pacman::p_load(mlmRev, tidyverse, lme4, nlme, irr)
Input Data
dta_1 <- read.table("C:/Users/HANK/Desktop/HOMEWORK/demo1.txt", header = T)
dta_2 <- read.table("C:/Users/HANK/Desktop/HOMEWORK/demo2.txt", header = T)
# mean
summary(dta_1$Score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 51.00 55.00 62.11 93.00 97.00
summary(dta_2$Score)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 51.00 53.00 57.44 91.00 97.00
dta_1 <- dta_1 %>%
group_by(School) %>%
mutate( ave_Score = mean(Score),
c_Score = ave_Score - Score)
dta_2 <- dta_2 %>%
group_by(School) %>%
mutate( ave_Score = mean(Score),
c_Score = ave_Score - Score)
summary(m0_1 <- lmer(Score ~ (1|School), data = dta_1))
## Linear mixed model fit by REML ['lmerMod']
## Formula: Score ~ (1 | School)
## Data: dta_1
##
## REML criterion at convergence: 51.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3280 -0.4745 0.0000 0.4608 1.3553
##
## Random effects:
## Groups Name Variance Std.Dev.
## School (Intercept) 1679 40.976
## Residual 5 2.236
## Number of obs: 9, groups: School, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 53.01 23.67 2.24
summary(m0_2 <- lmer(Score ~ (1|School), data = dta_2))
## Linear mixed model fit by REML ['lmerMod']
## Formula: Score ~ (1 | School)
## Data: dta_2
##
## REML criterion at convergence: 80.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.36734 -0.34286 -0.02776 1.07153 1.13999
##
## Random effects:
## Groups Name Variance Std.Dev.
## School (Intercept) 104.2 10.21
## Residual 967.8 31.11
## Number of obs: 9, groups: School, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 56.36 12.01 4.692
sjPlot::tab_model(m0_1, m0_2, show.p=FALSE, show.r2=FALSE, show.obs=FALSE, show.ngroups=FALSE, show.se=TRUE, show.ci=FALSE)
|
|
Score
|
Score
|
|
Predictors
|
Estimates
|
std. Error
|
Estimates
|
std. Error
|
|
(Intercept)
|
53.01
|
23.67
|
56.36
|
12.01
|
|
Random Effects
|
|
σ2
|
5.00
|
967.76
|
|
τ00
|
1679.06 School
|
104.17 School
|
|
ICC
|
1.00
|
0.10
|
summary(m0_1_1 <- lm(Score ~ School:c_Score, data = dta_1))
##
## Call:
## lm(formula = Score ~ School:c_Score, data = dta_1)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9
## -50.111 -50.111 -9.111 -9.111 -9.111 31.889 31.889 31.889 31.889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.111 14.406 4.311 0.00763 **
## SchoolS1:c_Score -1.000 30.560 -0.033 0.97516
## SchoolS2:c_Score -1.000 15.280 -0.065 0.95036
## SchoolS3:c_Score -1.000 9.664 -0.103 0.92161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.22 on 5 degrees of freedom
## Multiple R-squared: 0.003202, Adjusted R-squared: -0.5949
## F-statistic: 0.005354 on 3 and 5 DF, p-value: 0.9994
summary(m0_2_2 <- lm(Score ~ School:c_Score, data = dta_2))
##
## Call:
## lm(formula = Score ~ School:c_Score, data = dta_2)
##
## Residuals:
## 1 2 3 4 5 6 7 8 9
## -26.444 -26.444 -4.444 -4.444 -4.444 16.556 16.556 16.556 16.556
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 57.4444 7.5340 7.625 0.000617 ***
## SchoolS1:c_Score -1.0000 0.7991 -1.251 0.266142
## SchoolS2:c_Score -1.0000 0.4094 -2.443 0.058462 .
## SchoolS3:c_Score -1.0000 0.5131 -1.949 0.108844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.6 on 5 degrees of freedom
## Multiple R-squared: 0.6938, Adjusted R-squared: 0.5101
## F-statistic: 3.777 on 3 and 5 DF, p-value: 0.09333
sjPlot::tab_model(m0_1_1, m0_2_2, show.p=FALSE, show.r2=FALSE, show.obs=FALSE, show.ngroups=FALSE, show.se=TRUE, show.ci=FALSE)
|
|
Score
|
Score
|
|
Predictors
|
Estimates
|
std. Error
|
Estimates
|
std. Error
|
|
(Intercept)
|
62.11
|
14.41
|
57.44
|
7.53
|
|
School [S1] : c_Score
|
-1.00
|
30.56
|
-1.00
|
0.80
|
|
School [S2] : c_Score
|
-1.00
|
15.28
|
-1.00
|
0.41
|
|
School [S3] : c_Score
|
-1.00
|
9.66
|
-1.00
|
0.51
|
The end