# [R] How to test for significance of random effects?

From: Jon Olav Vik <jonovik_at_start.no>
Date: Sat 06 May 2006 - 22:21:09 EST

I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either, so I would greatly appreciate the help of this list.

Pinheiro & Bates (2000:83) state that random-effect terms can be tested based on their likelihood ratio, if both models have the same fixed-effects structure and both are estimated with REML (I must admit I do not know exactly what REML is, although I do understand the concept of ML).

The examples in Pinheiro & Bates 2000 deal with simple vs. complicated random-effects structures, both fitted with lme and method="REML". However, to fit a model without random effects I must use lm() or glm(). Is there a way to tell these functions to use REML? I see that lme() can use ML, but Pinheiro&Bates (2000) advised against this for some reason.

lme() does provide a confidence interval for the between-group variance, but this is constructed so as to never include zero (I guess the interval is as narrow as possible on log scale, or something). I would be grateful if anyone could tell me how to test for zero variance between groups.

If lm1 and lme1 are fitted with lm() and lme() respectively, then anova(lm1,lme1) gives an error, whereas anova(lme1,lm1) gives an answer which looks reasonable enough.

The command logLik() can retrieve either restricted or ordinary log-likelihoods from a fitted model object, but the likelihoods are then evaluated at the fitted parameter estimates. I guess these estimates differ from if the model were estimated using REML?

My actual application is a logistic regression with two continuous and one binary predictor, in which I would like to avoid the complications of using generalized linear mixed models. Here is a simpler example, which is rather trivial but illustrates the general question:

Example (run in R 2.2.1):

```library(nlme)
summary(lm1 <- lm(travel~1,data=Rail)) # no random effect
summary(lme1 <- lme(fixed=travel~1,random=~1|Rail,data=Rail)) # random
```
effect
intervals(lme1) # confidence for random effect anova(lm1,lme1)
```## Outputs warning message:
```

# models with response "NULL" removed because
# response differs from model 1 in: anova.lmlist(object, ...)
anova(lme1,lm1)
```## Output: Can I trust this?

#      Model df      AIC      BIC    logLik   Test  L.Ratio p-value

# lme1     1  3 128.1770 130.6766 -61.08850
# lm1      2  2 162.6815 164.3479 -79.34075 1 vs 2 36.50451  <.0001
## Various log likelihoods:
logLik(lm1,REML=FALSE)
logLik(lm1,REML=TRUE)
logLik(lme1,REML=FALSE)
```

logLik(lme1,REML=TRUE)

Any help is highly appreciated.

Best regards,
Jon Olav Vik

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