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library(lme4) #glmer
library(readr)
SchoolData<-read_tsv("https://raw.githubusercontent.com/MixedModels/LearningMLwinN/master/tutorial.txt")
SchoolData$school<-factor(SchoolData$school)
SchoolData$girl<-factor(SchoolData$girl)
MOD.1 <- lmer(normexam ~ standlrt + (1|school), data = SchoolData, REML=F)
summary(MOD.1)
## Linear mixed model fit by maximum likelihood  ['lmerMod']
## Formula: normexam ~ standlrt + (1 | school)
##    Data: SchoolData
## 
##      AIC      BIC   logLik deviance df.resid 
##   9366.7   9391.9  -4679.4   9358.7     4055 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7192 -0.6290  0.0266  0.6850  3.2723 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  school   (Intercept) 0.09217  0.3036  
##  Residual             0.56594  0.7523  
## Number of obs: 4059, groups:  school, 65
## 
## Fixed effects:
##             Estimate Std. Error t value
## (Intercept) 0.001539   0.040031    0.04
## standlrt    0.563440   0.012469   45.19
## 
## Correlation of Fixed Effects:
##          (Intr)
## standlrt 0.008