dat = read.csv("~/Desktop/R GS Tuấn 2020/TDTU Datasets for 2020 Workshop/imm10.csv", header = T)
head(dat)
## school id ses meanses homework white parented public ratio percmin math
## 1 7472 3 -0.13 -0.4826087 1 1 2 1 19 0 48
## 2 7472 8 -0.39 -0.4826087 0 1 2 1 19 0 48
## 3 7472 13 -0.80 -0.4826087 0 1 2 1 19 0 53
## 4 7472 17 -0.72 -0.4826087 1 1 2 1 19 0 42
## 5 7472 27 -0.74 -0.4826087 2 1 2 1 19 0 43
## 6 7472 28 -0.58 -0.4826087 1 1 2 1 19 0 57
## sex race sctype cstr scsize urban region schnum
## 1 2 4 1 2 3 2 2 1
## 2 1 4 1 2 3 2 2 1
## 3 1 4 1 2 3 2 2 1
## 4 1 4 1 2 3 2 2 1
## 5 2 4 1 2 3 2 2 1
## 6 2 4 1 2 3 2 2 1
library(lme4)
## Loading required package: Matrix
m = lmer(math ~ homework + (1 | school), data=dat)
library(jtools)
m = lmer(math ~ homework + (1 | school), data=dat)
summ(m)
## MODEL INFO:
## Observations: 260
## Dependent Variable: math
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 1847.95, BIC = 1862.19
## Pseudo-R² (fixed effects) = 0.12
## Pseudo-R² (total) = 0.36
##
## FIXED EFFECTS:
## ---------------------------------------------------------
## Est. S.E. t val. d.f. p
## ----------------- ------- ------ -------- -------- ------
## (Intercept) 44.98 1.80 24.95 12.67 0.00
## homework 2.21 0.38 5.82 257.15 0.00
## ---------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## ------------------------------------
## Group Parameter Std. Dev.
## ---------- ------------- -----------
## school (Intercept) 5.02
## Residual 8.03
## ------------------------------------
##
## Grouping variables:
## --------------------------
## Group # groups ICC
## -------- ---------- ------
## school 10 0.28
## --------------------------
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