library(foreign) library(lme4)
Table 2.1 on page 17 using the popular dataset.
Part 1: Intercept only.
popdata<-read.dta("https://stats.idre.ucla.edu/stat/stata/examples/mlm_ma_hox/popular.dta")
m2.1.1<-lmer(popular ~ (1|school), popdata)
summary(m2.1.1)
Linear mixed model fit by REML
Formula: popular ~ (1 | school)
Data: popdata
AIC BIC logLik deviance REMLdev
5122 5138 -2558 5113 5116
Random effects:
Groups Name Variance Std.Dev.
school (Intercept) 0.87981 0.93798
Residual 0.63868 0.79917
Number of obs: 2000, groups: school, 100
Fixed effects:
Estimate Std. Error t value
(Intercept) 5.3076 0.0955 55.58
Part 2: intercept plus pupil level variables
m2.1.2<-lmer(popular ~ sex + texp +(1 + sex|school), popdata)
summary(m2.1.2)
Linear mixed model fit by REML
Formula: popular ~ sex + texp + (1 + sex | school)
Data: popdata
AIC BIC logLik deviance REMLdev
4290 4329 -2138 4261 4276
Random effects:
Groups Name Variance Std.Dev. Corr
school (Intercept) 0.41158 0.64155
sexgirl 0.27329 0.52278 0.062
Residual 0.39248 0.62648
Number of obs: 2000, groups: school, 100
Fixed effects:
Estimate Std. Error t value
(Intercept) 3.34001 0.16079 20.77
sexgirl 0.84315 0.05969 14.13
texp 0.10835 0.01022 10.61
Correlation of Fixed Effects:
(Intr) sexgrl
sexgirl -0.020
texp -0.908 0.000
Table 2.2 on page 20, part 2 (first part is part 2 of Table 2.1)
m2.2.2
Linear mixed model fit by REML
Formula: popular ~ texp + sex + texp * sex + (1 + sex | school)
Data: popdata
AIC BIC logLik deviance REMLdev
4284 4329 -2134 4246 4268
Random effects:
Groups Name Variance Std.Dev. Corr
school (Intercept) 0.41198 0.64186
sexgirl 0.22641 0.47582 0.077
Residual 0.39241 0.62643
Number of obs: 2000, groups: school, 100
Fixed effects:
Estimate Std. Error t value
(Intercept) 3.313521 0.161017 20.579
texp 0.110235 0.010232 10.773
sexgirl 1.329594 0.133052 9.993
texp:sexgirl -0.034035 0.008457 -4.024
Correlation of Fixed Effects:
(Intr) texp sexgrl
texp -0.909
sexgirl -0.046 0.042
texp:sexgrl 0.042 -0.046 -0.908
Table 2.3 on page 21, part 2 (first part is part 2 of Table 2.2)
attach(popdata)
sex01 <- (sex=="girl")
zsex <- (sex01-mean(sex01))/sd(sex01)
ztexp <- (texp - mean(texp))/sd(texp)
zpop <- (popular - mean(popular))/sd(popular)
m2.3.2 <-lmer(zpop ~ ztexp + zsex + (1 + zsex | school))
summary(m2.3.2)
Linear mixed model fit by REML
Formula: zpop ~ ztexp + zsex + (1 + zsex | school)
AIC BIC logLik deviance REMLdev
3474 3513 -1730 3446 3460
Random effects:
Groups Name Variance Std.Dev. Corr
school (Intercept) 0.330522 0.57491
zsex 0.045453 0.21320 0.418
Residual 0.261151 0.51103
Number of obs: 2000, groups: school, 100
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.009779 0.058669 -0.167
ztexp 0.579071 0.054596 10.606
zsex 0.343852 0.024341 14.126
Correlation of Fixed Effects:
(Intr) ztexp
ztexp -0.005
zsex 0.359 0.000
Figure 2.1 on page 23.
linres
Figure 2.2 on page 24.
linp
Figure 2.3 on page 24.
re plot(x = smeans, y = randslopes, pch = 'x', xlab = "Predicted Values", ylab = "(sex)") abline(a = 0, b = 0)
Figure 2.4 on page 25–we have skipped this figure for now.
Figure 2.6 on page 28–we have skipped this figure for now.