[R-sig-ME] Standard Error of a coef. in a 2-level model vs. 2 OLS models
@|m@h@rme| @end|ng |rom gm@||@com
Mon Sep 14 01:37:38 CEST 2020
I have fit two ols models (ols1 & ols2) and an mixed-effects model (m1).
ols1 is a simple lm() model that ignores the second-level. ols2 is a simple
lm() model that ignores the first-level.
For `ols1` model, `sigma(ols1)^2` almost equals sum of variance components
in the `m1` model: 6.68 (bet.) + 39.15 (with.)
For `ols2` model, I wonder what does `sigma(ols2)^2` represents when
compared to the `m1` model?
Here is the fully reproducible code:
hsb <- read.csv('
hsb_ave <- hsb %>% group_by(sch.id) %>% mutate(math_ave = mean(math)) %>%
slice(1) # data that only considers grouping but ignores lower level
ols1 <- lm(math ~ sector, data = hsb)
m1 <- lmer(math ~ sector + (1|sch.id), data = hsb)
# `sigma(ols1)^2` almost equals 6.68 (bet.) + 39.15 (with.) from lmer
But if I fit another ols model that only considers the grouping structure
(ignoring lower level):
ols2 <- lm(math_ave ~ sector, data = hsb_ave)
Then what does `sigma(ols2)^2` should amount to when compared to the `m1`
[[alternative HTML version deleted]]
More information about the R-sig-mixed-models