[R-sig-ME] Order of terms for random slopes

[Ben Bolker]

  Thanks. This is a known issue: https://github.com/lme4/lme4/issues/449

  At the risk of sounding like a stuffy old statistical fart:

  - yes, lme4 *should* give an identical fit either way
  - it's not terribly surprising that a model with 11 parameters fitted
to 48 observations is numerically unstable ...
  - there don't seem to be any _substantive_ differences in the estimate ...

  cheers
   Ben Bolker

https://github.com/lme4/lme4/issues/449

On 2018-08-29 12:31 PM, Stefan Th. Gries wrote:
> Hi all
> 
> I have a question about how the ordering of variable names in the
> random effects structure of an lmer model leads to different results.
> These are the data:
> 
> ###############
> x <- structure(list(OVERLAParcsine = c(0.232077682862713, 0.656060590924923,
> 0.546850950695944, 0.668742703202372, 0.631058840778021, 0.433445320069886,
> 0.315193032440724, 0.656060590924923, 0.389796296474261, 0.455598673395823,
> 0.500654712404588, 0.477995198518952, 0.304692654015398, 0.631058840778021,
> 0.489290778014116, 0.694498265626556, 0.656060590924923, 0.466765339047296,
> 0.411516846067488, 0.582364237868743, 0.33630357515398, 0.36826789343664,
> 0.489290778014116, 0.582364237868743, 0.283794109208328, 0.631058840778021,
> 0.33630357515398, 0.606505855213087, 0.512089752934148, 0.150568272776686,
> 0.273393031467473, 0.466765339047296, 0.160690652951911, 0.120289882394788,
> 0.558600565342801, 0.400631592701372, 0.273393031467473, 0.72081876087009,
> 0.444492776935819, 0.681553211563117, 0.546850950695944, 0.523598775598299,
> 0.273393031467473, 0.694498265626556, 0.294226837748982, 0.500654712404588,
> 0.411516846067488, 0.618728690672251), NAME = structure(c(1L,
> 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
> 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
> 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
> 11L, 12L), .Label = c("Anne", "Aran", "Becky", "Carl", "Dominic",
> "Gail", "Joel", "John", "Liz", "Nicole", "Ruth", "Warren"), class = "factor"),
>     PERSON = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>     1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>     2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("caretaker",
>     "child"), class = "factor"), PHASE = c(1L, 1L, 1L, 1L, 1L,
>     1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>     2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>     1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L)), class =
> "data.frame", row.names = c(NA, -48L))
> ###############
> 
> With the following order of variables in the random-effects structure,
> I get convergence warnings,
> 
> ###############
> summary(m1a <- lme4::lmer(OVERLAParcsine ~ 1+PERSON*PHASE +
> (1+PERSON+PHASE|NAME), data=x), correlation=F) # warning
>    logLik(m1a) # 31.89056
> ###############
> 
> but not with this order:
> 
> ###############
> summary(m1b <- lme4::lmer(OVERLAParcsine ~ 1+PERSON*PHASE +
> (1+PHASE+PERSON|NAME), data=x), correlation=F) # fine
>    logLik(m1b) # 31.89128
> ###############
> 
> Why does the order of the random effects matter when PHASE is still
> considered numeric? Thanks for any input you may have,
> STG
> 
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