Data
pacman::p_load(mlmRev, tidyverse, lme4, nlme)
dta_1 <- read.table("C:/Users/ASUS/Desktop/data/demo1.txt", header = T)
dta_2 <- read.table("C:/Users/ASUS/Desktop/data/demo2.txt", header = T)
Grand Means of score for dat_1 &_2
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
Null Model
M1(dta_1)
summary(m1 <- 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
M2(dta_2)
summary(m2 <- 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
ICC
dta_1
library(ICC)
ICCbare(School, Score, dta_1)
## Warning in ICCbare(School, Score, dta_1): 'x' has been coerced to a factor
## [1] 0.9969128
dta_2
ICCbare(School, Score, dta_2)
## Warning in ICCbare(School, Score, dta_2): 'x' has been coerced to a factor
## [1] 0.1008126