[R-sig-ME] Fwd: Question about non-significant interactions
Thierry Onkelinx
th|erry@onke||nx @end|ng |rom |nbo@be
Wed Jul 10 08:51:42 CEST 2019
Dear Francesco,
To answer your question, you should convert your hypothesis in a set of
linear contrasts and test those.
Best regards,
ir. Thierry Onkelinx
Statisticus / Statistician
Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be
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Op di 9 jul. 2019 om 23:25 schreef Francesco Romano <fbromano77 using gmail.com>:
> ---------- Forwarded message ---------
> From: Francesco Romano <fbromano77 using gmail.com>
> Date: Tue, Jul 9, 2019 at 11:24 PM
> Subject: Re: [R-sig-ME] Question about non-significant interactions
> To: Fox, John <jfox using mcmaster.ca>
>
>
> Dear John,
>
> Thanks for the reply. One of my research entails examining the relationship
> between 3 groups of speakers, the 3 levels of the group categorical
> variable previously mentioned, and two tasks. One prediction is that one
> group will perform better than other groups on one test but not the other.
>
> I fit a maximal model using the bglmr function as shown previously, then
> used car::Anova to determine main effects. My understanding from previous
> interaction with you precisely here on r-sig-me is that the function works
> as a form of shortcut to the traditional way of model-fitting/ reduction
> via the function anova() comparing models, eliminating terms one at a time.
>
> I hope this is clearer now and yes, the question is more of a statistical
> one than an R one, even though I suspect the mixed-effect aspect of the
> regression may be relevant to answering it.
>
> Frank
>
> Tue, Jul 9, 2019 at 11:03 PM Fox, John <jfox using mcmaster.ca> wrote:
>
> > Dear Francesco,
> >
> > I didn't entirely follow your question and I expect that to answer it, it
> > would be necessary to know more about what your research entails. As you
> > imply, this seems to be more a statistics question than an R question.
> It's
> > also not clear to me what function you used to fit the mixed-effects
> > logistic regression.
> >
> > But I did notice that you're apparently using Anova() for type-III tests
> > with the default contr.treatment() coding for factors. The main-effect
> > tests that result are not sensible. As it says in ?Anova:
> >
> > "Warning
> > Be careful of type-III tests: For a traditional multifactor ANOVA model
> > with interactions, for example, these tests will normally only be
> sensible
> > when using contrasts that, for different terms, are orthogonal in the
> > row-basis of the model, such as those produced by contr.sum, contr.poly,
> or
> > contr.helmert, but not by the default contr.treatment. In a model that
> > contains factors, numeric covariates, and interactions, main-effect tests
> > for factors will be for differences over the origin. In contrast (pun
> > intended), type-II tests are invariant with respect to (full-rank)
> contrast
> > coding. If you don't understand this issue, then you probably shouldn't
> use
> > Anova for type-III tests."
> >
> > I hope that this is of some help,
> > John
> > -----------------------------
> > John Fox, Professor Emeritus
> > McMaster University
> > Hamilton, Ontario
> > Canada L8S 4M4
> > web: socserv.mcmaster.ca/jfox
> >
> >
> > ________________________________________
> > From: R-sig-mixed-models [r-sig-mixed-models-bounces using r-project.org] on
> > behalf of Francesco Romano [fbromano77 using gmail.com]
> > Sent: July 9, 2019 9:49 AM
> > To: r-sig-mixed-models using r-project.org
> > Subject: [R-sig-ME] Question about non-significant interactions
> >
> > Dear all,
> >
> >
> > I have more of a theoretical than practical question for you. The model I
> > am using has two IVs, group (3 levels) and task (2 levels), and a
> > categorical DV (correct versus incorrect), hence logistic regression.
> > Random effects for subjects and items, as well as slopes for group by
> item
> > and task by subject.
> >
> > I am interested in the effect of belonging any of three groups, the
> levels
> > of the group IV, in order to test some a priori predictions. The bayesian
> > wrapper is to help the model converge.
> >
> > Here is the output:
> >
> > > summary(paper2analysis1)
> > Cov prior : item ~ wishart(df = 5.5, scale = Inf, posterior.scale = cov,
> > common.scale = TRUE)
> > : Participant ~ wishart(df = 4.5, scale = Inf,
> posterior.scale =
> > cov, common.scale = TRUE)
> > Prior dev : 6.9466
> >
> > Generalized linear mixed model fit by maximum likelihood (Laplace
> > Approximation) ['bglmerMod']
> > Family: binomial ( logit )
> > Formula: correctness ~ task * group + (1 + task | Participant) + (1 +
> > group | item)
> > Data: data
> > Control: glmerControl(optimizer = "bobyqa")
> >
> > AIC BIC logLik deviance df.resid
> > 3857.8 3957.2 -1913.9 3827.8 5570
> >
> > Scaled residuals:
> > Min 1Q Median 3Q Max
> > -2.0196 -0.3744 -0.2312 -0.1368 6.9534
> >
> > Random effects:
> > Groups Name Variance Std.Dev. Corr
> > item (Intercept) 1.1266 1.0614
> > groupL2 0.1311 0.3620 -0.12
> > groupNS 0.2029 0.4504 -0.31 0.17
> > Participant (Intercept) 0.7582 0.8708
> > taskpriming 1.2163 1.1029 -0.77
> > Number of obs: 5585, groups: item, 219; Participant, 46
> >
> > Fixed effects:
> > Estimate Std. Error z value Pr(>|z|)
> > (Intercept) -2.49187 0.28318 -8.800 < 2e-16 ***
> > taskpriming 1.30911 0.37367 3.503 0.000459 ***
> > groupL2 -0.04042 0.38322 -0.105 0.916005
> > groupNS -1.01144 0.36607 -2.763 0.005727 **
> > taskpriming:groupL2 0.04305 0.48693 0.088 0.929544
> > taskpriming:groupNS -0.04942 0.46034 -0.107 0.914506
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> > Correlation of Fixed Effects:
> > (Intr) tskprm gropL2 gropNS tsk:L2
> > taskpriming -0.733
> > groupL2 -0.660 0.482
> > groupNS -0.693 0.507 0.509
> > tskprmng:L2 0.499 -0.632 -0.755 -0.386
> > tskprmng:NS 0.530 -0.676 -0.390 -0.750 0.508
> >
> > The model was then subjected to car::Anova for ANOVA type III analysis
> with
> > the following output:
> >
> > > car::Anova(paper2analysis1, type = "III")
> > Analysis of Deviance Table (Type III Wald chisquare tests)
> >
> > Response: correctness
> > Chisq Df Pr(>Chisq)
> > (Intercept) 77.4344 1 < 2.2e-16 ***
> > task 12.2737 1 0.0004594 ***
> > group 9.9237 2 0.0070000 **
> > task:group 0.0391 2 0.9806462
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> > I am not sure how to interpret the non-significant interaction in this
> > case. Does this mean that, although simple effects exist at group level
> > within one particular task or at task level within one particular group,
> I
> > lack sufficient power to conclude those effects are real? If I look at
> the
> > simple effects, I do indeed find such effects but am not sure how to
> > interpret them against the lack of a main interaction. At a practical
> > level, the interaction, rather than the main effects, is the most
> important
> > part of the analysis.
> >
> > Thank you in advance for any advice.
> >
> > Francesco
> >
> >
> >
> >
> >
> > Best,
> >
> > Frank
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
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