[R-sig-ME] Statistical significance of random-effects (lme4 or others)

[Victor Oribamise]

Hey Simon,

You can check the lsmeans package in R, you can obtain p values for your
models using the package

Victor

On Sun, Sep 6, 2020 at 9:38 PM Simon Harmel <sim.harmel using gmail.com> wrote:

> 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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