Load neighborhood data and create complete cases variables

#DATA$complete = complete.cases(DATA)
#Neigh=DATA
#save(Neigh,file ="/Users/meganwilliams/Desktop/Neighborhood and Cognitive Function/Data/Neigh.Rda")
load(file ="/Users/meganwilliams/Desktop/Neighborhood and Cognitive Function/Data/Neigh.Rda")
library(lme4)

## Loading required package: Matrix

Run mixed effects logistic regression with complete cases as the outcome

gm2 <- glmer(complete~age + Race + PovStat + Sex +(1|HNDid),data=Neigh,family=binomial)
summary(gm2)

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: complete ~ age + Race + PovStat + Sex + (1 | HNDid)
##    Data: Neigh
## 
##      AIC      BIC   logLik deviance df.resid 
##   6807.3   6846.3  -3397.6   6795.3     4930 
## 
## Scaled residuals: 
##        Min         1Q     Median         3Q        Max 
## -1.2386740 -0.9419528  0.7494828  0.9090893  1.1656016 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. 
##  HNDid  (Intercept) 0.2841336 0.5330418
## Number of obs: 4936, groups:  HNDid, 2468
## 
## Fixed effects:
##                  Estimate   Std. Error  z value   Pr(>|z|)
## (Intercept)   0.190742778  0.061714559  3.09073  0.0019967
## age          -0.014942545  0.003405067 -4.38833 1.1423e-05
## RaceAfrAm    -0.198309405  0.065728252 -3.01711  0.0025520
## PovStatBelow  0.011653180  0.065132952  0.17891  0.8580054
## SexMen       -0.043967102  0.063799710 -0.68914  0.4907335
## 
## Correlation of Fixed Effects:
##             (Intr) age    RcAfrA PvSttB
## age         -0.072                     
## RaceAfrAm   -0.588  0.018              
## PovStatBelw -0.348  0.046 -0.161       
## SexMen      -0.459  0.010 -0.006  0.058