CODEWITHSUNDEEP
rspssmixed-models
CogNeuro123
CogNeuro123

Reputation: 33

Different interaction p-values in R versus SPSS

I originally ran my data in SPSS because figuring out the lmer package took some time for me to learn. I spent a few weeks writing up a script in R, but my output in R is different than what I'm getting using SPSS.

I have 3 Fixed Effects: Group, Session, and TrialType.

When I ran a mixed model in SPSS, I got the interaction Group*Session p=.08 OR p=.02, depending on which covariance structure I used. This is partly the reason I wanted to use R, because I didn't have enough information to help me decide which structure to use.

Here are my models in R. I'm using Log Likelihood Test to get a p-value for this Group*Session interaction.

Mod2 = lmer(accuracy ~ group*session*trialtype + (trialtype|subject), REML=F, data=data, 
    control = lmerControl(optimizer = "optimx", optCtrl=list(method='L-BFGS-B'))))
 Mod5 = lmer(accuracy ~ session + trialtype + group + session*trialtype + trialtype*group + (trialtype|subject), 
    data=data, REML=FALSE,
    control = lmerControl(optimizer = "optimx", optCtrl=list(method='L-BFGS-B')))

 anova(Mod2, Mod5)

 Data: data
 Models:
 Mod5: accuracy ~ session + trialtype + group + session * trialtype + 
 Mod5:     trialtype * group + (trialtype | subject)
 Mod2: accuracy ~ group * session * trialtype + (trialtype | subject)
 Df     AIC     BIC logLik deviance  Chisq Chi Df Pr(>Chisq)
 Mod5 23 -961.32 -855.74 503.66  -1007.3                         
 Mod2 27 -956.32 -832.38 505.16  -1010.3 2.9989      4      0.558

I'll also note that I added the lmerControl based on the 2 warning/error messages I was getting. When I added, this, I got the singular boundary warning message.

Is it possible that R is not recognizing a grouping variable in my data? I'm not sure how to identify this or correct it.

Here is my syntax from SPSS:

 MIXED Acc BY Test TrialType Group
   /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1) SINGULAR(0.000000000001) HCONVERGE(0,
ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
   /FIXED=Test TrialType Group Test*TrialType Test*Group TrialType*Group Test*TrialType*Group |
SSTYPE(3)
   /METHOD=ML
   /PRINT=COVB DESCRIPTIVES G  SOLUTION
   /RANDOM=INTERCEPT TrialType | SUBJECT(Subject) COVTYPE(CS)
   /REPEATED=Test | SUBJECT(Subject) COVTYPE(ID).

Upvotes: 1

Views: 292

Answers (1)

David Nichols
David Nichols

Reputation: 586

The first thing to do to figure this out is to make sure the log-likelihood values for the fitted models are the same, as if the models aren't getting the same results, the test statistics wouldn't be expected to be the same. Even if the models are the same, in R you're using a chi-square statistic rather than an F, as is used in SPSS Statistics MIXED. The p values often would differ, though not usually by as much as from .02-.08 to .558. I suspect you haven't actually got strictly comparable results here.

Upvotes: 0

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