save(exdata,file ="/Users/meganwilliams/Desktop/Research/Cannabis and Cardiovascular Disease/exdata.rdata")
model1 = lm(PhysMeanSBPsitimp~MedHxMarijStatusCurrent + Sex + Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC + MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC + CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP + PhysBMI, data = exdata)
summary(model1)
##
## Call:
## lm(formula = PhysMeanSBPsitimp ~ MedHxMarijStatusCurrent + Sex +
## Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC +
## MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC +
## CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP +
## PhysBMI, data = exdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.39 -10.67 -1.62 9.53 78.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 80.6164 3.3598 23.99 < 2e-16
## MedHxMarijStatusCurrent1 3.7480 1.0026 3.74 0.00019
## SexMen 3.2578 0.6542 4.98 6.8e-07
## RaceAfrAm 2.9667 0.6464 4.59 4.6e-06
## Age0 0.5060 0.0365 13.88 < 2e-16
## HseHldEducation -0.2025 0.1095 -1.85 0.06449
## PovStatBelow 1.7689 0.6657 2.66 0.00792
## MedHxCigaretteStatusNFC -0.7511 0.4229 -1.78 0.07579
## MedHxAlcStatusNFC 0.7366 0.4048 1.82 0.06889
## MedHxCokeStatusNFC 0.6370 0.6675 0.95 0.34000
## MedHxOpiateStatusNFC -2.7310 0.8014 -3.41 0.00066
## CESimp 0.0253 0.0288 0.88 0.37999
## DMMclusterdich1 -0.5289 0.7413 -0.71 0.47556
## CVDclusterdich1 -2.4808 1.4135 -1.76 0.07935
## MedHxMedsBP 5.0348 0.7537 6.68 2.9e-11
## PhysBMI 0.4835 0.0430 11.24 < 2e-16
##
## Residual standard error: 16.1 on 2712 degrees of freedom
## (74 observations deleted due to missingness)
## Multiple R-squared: 0.183, Adjusted R-squared: 0.179
## F-statistic: 40.6 on 15 and 2712 DF, p-value: <2e-16
model2 = lm(PhysMeanPPsitimp~MedHxMarijStatusCurrent + Sex + Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC + MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC + CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP + PhysBMI, data = exdata)
summary(model2)
##
## Call:
## lm(formula = PhysMeanPPsitimp ~ MedHxMarijStatusCurrent + Sex +
## Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC +
## MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC +
## CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP +
## PhysBMI, data = exdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.55 -7.58 -1.42 6.11 53.64
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.8634 2.4141 6.57 6.0e-11
## MedHxMarijStatusCurrent1 2.8602 0.7204 3.97 7.4e-05
## SexMen -0.2257 0.4701 -0.48 0.6311
## RaceAfrAm 2.1462 0.4644 4.62 4.0e-06
## Age0 0.4679 0.0262 17.86 < 2e-16
## HseHldEducation -0.0819 0.0787 -1.04 0.2981
## PovStatBelow 0.4041 0.4783 0.84 0.3982
## MedHxCigaretteStatusNFC 0.1664 0.3038 0.55 0.5839
## MedHxAlcStatusNFC 0.3710 0.2909 1.28 0.2022
## MedHxCokeStatusNFC -0.5471 0.4796 -1.14 0.2541
## MedHxOpiateStatusNFC -1.3908 0.5758 -2.42 0.0158
## CESimp 0.0132 0.0207 0.64 0.5234
## DMMclusterdich1 1.2212 0.5326 2.29 0.0219
## CVDclusterdich1 0.3305 1.0156 0.33 0.7449
## MedHxMedsBP 1.5706 0.5416 2.90 0.0038
## PhysBMI 0.2916 0.0309 9.43 < 2e-16
##
## Residual standard error: 11.6 on 2712 degrees of freedom
## (74 observations deleted due to missingness)
## Multiple R-squared: 0.193, Adjusted R-squared: 0.189
## F-statistic: 43.3 on 15 and 2712 DF, p-value: <2e-16
model3 = lm(PhysMeanDBPsitimp~MedHxMarijStatusCurrent + Sex + Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC + MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC + CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP + PhysBMI, data = exdata)
summary(model3)
##
## Call:
## lm(formula = PhysMeanDBPsitimp ~ MedHxMarijStatusCurrent + Sex +
## Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC +
## MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC +
## CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP +
## PhysBMI, data = exdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -32.76 -7.64 -0.14 6.99 40.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 64.7530 2.1795 29.71 < 2e-16
## MedHxMarijStatusCurrent1 0.8878 0.6504 1.37 0.17232
## SexMen 3.4835 0.4244 8.21 3.4e-16
## RaceAfrAm 0.8205 0.4193 1.96 0.05047
## Age0 0.0381 0.0236 1.61 0.10758
## HseHldEducation -0.1207 0.0710 -1.70 0.08953
## PovStatBelow 1.3647 0.4318 3.16 0.00159
## MedHxCigaretteStatusNFC -0.9176 0.2743 -3.35 0.00083
## MedHxAlcStatusNFC 0.3656 0.2626 1.39 0.16392
## MedHxCokeStatusNFC 1.1841 0.4330 2.73 0.00629
## MedHxOpiateStatusNFC -1.3402 0.5198 -2.58 0.00999
## CESimp 0.0121 0.0187 0.65 0.51788
## DMMclusterdich1 -1.7502 0.4809 -3.64 0.00028
## CVDclusterdich1 -2.8113 0.9169 -3.07 0.00219
## MedHxMedsBP 3.4642 0.4889 7.09 1.8e-12
## PhysBMI 0.1918 0.0279 6.87 7.7e-12
##
## Residual standard error: 10.4 on 2712 degrees of freedom
## (74 observations deleted due to missingness)
## Multiple R-squared: 0.0794, Adjusted R-squared: 0.0743
## F-statistic: 15.6 on 15 and 2712 DF, p-value: <2e-16
# Results from a linear multiple regression analysis revealed significant main effects for systolic blood pressure (b = 3.75, SE = 1.00, p < .001) and pulse pressure (b = - 2.86,SE = 2.25, p < .001). These results suggest that current marijuana use may be associated with an increase in systolic blood pressure and pulse pressure in adults.
library(effects) mod <- lm(PhysMeanPPsitimp~MedHxMarijStatusCurrent + Sex + Race + Age0 + HseHldEducation + PovStat + MedHxCigaretteStatusNFC + MedHxAlcStatusNFC + MedHxCokeStatusNFC + MedHxOpiateStatusNFC + CESimp + DMMclusterdich + CVDclusterdich + MedHxMedsBP + PhysBMI, data = exdata)
eff.mod <- allEffects(mod, xlevels=list(MedHxMarijStatusCurrent=0:1)) plot(eff.mod,'MedHxMarijStatusCurrent', ylab="Systolic Blood Pressure", type="h",grid=TRUE, rotx=90, lty=0)