[R-sig-ME] Statistical significance of random-effects (lme4 or others)
Simon Harmel
@|m@h@rme| @end|ng |rom gm@||@com
Mon Sep 7 05:15:26 CEST 2020
Hi Victor,
Thanks for your response. First, as far as I know "lsmeans" has now become
"emmeans".
Second, all my data and code is 100% reproducible, would you please let me
know how can I possibly obtain the p-value for the random-effects' variance
components in any of the 4 models I showed in my original question?
Thanks, Simon
On Sun, Sep 6, 2020 at 9:42 PM Victor Oribamise <victor.oribamise using gmail.com>
wrote:
> 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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>>
>>
>>
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>>
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>>
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>>
>>
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