:PovStatBelow -0.55 0.584
## Age:BMI 0.31 0.757
## LifeIPV1:SexMen 0.14 0.886
## LifeIPV1:PovStatBelow -1.50 0.135
## LifeIPV1:BMI -1.92 0.056
## SexMen:PovStatBelow -0.23 0.816
## SexMen:BMI -1.38 0.170
## PovStatBelow:BMI -0.83 0.406
## Age:LifeIPV1:SexMen -0.02 0.986
## Age:LifeIPV1:PovStatBelow 0.97 0.330
## Age:LifeIPV1:BMI -0.94 0.350
## Age:SexMen:PovStatBelow 0.91 0.364
## Age:SexMen:BMI 0.34 0.731
## Age:PovStatBelow:BMI 0.34 0.734
## LifeIPV1:SexMen:PovStatBelow 0.39 0.698
## LifeIPV1:SexMen:BMI 0.03 0.976
## LifeIPV1:PovStatBelow:BMI 1.67 0.096
## SexMen:PovStatBelow:BMI 0.64 0.524
## Age:LifeIPV1:SexMen:PovStatBelow 0.91 0.363
## Age:LifeIPV1:SexMen:BMI 0.19 0.849
## Age:LifeIPV1:PovStatBelow:BMI -1.22 0.224
## Age:SexMen:PovStatBelow:BMI -0.40 0.686
## LifeIPV1:SexMen:PovStatBelow:BMI -0.31 0.756
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -0.79 0.431
##
## Correlation matrix not shown by default, as p = 32 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
SBP Final Model
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ Age + LifeIPV + Sex + PovStat + BMI + Age:LifeIPV + Age:Sex + Age:PovStat + Age:BMI + LifeIPV:BMI + Sex:PovStat + Age:LifeIPV:BMI + Age:Sex:PovStat + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 5493
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 137.731 11.736
## Age 0.116 0.340 1.00
## subclass (Intercept) 0.696 0.834
## Residual 130.904 11.441
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 89.5638 4.5619 331.0000 19.63 < 2e-16
## Age 0.5044 0.4893 513.0000 1.03 0.3031
## LifeIPV1 10.6734 7.2557 367.0000 1.47 0.1421
## SexMen -0.7366 2.1985 260.0000 -0.34 0.7379
## PovStatBelow 0.1859 2.1646 256.0000 0.09 0.9316
## BMI 1.0475 0.1407 335.0000 7.45 8.2e-13
## Age:LifeIPV1 2.2890 0.8140 560.0000 2.81 0.0051
## Age:SexMen -0.5869 0.2349 477.0000 -2.50 0.0128
## Age:PovStatBelow -0.3793 0.2169 471.0000 -1.75 0.0810
## Age:BMI 0.0140 0.0149 523.0000 0.94 0.3467
## LifeIPV1:BMI -0.3074 0.2268 380.0000 -1.36 0.1761
## SexMen:PovStatBelow 9.6714 3.5600 261.0000 2.72 0.0070
## Age:LifeIPV1:BMI -0.0776 0.0254 552.0000 -3.05 0.0024
## Age:SexMen:PovStatBelow 1.2424 0.3756 510.0000 3.31 0.0010
##
## Correlation of Fixed Effects:
## (Intr) Age LfIPV1 SexMen PvSttB BMI Ag:LIPV1 Ag:SxM
## Age 0.459
## LifeIPV1 -0.590 -0.278
## SexMen -0.334 -0.114 0.088
## PovStatBelw -0.087 0.062 -0.116 0.331
## BMI -0.942 -0.475 0.595 0.117 -0.079
## Age:LifIPV1 -0.259 -0.579 0.395 0.013 -0.143 0.291
## Age:SexMen -0.126 -0.378 0.021 0.420 0.090 0.049 0.155
## Ag:PvSttBlw 0.054 -0.192 -0.137 0.104 0.450 -0.111 0.029 0.390
## Age:BMI -0.479 -0.942 0.312 0.036 -0.119 0.541 0.566 0.168
## LifIPV1:BMI 0.569 0.278 -0.966 -0.016 0.087 -0.619 -0.373 0.013
## SxMn:PvSttB 0.051 -0.021 0.080 -0.554 -0.598 0.063 0.073 -0.221
## A:LIPV1:BMI 0.260 0.561 -0.373 0.017 0.124 -0.310 -0.972 -0.094
## Ag:SxMn:PSB 0.004 0.146 0.055 -0.227 -0.252 0.032 -0.030 -0.579
## Ag:PSB Ag:BMI LIPV1: SM:PSB A:LIPV1:
## Age
## LifeIPV1
## SexMen
## PovStatBelw
## BMI
## Age:LifIPV1
## Age:SexMen
## Ag:PvSttBlw
## Age:BMI 0.001
## LifIPV1:BMI 0.117 -0.333
## SxMn:PvSttB -0.272 0.062 -0.083
## A:LIPV1:BMI -0.030 -0.590 0.380 -0.069
## Ag:SxMn:PSB -0.572 -0.030 -0.051 0.400 0.020
Diastolic Blood Pressure
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ (Age + LifeIPV + Sex + PovStat + BMI)^5 + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 4885
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.93e+01 5.41e+00
## Age 7.13e-03 8.44e-02 1.00
## subclass (Intercept) 1.23e-10 1.11e-05
## Residual 6.36e+01 7.98e+00
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 52.76637 4.01270 314.00000
## Age -0.10613 0.45152 401.00000
## LifeIPV1 15.04874 6.62041 334.00000
## SexMen 9.41670 6.30676 313.00000
## PovStatBelow 3.10896 6.36041 299.00000
## BMI 0.57851 0.12878 305.00000
## Age:LifeIPV1 1.23185 0.72761 456.00000
## Age:SexMen -0.31963 0.71398 429.00000
## Age:PovStatBelow -0.39832 0.67771 406.00000
## Age:BMI 0.00367 0.01415 422.00000
## LifeIPV1:SexMen -18.91715 24.22005 458.00000
## LifeIPV1:PovStatBelow -17.00978 9.60749 329.00000
## LifeIPV1:BMI -0.45934 0.20628 347.00000
## SexMen:PovStatBelow 0.65166 11.37558 348.00000
## SexMen:BMI -0.21771 0.21153 308.00000
## PovStatBelow:BMI -0.05340 0.19431 311.00000
## Age:LifeIPV1:SexMen -2.15198 2.63998 372.00000
## Age:LifeIPV1:PovStatBelow -0.73095 1.07029 427.00000
## Age:LifeIPV1:BMI -0.03553 0.02285 466.00000
## Age:SexMen:PovStatBelow 1.64135 1.27177 472.00000
## Age:SexMen:BMI 0.01312 0.02396 437.00000
## Age:PovStatBelow:BMI 0.01443 0.02119 424.00000
## LifeIPV1:SexMen:PovStatBelow 28.27771 28.09857 456.00000
## LifeIPV1:SexMen:BMI 0.62957 0.95339 449.00000
## LifeIPV1:PovStatBelow:BMI 0.57882 0.29597 344.00000
## SexMen:PovStatBelow:BMI 0.03576 0.38582 361.00000
## Age:LifeIPV1:SexMen:PovStatBelow 7.94957 3.44538 499.00000
## Age:LifeIPV1:SexMen:BMI 0.08045 0.09588 319.00000
## Age:LifeIPV1:PovStatBelow:BMI 0.00893 0.03311 431.00000
## Age:SexMen:PovStatBelow:BMI -0.04596 0.04258 474.00000
## LifeIPV1:SexMen:PovStatBelow:BMI -0.76965 1.05831 459.00000
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -0.23519 0.11811 445.00000
## t value Pr(>|t|)
## (Intercept) 13.15 <2e-16
## Age -0.24 0.814
## LifeIPV1 2.27 0.024
## SexMen 1.49 0.136
## PovStatBelow 0.49 0.625
## BMI 4.49 1e-05
## Age:LifeIPV1 1.69 0.091
## Age:SexMen -0.45 0.655
## Age:PovStatBelow -0.59 0.557
## Age:BMI 0.26 0.795
## LifeIPV1:SexMen -0.78 0.435
## LifeIPV1:PovStatBelow -1.77 0.078
## LifeIPV1:BMI -2.23 0.027
## SexMen:PovStatBelow 0.06 0.954
## SexMen:BMI -1.03 0.304
## PovStatBelow:BMI -0.27 0.784
## Age:LifeIPV1:SexMen -0.82 0.416
## Age:LifeIPV1:PovStatBelow -0.68 0.495
## Age:LifeIPV1:BMI -1.55 0.121
## Age:SexMen:PovStatBelow 1.29 0.197
## Age:SexMen:BMI 0.55 0.584
## Age:PovStatBelow:BMI 0.68 0.496
## LifeIPV1:SexMen:PovStatBelow 1.01 0.315
## LifeIPV1:SexMen:BMI 0.66 0.509
## LifeIPV1:PovStatBelow:BMI 1.96 0.051
## SexMen:PovStatBelow:BMI 0.09 0.926
## Age:LifeIPV1:SexMen:PovStatBelow 2.31 0.021
## Age:LifeIPV1:SexMen:BMI 0.84 0.402
## Age:LifeIPV1:PovStatBelow:BMI 0.27 0.788
## Age:SexMen:PovStatBelow:BMI -1.08 0.281
## LifeIPV1:SexMen:PovStatBelow:BMI -0.73 0.467
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -1.99 0.047
##
## Correlation matrix not shown by default, as p = 32 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Diastolic Blood Pressure Final Model
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ Age + LifeIPV + Sex + PovStat + BMI + Age:LifeIPV + Age:Sex + Age:PovStat + Age:BMI + LifeIPV:Sex + LifeIPV:PovStat + LifeIPV:BMI + Sex:PovStat + Sex:BMI + PovStat:BMI + Age:LifeIPV:Sex + Age:LifeIPV:PovStat + Age:LifeIPV:BMI + Age:Sex:PovStat + Age:Sex:BMI + Age:PovStat:BMI + LifeIPV:Sex:PovStat + LifeIPV:Sex:BMI + LifeIPV:PovStat:BMI + Sex:PovStat:BMI + Age:LifeIPV:Sex:PovStat + Age:LifeIPV:Sex:BMI + Age:LifeIPV:PovStat:BMI + Age:Sex:PovStat:BMI + LifeIPV:Sex:PovStat:BMI + Age:LifeIPV:Sex:PovStat:BMI + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 4885
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 2.93e+01 5.41e+00
## Age 7.13e-03 8.44e-02 1.00
## subclass (Intercept) 1.23e-10 1.11e-05
## Residual 6.36e+01 7.98e+00
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 52.76637 4.01270 314.00000
## Age -0.10613 0.45152 401.00000
## LifeIPV1 15.04874 6.62041 334.00000
## SexMen 9.41670 6.30676 313.00000
## PovStatBelow 3.10896 6.36041 299.00000
## BMI 0.57851 0.12878 305.00000
## Age:LifeIPV1 1.23185 0.72761 456.00000
## Age:SexMen -0.31963 0.71398 429.00000
## Age:PovStatBelow -0.39832 0.67771 406.00000
## Age:BMI 0.00367 0.01415 422.00000
## LifeIPV1:SexMen -18.91715 24.22005 458.00000
## LifeIPV1:PovStatBelow -17.00978 9.60749 329.00000
## LifeIPV1:BMI -0.45934 0.20628 347.00000
## SexMen:PovStatBelow 0.65166 11.37558 348.00000
## SexMen:BMI -0.21771 0.21153 308.00000
## PovStatBelow:BMI -0.05340 0.19431 311.00000
## Age:LifeIPV1:SexMen -2.15198 2.63998 372.00000
## Age:LifeIPV1:PovStatBelow -0.73095 1.07029 427.00000
## Age:LifeIPV1:BMI -0.03553 0.02285 466.00000
## Age:SexMen:PovStatBelow 1.64135 1.27177 472.00000
## Age:SexMen:BMI 0.01312 0.02396 437.00000
## Age:PovStatBelow:BMI 0.01443 0.02119 424.00000
## LifeIPV1:SexMen:PovStatBelow 28.27771 28.09857 456.00000
## LifeIPV1:SexMen:BMI 0.62957 0.95339 449.00000
## LifeIPV1:PovStatBelow:BMI 0.57882 0.29597 344.00000
## SexMen:PovStatBelow:BMI 0.03576 0.38582 361.00000
## Age:LifeIPV1:SexMen:PovStatBelow 7.94957 3.44538 499.00000
## Age:LifeIPV1:SexMen:BMI 0.08045 0.09588 319.00000
## Age:LifeIPV1:PovStatBelow:BMI 0.00893 0.03311 431.00000
## Age:SexMen:PovStatBelow:BMI -0.04596 0.04258 474.00000
## LifeIPV1:SexMen:PovStatBelow:BMI -0.76965 1.05831 459.00000
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -0.23519 0.11811 445.00000
## t value Pr(>|t|)
## (Intercept) 13.15 <2e-16
## Age -0.24 0.814
## LifeIPV1 2.27 0.024
## SexMen 1.49 0.136
## PovStatBelow 0.49 0.625
## BMI 4.49 1e-05
## Age:LifeIPV1 1.69 0.091
## Age:SexMen -0.45 0.655
## Age:PovStatBelow -0.59 0.557
## Age:BMI 0.26 0.795
## LifeIPV1:SexMen -0.78 0.435
## LifeIPV1:PovStatBelow -1.77 0.078
## LifeIPV1:BMI -2.23 0.027
## SexMen:PovStatBelow 0.06 0.954
## SexMen:BMI -1.03 0.304
## PovStatBelow:BMI -0.27 0.784
## Age:LifeIPV1:SexMen -0.82 0.416
## Age:LifeIPV1:PovStatBelow -0.68 0.495
## Age:LifeIPV1:BMI -1.55 0.121
## Age:SexMen:PovStatBelow 1.29 0.197
## Age:SexMen:BMI 0.55 0.584
## Age:PovStatBelow:BMI 0.68 0.496
## LifeIPV1:SexMen:PovStatBelow 1.01 0.315
## LifeIPV1:SexMen:BMI 0.66 0.509
## LifeIPV1:PovStatBelow:BMI 1.96 0.051
## SexMen:PovStatBelow:BMI 0.09 0.926
## Age:LifeIPV1:SexMen:PovStatBelow 2.31 0.021
## Age:LifeIPV1:SexMen:BMI 0.84 0.402
## Age:LifeIPV1:PovStatBelow:BMI 0.27 0.788
## Age:SexMen:PovStatBelow:BMI -1.08 0.281
## LifeIPV1:SexMen:PovStatBelow:BMI -0.73 0.467
## Age:LifeIPV1:SexMen:PovStatBelow:BMI -1.99 0.047
##
## Correlation matrix not shown by default, as p = 32 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Systolic Blood Pressure (categorical BMI)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (1 | subclass) was eliminated because of standard deviation being equal to 0
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (Age + 0 | HNDid) was eliminated because of standard deviation being equal to 0
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ (Age + LifeIPV + PovStat + Sex + BMIcat2)^5 + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 5428
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 153.8561 12.40
## Age 0.0485 0.22 1.00
## subclass (Intercept) 0.0000 0.00
## Residual 131.9516 11.49
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 115.543 2.354 383.000
## Age 0.947 0.260 549.000
## LifeIPV1 4.565 3.818 396.000
## PovStatBelow -0.802 4.499 320.000
## SexMen 1.399 3.160 344.000
## BMIcat2Obese 14.626 3.103 553.000
## Age:LifeIPV1 0.227 0.399 579.000
## Age:PovStatBelow -0.476 0.425 522.000
## Age:SexMen -0.804 0.346 528.000
## Age:BMIcat2Obese -0.291 0.344 631.000
## LifeIPV1:PovStatBelow -1.049 6.099 343.000
## LifeIPV1:SexMen 3.744 7.831 310.000
## LifeIPV1:BMIcat2Obese -11.143 4.745 577.000
## PovStatBelow:SexMen 7.053 5.809 320.000
## PovStatBelow:BMIcat2Obese 2.681 5.787 409.000
## SexMen:BMIcat2Obese -10.028 4.637 419.000
## Age:LifeIPV1:PovStatBelow -0.108 0.614 566.000
## Age:LifeIPV1:SexMen 0.564 0.936 579.000
## Age:LifeIPV1:BMIcat2Obese -0.217 0.509 632.000
## Age:PovStatBelow:SexMen 1.123 0.591 539.000
## Age:PovStatBelow:BMIcat2Obese 0.866 0.566 583.000
## Age:SexMen:BMIcat2Obese 0.473 0.518 578.000
## LifeIPV1:PovStatBelow:SexMen 9.873 12.252 307.000
## LifeIPV1:PovStatBelow:BMIcat2Obese 5.488 8.052 467.000
## LifeIPV1:SexMen:BMIcat2Obese 17.655 26.523 307.000
## PovStatBelow:SexMen:BMIcat2Obese 3.910 8.290 490.000
## Age:LifeIPV1:PovStatBelow:SexMen 3.000 1.686 634.000
## Age:LifeIPV1:PovStatBelow:BMIcat2Obese -0.988 0.811 625.000
## Age:LifeIPV1:SexMen:BMIcat2Obese 0.741 2.039 274.000
## Age:PovStatBelow:SexMen:BMIcat2Obese -0.456 0.900 607.000
## LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -27.295 29.567 369.000
## Age:LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -3.848 2.646 414.000
## t value Pr(>|t|)
## (Intercept) 49.09 < 2e-16
## Age 3.65 0.00029
## LifeIPV1 1.20 0.23257
## PovStatBelow -0.18 0.85860
## SexMen 0.44 0.65813
## BMIcat2Obese 4.71 3.1e-06
## Age:LifeIPV1 0.57 0.56945
## Age:PovStatBelow -1.12 0.26335
## Age:SexMen -2.32 0.02066
## Age:BMIcat2Obese -0.85 0.39814
## LifeIPV1:PovStatBelow -0.17 0.86351
## LifeIPV1:SexMen 0.48 0.63295
## LifeIPV1:BMIcat2Obese -2.35 0.01918
## PovStatBelow:SexMen 1.21 0.22558
## PovStatBelow:BMIcat2Obese 0.46 0.64338
## SexMen:BMIcat2Obese -2.16 0.03115
## Age:LifeIPV1:PovStatBelow -0.18 0.86075
## Age:LifeIPV1:SexMen 0.60 0.54668
## Age:LifeIPV1:BMIcat2Obese -0.43 0.66970
## Age:PovStatBelow:SexMen 1.90 0.05787
## Age:PovStatBelow:BMIcat2Obese 1.53 0.12627
## Age:SexMen:BMIcat2Obese 0.91 0.36173
## LifeIPV1:PovStatBelow:SexMen 0.81 0.42100
## LifeIPV1:PovStatBelow:BMIcat2Obese 0.68 0.49588
## LifeIPV1:SexMen:BMIcat2Obese 0.67 0.50613
## PovStatBelow:SexMen:BMIcat2Obese 0.47 0.63741
## Age:LifeIPV1:PovStatBelow:SexMen 1.78 0.07567
## Age:LifeIPV1:PovStatBelow:BMIcat2Obese -1.22 0.22352
## Age:LifeIPV1:SexMen:BMIcat2Obese 0.36 0.71678
## Age:PovStatBelow:SexMen:BMIcat2Obese -0.51 0.61236
## LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -0.92 0.35653
## Age:LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -1.45 0.14662
##
## Correlation matrix not shown by default, as p = 32 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Systolic Blood Pressure Final Model (categorical BMI)
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjSBP ~ Age + LifeIPV + PovStat + Sex + BMIcat2 + Age:LifeIPV + Age:PovStat + Age:Sex + Age:BMIcat2 + LifeIPV:BMIcat2 + PovStat:Sex + Sex:BMIcat2 + Age:LifeIPV:BMIcat2 + Age:PovStat:Sex + (1 | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 5509
##
## Random effects:
## Groups Name Variance Std.Dev.
## HNDid (Intercept) 147 12.1
## subclass (Intercept) 0 0.0
## Residual 134 11.6
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 114.329 1.946 382.000 58.74 < 2e-16
## Age 0.748 0.194 531.000 3.86 0.00013
## LifeIPV1 5.495 2.457 407.000 2.24 0.02586
## PovStatBelow 0.765 2.150 334.000 0.36 0.72204
## SexMen 2.025 2.585 372.000 0.78 0.43385
## BMIcat2Obese 15.896 2.423 505.000 6.56 1.3e-10
## Age:LifeIPV1 0.390 0.263 599.000 1.48 0.13832
## Age:PovStatBelow -0.284 0.222 480.000 -1.28 0.20199
## Age:SexMen -0.536 0.240 492.000 -2.23 0.02638
## Age:BMIcat2Obese 0.207 0.205 606.000 1.01 0.31337
## LifeIPV1:BMIcat2Obese -9.354 3.360 534.000 -2.78 0.00557
## PovStatBelow:SexMen 9.377 3.564 328.000 2.63 0.00892
## SexMen:BMIcat2Obese -9.139 3.307 501.000 -2.76 0.00593
## Age:LifeIPV1:BMIcat2Obese -0.846 0.340 645.000 -2.49 0.01316
## Age:PovStatBelow:SexMen 1.085 0.393 503.000 2.76 0.00596
##
## Correlation of Fixed Effects:
## (Intr) Age LfIPV1 PvSttB SexMen BMIc2O Ag:LIPV1 Ag:PSB
## Age 0.025
## LifeIPV1 -0.537 -0.015
## PovStatBelw -0.382 0.038 -0.145
## SexMen -0.701 -0.026 0.311 0.299
## BMIcat2Obes -0.614 0.007 0.438 -0.019 0.406
## Age:LifIPV1 -0.013 -0.509 0.193 -0.156 0.009 0.031
## Ag:PvSttBlw 0.052 -0.478 -0.160 0.185 -0.022 -0.095 -0.053
## Age:SexMen -0.024 -0.625 0.005 0.000 0.074 -0.037 0.242 0.390
## Ag:BMIct2Ob -0.012 -0.493 0.047 -0.084 0.005 0.100 0.365 -0.016
## LIPV1:BMI2O 0.365 -0.045 -0.656 0.098 -0.192 -0.657 -0.118 0.152
## PvSttBlw:SM 0.245 0.006 0.056 -0.595 -0.477 0.034 0.069 -0.111
## SxMn:BMIc2O 0.409 -0.056 -0.251 -0.002 -0.536 -0.647 0.020 0.054
## A:LIPV1:BMI -0.022 0.292 -0.128 0.141 0.022 -0.078 -0.659 0.061
## Ag:PvStB:SM -0.021 0.285 0.080 -0.105 -0.013 0.050 0.017 -0.566
## Ag:SxM A:BMI2 LIPV1: PSB:SM SM:BMI A:LIPV1:
## Age
## LifeIPV1
## PovStatBelw
## SexMen
## BMIcat2Obes
## Age:LifIPV1
## Ag:PvSttBlw
## Age:SexMen
## Ag:BMIct2Ob 0.096
## LIPV1:BMI2O 0.066 -0.078
## PvSttBlw:SM -0.070 0.011 -0.098
## SxMn:BMIc2O 0.103 0.030 0.361 0.005
## A:LIPV1:BMI -0.045 -0.612 0.166 -0.063 0.009
## Ag:PvStB:SM -0.570 0.025 -0.082 0.126 -0.096 -0.073
SBP and IPV: Age x LifeIPV x BMI
## Loading required package: mvtnorm
## Loading required package: TH.data
## Age BMIcat2 LifeIPV adjSBP PovStat Sex hat
## 1 -20 nonObese 0 0 0.3724 0.3769 104.8
## 2 -19 nonObese 0 0 0.3724 0.3769 105.4
## 3 -18 nonObese 0 0 0.3724 0.3769 106.0
## 4 -17 nonObese 0 0 0.3724 0.3769 106.6
## 5 -16 nonObese 0 0 0.3724 0.3769 107.2
## 6 -15 nonObese 0 0 0.3724 0.3769 107.8
Diastolic Blood Pressure (categorical BMI)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Random term (Age | HNDid) was eliminated because of having correlation +-1 or NaN
## Warning: number of observations <= rank(Z); variance-covariance matrix
## will be unidentifiable
## Random term (Age + 0 | HNDid) was eliminated because of standard deviation being equal to 0
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ (Age + LifeIPV + PovStat + Sex + BMIcat2)^5 + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 4831
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 3.40e+01 5.83e+00
## Age 1.02e-03 3.20e-02 1.00
## subclass (Intercept) 4.38e-11 6.62e-06
## Residual 6.42e+01 8.01e+00
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 67.6587 1.3323 376.0000
## Age -0.0485 0.1534 486.0000
## LifeIPV1 3.4931 2.1658 382.0000
## PovStatBelow 0.7860 2.4838 298.0000
## SexMen 3.9286 1.7670 350.0000
## BMIcat2Obese 6.2084 1.8274 478.0000
## Age:LifeIPV1 0.3572 0.2376 513.0000
## Age:PovStatBelow -0.0694 0.2475 436.0000
## Age:SexMen 0.0567 0.2028 460.0000
## Age:BMIcat2Obese -0.0995 0.2112 588.0000
## LifeIPV1:PovStatBelow -1.3453 3.3965 326.0000
## LifeIPV1:SexMen -4.2436 4.3189 313.0000
## LifeIPV1:BMIcat2Obese -5.5453 2.8253 507.0000
## PovStatBelow:SexMen 1.5228 3.2116 313.0000
## PovStatBelow:BMIcat2Obese 1.6537 3.2742 356.0000
## SexMen:BMIcat2Obese -3.6476 2.6432 389.0000
## Age:LifeIPV1:PovStatBelow -0.4627 0.3628 482.0000
## Age:LifeIPV1:SexMen -0.1697 0.5578 509.0000
## Age:LifeIPV1:BMIcat2Obese -0.2244 0.3143 603.0000
## Age:PovStatBelow:SexMen 0.4157 0.3464 453.0000
## Age:PovStatBelow:BMIcat2Obese 0.4445 0.3374 497.0000
## Age:SexMen:BMIcat2Obese 0.1804 0.3075 503.0000
## LifeIPV1:PovStatBelow:SexMen 14.4373 6.7481 310.0000
## LifeIPV1:PovStatBelow:BMIcat2Obese 6.0489 4.6337 403.0000
## LifeIPV1:SexMen:BMIcat2Obese 14.7168 16.3252 249.0000
## PovStatBelow:SexMen:BMIcat2Obese 1.8459 4.8336 454.0000
## Age:LifeIPV1:PovStatBelow:SexMen 2.9079 1.0464 607.0000
## Age:LifeIPV1:PovStatBelow:BMIcat2Obese -0.0973 0.4907 551.0000
## Age:LifeIPV1:SexMen:BMIcat2Obese 1.0689 1.2896 218.0000
## Age:PovStatBelow:SexMen:BMIcat2Obese -0.6416 0.5415 537.0000
## LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -26.0766 17.8956 299.0000
## Age:LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -3.4198 1.6632 343.0000
## t value Pr(>|t|)
## (Intercept) 50.78 < 2e-16
## Age -0.32 0.75202
## LifeIPV1 1.61 0.10760
## PovStatBelow 0.32 0.75187
## SexMen 2.22 0.02683
## BMIcat2Obese 3.40 0.00074
## Age:LifeIPV1 1.50 0.13337
## Age:PovStatBelow -0.28 0.77913
## Age:SexMen 0.28 0.77987
## Age:BMIcat2Obese -0.47 0.63786
## LifeIPV1:PovStatBelow -0.40 0.69231
## LifeIPV1:SexMen -0.98 0.32659
## LifeIPV1:BMIcat2Obese -1.96 0.05023
## PovStatBelow:SexMen 0.47 0.63573
## PovStatBelow:BMIcat2Obese 0.51 0.61382
## SexMen:BMIcat2Obese -1.38 0.16838
## Age:LifeIPV1:PovStatBelow -1.28 0.20278
## Age:LifeIPV1:SexMen -0.30 0.76115
## Age:LifeIPV1:BMIcat2Obese -0.71 0.47558
## Age:PovStatBelow:SexMen 1.20 0.23065
## Age:PovStatBelow:BMIcat2Obese 1.32 0.18824
## Age:SexMen:BMIcat2Obese 0.59 0.55770
## LifeIPV1:PovStatBelow:SexMen 2.14 0.03318
## LifeIPV1:PovStatBelow:BMIcat2Obese 1.31 0.19250
## LifeIPV1:SexMen:BMIcat2Obese 0.90 0.36820
## PovStatBelow:SexMen:BMIcat2Obese 0.38 0.70272
## Age:LifeIPV1:PovStatBelow:SexMen 2.78 0.00562
## Age:LifeIPV1:PovStatBelow:BMIcat2Obese -0.20 0.84296
## Age:LifeIPV1:SexMen:BMIcat2Obese 0.83 0.40808
## Age:PovStatBelow:SexMen:BMIcat2Obese -1.18 0.23659
## LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -1.46 0.14612
## Age:LifeIPV1:PovStatBelow:SexMen:BMIcat2Obese -2.06 0.04053
##
## Correlation matrix not shown by default, as p = 32 > 20.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
Diastolic Blood Pressure Final Model (categorical BMI)
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Warning: number of observations <= rank(Z); variance-covariance matrix will be unidentifiable
## Linear mixed model fit by REML ['merModLmerTest']
## Formula: adjDBP ~ Age + LifeIPV + PovStat + Sex + BMIcat2 + Age:LifeIPV + Age:Sex + LifeIPV:Sex + Age:LifeIPV:Sex + (Age | HNDid) + (1 | subclass)
## Data: LifeIPVbp
##
## REML criterion at convergence: 4900
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## HNDid (Intercept) 3.69e+01 6.0761
## Age 4.22e-04 0.0205 1.00
## subclass (Intercept) 0.00e+00 0.0000
## Residual 6.36e+01 7.9762
## Number of obs: 666, groups: HNDid, 334; subclass, 222
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 67.8376 0.9754 337.0000 69.55 < 2e-16
## Age -0.0149 0.0851 421.0000 -0.18 0.8610
## LifeIPV1 0.7749 1.1738 296.0000 0.66 0.5097
## PovStatBelow 3.1238 0.9583 321.0000 3.26 0.0012
## SexMen 2.9653 1.1377 299.0000 2.61 0.0096
## BMIcat2Obese 4.9367 0.8687 490.0000 5.68 2.3e-08
## Age:LifeIPV1 -0.0488 0.1277 420.0000 -0.38 0.7023
## Age:SexMen 0.1415 0.1259 428.0000 1.12 0.2618
## LifeIPV1:SexMen 3.2652 2.6074 300.0000 1.25 0.2114
## Age:LifeIPV1:SexMen 0.6209 0.3000 434.0000 2.07 0.0391
##
## Correlation of Fixed Effects:
## (Intr) Age LfIPV1 PvSttB SexMen BMIc2O Ag:LIPV1 Ag:SxM
## Age 0.115
## LifeIPV1 -0.524 -0.140
## PovStatBelw -0.375 0.106 -0.083
## SexMen -0.645 -0.127 0.465 0.071
## BMIcat2Obes -0.471 0.015 0.017 0.058 0.127
## Age:LifIPV1 -0.063 -0.667 0.170 -0.076 0.081 -0.036
## Age:SexMen -0.083 -0.678 0.097 -0.092 0.164 0.018 0.452
## LfIPV1:SxMn 0.281 0.051 -0.441 -0.072 -0.435 -0.022 -0.068 -0.067
## Ag:LIPV1:SM 0.028 0.281 -0.069 -0.003 -0.066 0.039 -0.424 -0.415
## LIPV1:
## Age
## LifeIPV1
## PovStatBelw
## SexMen
## BMIcat2Obes
## Age:LifIPV1
## Age:SexMen
## LfIPV1:SxMn
## Ag:LIPV1:SM 0.328
DBP and IPV: Age x LifeIPV x Sex
pAge = seq(-20,20)
hatIPVhyp1 = zMixHat(LifeIPVbp, mm4, vary = "Age=pAge, Sex=zQ(Women,Men),LifeIPV=zQ(0,1)",fixedCov=c("PovStat","BMIcat2"))
head(hatIPVhyp1)
## Age Sex LifeIPV adjDBP PovStat BMIcat2 hat
## 1 -20 Women 0 0 0.3724 0.4414 71.48
## 2 -19 Women 0 0 0.3724 0.4414 71.46
## 3 -18 Women 0 0 0.3724 0.4414 71.45
## 4 -17 Women 0 0 0.3724 0.4414 71.43
## 5 -16 Women 0 0 0.3724 0.4414 71.42
## 6 -15 Women 0 0 0.3724 0.4414 71.40
par(mar=c(4,4,.5,2),las = 1, lwd = 2)
HNDcolors = HNDpltColors()
with(hatIPVhyp1[hatIPVhyp1$Sex == "Women" & hatIPVhyp1$LifeIPV == "0", ],plot(pAge,hat, lty = 1, col = "black", type = "l",ylim = c(50,110), ylab = "Diastolic Blood Pressure", xlab = "Age",xaxt = "n"))
with(hatIPVhyp1[hatIPVhyp1$Sex == "Women" & hatIPVhyp1$LifeIPV == "1", ], lines(pAge,hat, lty = 1, col = "red"))
with(hatIPVhyp1[hatIPVhyp1$Sex == "Men" & hatIPVhyp1$LifeIPV == "0", ], lines(pAge,hat, lty = 2, col = "black"))
with(hatIPVhyp1[hatIPVhyp1$Sex == "Men" & hatIPVhyp1$LifeIPV == "1", ], lines(pAge,hat, lty = 2, col = "red"))
axis(1,at=c(-20,-10,0,10,20),labels = c("30","40","50","60","70"))
legend(-20,110, zQ(Women,Men), lty = 1:2, col = "black",cex=.75,bty="n")
text(-20,55,"IPV in red", adj = c(0,0), col = "red",cex=.75)
text(-20,50,"No IPV in black", adj = c(0,0), col = "black",cex=.75)
