CODEWITHSUNDEEP
rlme4mixed-modelsnlme
user3585829
user3585829

Reputation: 965

Mixed Modelling - Different Results between lme and lmer functions

I am currently working through Andy Field's book, Discovering Statistics Using R. Chapter 14 is on Mixed Modelling and he uses the lme function from the nlme package.

The model he creates, using speed dating data, is such:

speedDateModel <- lme(dateRating ~ looks + personality +
                 gender + looks:gender + personality:gender + 
                 looks:personality,
    random = ~1|participant/looks/personality)

I tried to recreate a similar model using the lmer function from the lme4 package; however, my results are different. I thought I had the proper syntax, but maybe not?

speedDateModel.2 <- lmer(dateRating ~ looks + personality + gender + 
              looks:gender + personality:gender + 
              (1|participant) + (1|looks) + (1|personality), 
              data = speedData, REML = FALSE)

Also, when I run the coefficients of these models I notice that it only produces random intercepts for each participant. I was trying to then create a model that produces both random intercepts and slopes. I can't seem to get the syntax correct for either function to do this. Any help would be greatly appreciated.

Upvotes: 3

Views: 8032

Answers (1)

Ben Bolker
Ben Bolker

Reputation: 226731

The only difference between the lme and the corresponding lmer formula should be that the random and fixed components are aggregated into a single formula:

dateRating ~ looks + personality +
             gender + looks:gender + personality:gender + 
             looks:personality+ (1|participant/looks/personality)

using (1|participant) + (1|looks) + (1|personality) is only equivalent if looks and personality have unique values at each nested level.

It's not clear what continuous variable you want to define your slopes: if you have a continuous variable x and groups g, then (x|g) or equivalently (1+x|g) will give you a random-slopes model (x should also be included in the fixed-effects part of the model, i.e. the full formula should be y~x+(x|g) ...)

update: I got the data, or rather a script file that allows one to reconstruct the data, from here. Field makes a common mistake in his book, which I have made several times in the past: since there is only a single observation in the data set for each participant/looks/personality combination, the three-way interaction has one level per observation. In a linear mixed model, this means the variance at the lowest level of nesting will be confounded with the residual variance.

You can see this in two ways:

  • lme appears to fit the model just fine, but if you try to calculate confidence intervals via intervals(), you get
 intervals(speedDateModel)
 ## Error in intervals.lme(speedDateModel) : 
 ##   cannot get confidence intervals on var-cov components: 
 ##   Non-positive definite approximate variance-covariance
  • If you try this with lmer you get:
## Error: number of levels of each grouping factor
##   must be < number of observations

In both cases, this is a clue that something's wrong. (You can overcome this in lmer if you really want to: see ?lmerControl.)

If we leave out the lowest grouping level, everything works fine:

sd2 <- lmer(dateRating ~ looks + personality +
                 gender + looks:gender + personality:gender + 
                 looks:personality+
                     (1|participant/looks),
            data=speedData)

Compare lmer and lme fixed effects:

all.equal(fixef(sd2),fixef(speedDateModel)) ## TRUE

The starling example here gives another example and further explanation of this issue.

Upvotes: 5

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