10.3 Single Predictor Model

10.3.1 Read in Data

We can read in the data and create a mean-centered version of income.

ferraro2016 <- read.csv("data/ferraro2016.csv")
ferraro2016$income_star <- as.numeric(scale(ferraro2016$catincome, scale = FALSE))

model4 <- glm(
  formula = morbidityw1 ~ 1 + health + age + smoke_dose + heavydr2 + obese + fampos + friendpos , 
  family = quasipoisson(link=log), 
  data = ferraro2016,
  na.action = na.exclude
)

summary(model4)

## 
## Call:
## glm(formula = morbidityw1 ~ 1 + health + age + smoke_dose + heavydr2 + 
##     obese + fampos + friendpos, family = quasipoisson(link = log), 
##     data = ferraro2016, na.action = na.exclude)
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.5801319  0.1355025   4.281 1.92e-05 ***
## health       0.2219320  0.0425419   5.217 1.96e-07 ***
## age          0.0148076  0.0015510   9.547  < 2e-16 ***
## smoke_dose   0.0048612  0.0008919   5.451 5.46e-08 ***
## heavydr2     0.0743414  0.0444758   1.672   0.0947 .  
## obese        0.2736708  0.0402436   6.800 1.27e-11 ***
## fampos      -0.1310295  0.0316201  -4.144 3.52e-05 ***
## friendpos   -0.0329860  0.0288534  -1.143   0.2530    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 2.560173)
## 
##     Null deviance: 7443.0  on 2769  degrees of freedom
## Residual deviance: 6739.7  on 2762  degrees of freedom
##   (252 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 5