1 Data Management

dta3<-read.table("C:/Users/ASUS/Desktop/data/alcohol_use.txt",header=T)

head(dta3)
##   sid coa sex age14  alcuse    peer    cpeer  ccoa
## 1   1   1   0     0 1.73205 1.26491  0.24691 0.549
## 2   1   1   0     1 2.00000 1.26491  0.24691 0.549
## 3   1   1   0     2 2.00000 1.26491  0.24691 0.549
## 4   2   1   1     0 0.00000 0.89443 -0.12357 0.549
## 5   2   1   1     1 0.00000 0.89443 -0.12357 0.549
## 6   2   1   1     2 1.00000 0.89443 -0.12357 0.549
dta3$sid <- factor(dta3$sid)
dta3$coa <- factor(dta3$coa)
dta3$sex <- factor(dta3$sex, 0:1, labels = c("F", "M"))

2 Analysis n Output

library(lme4)
## Loading required package: Matrix
m0 <- lmer(alcuse ~ coa + peer + age14 + peer*age14 + ( 1 |sid), dta3, REML=FALSE)
summary(m0)
## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: alcuse ~ coa + peer + age14 + peer * age14 + (1 | sid)
##    Data: dta3
## 
##      AIC      BIC   logLik deviance df.resid 
##    620.5    645.0   -303.2    606.5      239 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3210 -0.6694 -0.0541  0.5612  2.6356 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  sid      (Intercept) 0.3214   0.5669  
##  Residual             0.4765   0.6903  
## Number of obs: 246, groups:  sid, 82
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept) -0.31177    0.16970  -1.837
## coa1         0.56513    0.15585   3.626
## peer         0.69586    0.13028   5.341
## age14        0.42469    0.09289   4.572
## peer:age14  -0.15138    0.07435  -2.036
## 
## Correlation of Fixed Effects:
##            (Intr) coa1   peer   age14 
## coa1       -0.310                     
## peer       -0.726 -0.133              
## age14      -0.547  0.000  0.465       
## peer:age14  0.446  0.000 -0.571 -0.814