Age * IncidIPV * Sex Interaction Plot (sBP)
Systolic Blood Pressure
modelsBP = lmer(adjSBP~Age * IncidIPV * Sex * Race + BMIcat + PovStat + (Age|HNDid) + (1|subclass),IncidIPVbp)
(st = step(modelsBP))
Final Model by backwards elimination
modelsBP = lmer(formula = adjSBP ~ Age + IncidIPV + Sex + BMIcat + (1 | 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 + (1 | 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.
## HNDid (Intercept) 150.8 12.28
## subclass (Intercept) 15.8 3.98
## Residual 129.0 11.36
## Number of obs: 138, groups: HNDid, 71; subclass, 46
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 125.425 4.734 81.200 26.49 <2e-16
## Age 1.640 0.440 109.400 3.73 0.0003
## IncidIPV1 -1.280 7.789 87.500 -0.16 0.8699
## SexMen -13.340 5.985 83.600 -2.23 0.0285
## BMIcatObese 8.912 3.563 109.300 2.50 0.0139
## Age:IncidIPV1 -0.837 0.801 113.400 -1.04 0.2985
## Age:SexMen -1.564 0.613 111.600 -2.55 0.0122
## IncidIPV1:SexMen 21.846 11.084 88.200 1.97 0.0519
## Age:IncidIPV1:SexMen 2.532 1.114 115.400 2.27 0.0248
##
## Correlation of Fixed Effects:
## (Intr) Age InIPV1 SexMen BMIctO Ag:IIPV1 Ag:SxM IIPV1:
## Age 0.520
## IncidIPV1 -0.377 -0.333
## SexMen -0.550 -0.425 0.394
## BMIcatObese -0.465 0.064 -0.180 -0.112
## Ag:IncdIPV1 -0.231 -0.537 0.745 0.251 -0.125
## Age:SexMen -0.337 -0.697 0.255 0.677 -0.088 0.403
## IncdIPV1:SM 0.234 0.239 -0.730 -0.568 0.204 -0.542 -0.387
## Ag:IIPV1:SM 0.144 0.389 -0.555 -0.396 0.139 -0.739 -0.574 0.702
Age * IncidIPV * Sex Interaction Plot (sBP)
Diastolic Blood Pressure
modeldBP = lmer(adjDBP~(Age * IncidIPV * Sex * Race + PovStat + BMIcat) + (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,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
Age * IncidIPV * Sex Interaction Plot (dBP)
