Why is my ANOVA output identical when comparing two models? I am comparing two models in order to see if a specific interaction (SessionGroup) is significant. Mod1 is the full model, Mod2 is the full model MINUS the SessionGroup interaction.
mod1 = lmer(accuracy ~ session + trialtype + group + session*trialtype +     
session*group + session*group*trialtype + trialtype*group + 
(1+trialtype|subject), data=data, REML=FALSE)

mod2 = lmer(accuracy ~ session + trialtype + group + session*trialtype + 
session*group*trialtype + trialtype*group + (1+trialtype|subject), 
data=data, REML=FALSE)

Here is my identical output:
Data: data
Models:
mod1: accuracy ~ session + trialtype + group + session * trialtype + 
mod1:     session * group + session * group * trialtype + trialtype * 
mod1:     group + (1 + trialtype | subject)
mod2: accuracy ~ session + trialtype + group + session * trialtype + 
mod2:     session * group * trialtype + trialtype * group + (1 + trialtype 
| 
mod2:     subject)
     Df    AIC    BIC  logLik deviance Chisq Chi Df Pr(>Chisq)
mod1 27 4026.4 4150.3 -1986.2   3972.4                        
mod2 27 4026.4 4150.3 -1986.2   3972.4     0      0          1

Something is wrong with the code, I just can't figure it out. Also, is this the correct way to compare 2 models when looking at main effects/interactions? I've never taken an MLM class, so I've been teaching myself as I do this.
Thank you in advance!
 A: The second model also includes the interaction between session and group.
The function terms allows for looking at all main effects and interactions created by a formula.
# formula of model 1
form1 <- accuracy ~ session + trialtype + group + session*trialtype + session*group + session*group*trialtype + trialtype*group + (1+trialtype|subject)

# formula of model 2
form2 <- accuracy ~ session + trialtype + group + session*trialtype + session*group*trialtype + trialtype*group + (1+trialtype|subject)

# terms of formula 1
attr(terms(form1), "term.labels")
# [1] "session"                 "trialtype"               "group"                  
# [4] "1 + trialtype | subject" "session:trialtype"       "session:group"          
# [7] "trialtype:group"         "session:trialtype:group"

# terms of formula 2
attr(terms(form2), "term.labels")
# [1] "session"                 "trialtype"               "group"                  
# [4] "1 + trialtype | subject" "session:trialtype"       "session:group"          
# [7] "trialtype:group"         "session:trialtype:group"

identical(attr(terms(form1), "term.labels"), attr(terms(form2), "term.labels"))
# TRUE

As you can see, both formulas result in identical predictors and hence identical models. Note that session*group*trialtype (which is present in both formulas) expands to session + group + trialtype + session : group + session : trialtype + group : trialtype + session : group : trialtype.
If you want to exclude the interaction between session and groupfrom the second model, you have to modify its formula:
accuracy ~ session + trialtype + group + session*trialtype + trialtype*group + (1+trialtype|subject)

Note: In the above formula, the higher-order interaction between three variables has been removed too, since it is highly recommended to not include higher-order interactions when interactions of lower order or the corresponding main effects are absent.
