Statistical significance of random-effects (lme4 or others)

Dear All,



Most MLM packages (e.g., HLM, SPSS, SAS, STATA) provide a p-value for the

variance components.



My understanding based on (


https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-significance-of-random-effects
)

is that this is not possible to achieve in R, right?



If not, for my 4 models below, I assume I need to compare, using anova(),

each model against its OLS equivalent to obtain a likelihood ratio test

p-value for each model's variance component, correct?



hsb <- read.csv('

https://raw.githubusercontent.com/rnorouzian/e/master/hsb.csv')



library(lme4)

m1 <- lmer(math ~ 1 + (1|sch.id), data = hsb)

m2 <- lmer(math ~ meanses + (1|sch.id), data = hsb)

m3 <- lmer(math ~ ses + (ses | sch.id), data = hsb)

m4 <- lmer(math~ ses * meanses + (ses | sch.id ), data = hsb)



ols1 <- lm(math ~ 1, data = hsb)

ols2 <- lm(math ~ meanses, data = hsb)

ols3 <- lm(math ~ ses, data = hsb)

ols4 <- lm(math ~ ses * meanses, data = hsb)



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