Message-ID: <CAHLnndZg2VVUzUWG+52G7H9_HavDSBGHhck4xqi8yfBZ6PJmHA@mail.gmail.com>
Date: 2015-12-11T03:16:36Z
From: Li Li
Subject: random effect model
Hi all,
I have a very simple data set "data". Here both day and analysts are
considered as random.
I fit the mod1 and mod2 as below. The random effect in both models come out
to be zero and same results are returned from both models. It seems very
strange to me. Anyone have an explanation or suggestion?
Thanks. Hanna
> data
values day analyst
stat_d1p1 -0.06357455 1 1
stat_d1p2 -0.05564684 1 2
stat_d1p3 0.16145903 1 3
stat_d2p1 0.07763253 2 1
stat_d2p2 -0.02988389 2 2
stat_d2p3 -0.16899311 2 3
stat_d3p1 -0.13545138 3 1
stat_d3p2 -0.07537850 3 2
stat_d3p3 -0.01313345 3 3
> library(lme4)
> library(lmerTest)
> mod1 <- lmer(values ~ 1+(1|day),data=data)
> summary(mod1)
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: values ~ 1 + (1 | day)
Data: data
REML criterion at convergence: -11.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.3311 -0.4103 -0.2162 0.2019 1.9192
Random effects:
Groups Name Variance Std.Dev.
day (Intercept) 0.00000 0.0000
Residual 0.01034 0.1017
Number of obs: 9, groups: day, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.03366 0.03389 8.00000 -0.993 0.35
>
> mod2 <- lmer(values ~ 1+(1|analyst),data=data)
> summary(mod2)
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: values ~ 1 + (1 | analyst)
Data: data
REML criterion at convergence: -11.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.3311 -0.4103 -0.2162 0.2019 1.9192
Random effects:
Groups Name Variance Std.Dev.
analyst (Intercept) 0.00000 0.0000
Residual 0.01034 0.1017
Number of obs: 9, groups: analyst, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.03366 0.03389 8.00000 -0.993 0.35
>
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