Data Management
library(WWGbook)
data(classroom)
dta<-classroom
head(dta)
## sex minority mathkind mathgain ses yearstea mathknow housepov mathprep
## 1 1 1 448 32 0.46 1 NA 0.082 2.00
## 2 0 1 460 109 -0.27 1 NA 0.082 2.00
## 3 1 1 511 56 -0.03 1 NA 0.082 2.00
## 4 0 1 449 83 -0.38 2 -0.11 0.082 3.25
## 5 0 1 425 53 -0.03 2 -0.11 0.082 3.25
## 6 1 1 450 65 0.76 2 -0.11 0.082 3.25
## classid schoolid childid
## 1 160 1 1
## 2 160 1 2
## 3 160 1 3
## 4 217 1 4
## 5 217 1 5
## 6 217 1 6
names(dta) <- c("SEX", "MINORITY", "MATHkind", "MATHgain","SES", "YEARS","MATHknow","HOUSE","MATHprep","CLASS","SCHOOL","CHILD")
dta1<-dta[, c(1,2,3,4,5,10,11,12)]
head(dta1)
## SEX MINORITY MATHkind MATHgain SES CLASS SCHOOL CHILD
## 1 1 1 448 32 0.46 160 1 1
## 2 0 1 460 109 -0.27 160 1 2
## 3 1 1 511 56 -0.03 160 1 3
## 4 0 1 449 83 -0.38 217 1 4
## 5 0 1 425 53 -0.03 217 1 5
## 6 1 1 450 65 0.76 217 1 6
library (nlme)
library(lme4)
## Loading required package: Matrix
##
## Attaching package: 'lme4'
## The following object is masked from 'package:nlme':
##
## lmList
M0
# mathgainijk(j) = b0j + b1k(j) + εijk(j); i student, j school, k class
summary(m0 <-lmer(MATHgain ~ (1|SCHOOL) + (1 |CLASS), data = dta1))
## Linear mixed model fit by REML ['lmerMod']
## Formula: MATHgain ~ (1 | SCHOOL) + (1 | CLASS)
## Data: dta1
##
## REML criterion at convergence: 11768.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6441 -0.5984 -0.0336 0.5334 5.6335
##
## Random effects:
## Groups Name Variance Std.Dev.
## CLASS (Intercept) 99.22 9.961
## SCHOOL (Intercept) 77.50 8.804
## Residual 1028.23 32.066
## Number of obs: 1190, groups: CLASS, 312; SCHOOL, 107
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 57.427 1.443 39.79
sjPlot::tab_model(m0,
show.intercept = TRUE,
show.est = TRUE,
show.se = TRUE,
show.df = TRUE,
show.stat = TRUE,
show.p = TRUE,
show.aic = TRUE,
show.aicc = TRUE)
|
|
MATHgain
|
|
Predictors
|
Estimates
|
std. Error
|
CI
|
Statistic
|
p
|
df
|
|
(Intercept)
|
57.43
|
1.44
|
54.60 – 60.26
|
39.79
|
<0.001
|
1186.00
|
|
Random Effects
|
|
σ2
|
1028.23
|
|
τ00 CLASS
|
99.22
|
|
τ00 SCHOOL
|
77.50
|
|
ICC
|
0.15
|
|
N SCHOOL
|
107
|
|
N CLASS
|
312
|
|
Observations
|
1190
|
|
Marginal R2 / Conditional R2
|
0.000 / 0.147
|
|
AIC
|
11776.799
|
M1
# mathgainijk(j) = b0j + b1k(j) + β2 × mathkindijk(j) + β3 × sexijk(j) + β4 × minorityijk(j) + β5 × sesijk(j) + εijk(j)
m1 <- lmer( MATHgain ~ (1|SCHOOL) + (1 |CLASS) + MATHkind + SEX + MINORITY + SES, data=dta1)
summary(m1)
## Linear mixed model fit by REML ['lmerMod']
## Formula: MATHgain ~ (1 | SCHOOL) + (1 | CLASS) + MATHkind + SEX + MINORITY +
## SES
## Data: dta1
##
## REML criterion at convergence: 11385.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8257 -0.6110 -0.0337 0.5538 4.2678
##
## Random effects:
## Groups Name Variance Std.Dev.
## CLASS (Intercept) 83.28 9.126
## SCHOOL (Intercept) 75.20 8.672
## Residual 734.57 27.103
## Number of obs: 1190, groups: CLASS, 312; SCHOOL, 107
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 282.79033 10.85323 26.056
## MATHkind -0.46980 0.02227 -21.100
## SEX -1.25119 1.65773 -0.755
## MINORITY -8.26213 2.34011 -3.531
## SES 5.34638 1.24109 4.308
##
## Correlation of Fixed Effects:
## (Intr) MATHkn SEX MINORI
## MATHkind -0.978
## SEX -0.044 -0.031
## MINORITY -0.307 0.163 -0.018
## SES 0.140 -0.168 0.019 0.163
sjPlot::tab_model(m1,
show.intercept = TRUE,
show.est = TRUE,
show.se = TRUE,
show.df = TRUE,
show.stat = TRUE,
show.p = TRUE,
show.aic = TRUE,
show.aicc = TRUE)
|
|
MATHgain
|
|
Predictors
|
Estimates
|
std. Error
|
CI
|
Statistic
|
p
|
df
|
|
(Intercept)
|
282.79
|
10.85
|
261.52 – 304.06
|
26.06
|
<0.001
|
1182.00
|
|
MATHkind
|
-0.47
|
0.02
|
-0.51 – -0.43
|
-21.10
|
<0.001
|
1182.00
|
|
SEX
|
-1.25
|
1.66
|
-4.50 – 2.00
|
-0.75
|
0.450
|
1182.00
|
|
MINORITY
|
-8.26
|
2.34
|
-12.85 – -3.68
|
-3.53
|
<0.001
|
1182.00
|
|
SES
|
5.35
|
1.24
|
2.91 – 7.78
|
4.31
|
<0.001
|
1182.00
|
|
Random Effects
|
|
σ2
|
734.57
|
|
τ00 CLASS
|
83.28
|
|
τ00 SCHOOL
|
75.20
|
|
ICC
|
0.18
|
|
N SCHOOL
|
107
|
|
N CLASS
|
312
|
|
Observations
|
1190
|
|
Marginal R2 / Conditional R2
|
0.274 / 0.403
|
|
AIC
|
11401.925
|