I know that the question has been submitted several times but given my level of knowledge, I'm afraid I haven't been able to find a satisfactory answer. I have two fixed effects : frequency (high vs low) and predictability (high vs low). my model is as follows :
m1=lmer(log(FF) ~ frequence * Predic + (1 | Sujet) + (1 | item), data= FreqPredicExpert72) The anova function returns : Type III Analysis of Variance Table with Satterthwaite's method Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Predic 1.78800 1.78800 1 68.035 23.1208 8.774e-06 *** frequence 0.78005 0.78005 1 68.155 10.0869 0.002243 ** Predic:frequence 0.16368 0.16368 1 67.894 2.1166 0.150321 The summary function returns : Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest'] Formula: log(FF) ~ Predic * frequence + (1 | Sujet) + (1 | item) Data: FreqPredicExpert72 REML criterion at convergence: 558.9 Scaled residuals: Min 1Q Median 3Q Max -3.6928 -0.5517 -0.0021 0.5770 4.3347 Random effects: Groups Name Variance Std.Dev. item (Intercept) 0.002058 0.04536 Sujet (Intercept) 0.014550 0.12062 Residual 0.077333 0.27809 Number of obs: 1580, groups: item, 72; Sujet, 28 Fixed effects: Estimate Std. Error df t value Pr(>|t|) (Intercept) 5.33733 0.02883 49.82410 185.118 <2e-16 *** PredicHP -0.05921 0.02490 67.27315 -2.378 0.0202 * frequenceHF -0.03042 0.02489 67.28622 -1.222 0.2260 PredicHP:frequenceHF -0.05133 0.03528 67.89410 -1.455 0.1503 Correlation of Fixed Effects: (Intr) PrdcHP frqnHF PredicHP -0.428 frequenceHF -0.429 0.497 PrdcHP:frHF 0.302 -0.705 -0.705
There is no difference concerning p value fonte interaction, but as on can see, anova returns a significant effect of frequency (p = 0.002243 ** ) while this effect is not significant in results with summary (p = 0.2260). For me it's a big problem.
My question is threefold
1) Why both p value of the interaction and p value of the Predic effect are identical in summary() and in anova() function? And why it is not the case for the frequency effect?
2) In what outcome can we trust? (Anova or summary?). Pratically, effect of frequency amounted to 6 ms in the group of 21 adults. It seems unlikely that this effect is significant. So I will tend to rely on results from the summary() rather than anova()
3) What I have just described is part of my data. IN fact, I have three groups of participants (grade 3, grade 5 and Adults).
To analyse my data, I conducted a 3 way-anova : Group x frequency x Predic The model used is as follows :
m1=lmer(log(FF) ~ Group * frequence * Predic + (1 | Sujet) + (1 | item), data= FreqPredicExpert72)
For this model, anova() and summary() returns very different results. If I understand correctly, it’s because summary() function tests contrasts? Is it possible to specify something so that summary function gives global effect for the Group x frequency x Predic interaction as in anova() function. Does this have anything to do with contrast specification?