[R-lang] Re: Random slopes in LME

[Zhenguang Garry Cai]

Hi Ariel,

Sorry for the somewhat misleading information. I do not recall any 
formal requirement from JML for random slopes, but in a recent 
submission to JML, I was required to include random slopes. Also, as 
Roger Levy said, including random slopes is a sensible thing to do.


Garry

On 20/02/2011 12:37, Ariel M. Goldberg wrote:
> Garry,
>
> Does JML have a set of specific requirements for mixed-effects analyses?  I wasn't able to find anything in their author instructions.
>
> Thanks,
> AG
>
> On Feb 13, 2011, at 1:17 PM, Zhenguang Cai wrote:
>
>> Hi all,
>>
>> I have a question concerning random slopes in mixed effects modeling. So I ran a structural priming experiment with a 4-level variable (prime type, A, B, D and D). The dependent variable is response construction (DO dative vs. PO dative).  The following is a summary of the experiment results.
>>
>> Prime		A	B	C	D
>> DOs		85	24	38	59
>> POs		82	144	128	109
>> % of DOs	.51	.14	.23	.35
>>
>> I am interested in whether different prime types induced different priming, e.g., whether A led to more DO responses than B, C or D. Initially, I ran LME analyses with random intercepts only. For instance, I did the following to see whether there was a main effect of prime type.
>>
>> fit.0 = lmer(Response~1+(1|Subject)+(1|Item), family=binomial)
>> fit.p = lmer(Response~Prime+(1|Subject)+(1|Item), family=binomial)
>> anova (fit.0, fit.p)
>>
>> Then, I did pairwise comparison by changing the reference level for Prime, e.g.,
>>
>> fit.p = lmer(Response~relevel(Prime,ref="B")+(1|Subject)+(1|Item),
>> family=binomial)
>>
>> It seems that all the levels differed from each other. In particular, the comparison between C and D results in Estimate = -1.02, SE = .32, Z
>> = -3.21, p<  .01.
>>
>> But it seems I have to consider whether the slope for Prime differs across subjects or item (at least this is a requirement from JML). So the following is the way I considered whether a random slope should be included in a model. I wonder whether I did the correct thing. I first determined whether subject random slope should be included by comparing the following two models.
>>
>> fit.p = lmer(Response~Prime+(1|Subject)+(1|Item), family=binomial)
>> fit.ps = lmer(Response~Prime+(Prime+1|Subject)+(1|Item), family=binomial)
>> anova (fit.p, fit.ps)
>>
>> I did the same thing about item random slopes.
>>
>> fit.p = lmer(Response~Prime+(1|Subject)+(1|Item), family=binomial)
>> fit.pi = lmer(Response~Prime+(1|Subject)+(Prime+1|Item), family=binomial)
>> anova (fit.p, fit.pi)
>>
>> The subject random slope had a significant effect, so I included it in the final model (e.g., fit.ps). But pairwise comparison returned something that is different from my initial analyses (when random slope was not considered). That is, the comparison between C and D became only marginally significant (Estimate = -.85, SE = .47, z = -1.79, p = .07). It is a bit strange because the 9% difference between B and C turned out to be significant, but the 12% difference between C and D was not.
>>
>> Or did I do anything wrong in the analyses?
>>
>> Thanks,
>>
>> Garry
>>
>>
>>
>>
>>
>>
>