If I understood correctly, your model is already doing the interpretation you would wish to have inside target==TRUE. If I am correct, you could translate the model terms in your example as follows:
"(Intercept)" -> target==TRUE, cond==0 (even if model matrix contains all conds)
"cond1" -> target==TRUE, cond==1 on top of cond==0
"cond2" -> target==TRUE, cond==2 on top of cond==0
"targetFALSE" -> target==FALSE, cond==0 (even if model matrix contains all conds)
"cond1:targetFALSE" -> target==FALSE, cond==1 on top of cond==0
"cond2:targetFALSE" -> target==FALSE, cond==2 on top of cond==0
So isn't the interesting difference detected in terms "(Intercept)", "cond1" and "cond2"? Taking a look at the fixed effects' model matrix structure in getME(stu3,'X') may be helpful.
Below is an example data I constructed to test your case. Notice that I built three different responses: one without any effect, one with just target==TRUE effect, and one with an effect for target==TRUE and an interaction effect with target==TRUE and the different levels of cond. The artificially introduced effect is detected in fit1 and fit2:
set.seed(0)
struct <- expand.grid(target = c(FALSE,TRUE), cond = as.factor(0:2), patient = LETTERS[1:20])
attach(struct)
ranpatient <- rep(rnorm(20), each=6)
rerror <- rnorm(120)
# Just random noise
response0 <- ranpatient + rerror
# When target==TRUE we increment the response by 1 and add errors
response1 <- 1*target + ranpatient + rerror
# When target==TRUE we increment the response by 1,
# to which we also add an interaction effect condition {0,1,2} * target {0,1}
# notice that numeric transformation of cond {0,1,2} transforms to ranks {1,2,3}
response2 <- 1*target + target*(as.numeric(cond)-1) + ranpatient + rerror
dat <- data.frame(cond, target, patient, response0, response1, response2)
detach(struct)
require(lme4)
fit0 <- lmer(response0 ~ cond*target + (1|patient), data=dat)
fit1 <- lmer(response1 ~ cond*target + (1|patient), data=dat)
fit2 <- lmer(response2 ~ cond*target + (1|patient), data=dat)
head(dat)
round(coef(summary(fit0)),2) # Notice low t values
round(coef(summary(fit1)),2) # High t value for targetTRUE
round(coef(summary(fit2)),2) # High t value for interaction cond0/1/2 with targetTRUE
# Notice how cond==1 adds 1, and cond==2 adds 2 in comparison to cond==0 when targetTRUE
# Notice also that coefficient "cond2:targetTRUE" is incremental to term "targetTRUE", not "cond1:targetTRUE"
head(getME(fit2,'X')) # Columns correspond to the fixed effect terms
With the output
> head(dat)
cond target patient response0 response1 response2
1 0 FALSE A 1.038686 1.038686 1.038686
2 0 TRUE A 1.640350 2.640350 2.640350
3 1 FALSE A 1.396291 1.396291 1.396291
4 1 TRUE A 2.067144 3.067144 4.067144
5 2 FALSE A 1.205848 1.205848 1.205848
6 2 TRUE A 1.766562 2.766562 4.766562
> round(coef(summary(fit0)),2) # Notice low t values
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 0.00 0.29 -0.01
cond1:targetTRUE 0.13 0.41 0.32
cond2:targetTRUE 0.08 0.41 0.19
> round(coef(summary(fit1)),2) # High t value for targetTRUE
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 1.00 0.29 3.42
cond1:targetTRUE 0.13 0.41 0.32
cond2:targetTRUE 0.08 0.41 0.19
> round(coef(summary(fit2)),2) # High t value for interaction cond0/1/2 with targetTRUE
Estimate Std. Error t value
(Intercept) -0.13 0.31 -0.40
cond1 0.18 0.29 0.62
cond2 0.00 0.29 0.00
targetTRUE 1.00 0.29 3.42
cond1:targetTRUE 1.13 0.41 2.75
cond2:targetTRUE 2.08 0.41 5.04
> # Notice how cond==1 adds 1, and cond==2 adds 2 in comparison to cond==0 when targetTRUE
> # Notice also that coefficient "cond2:targetTRUE" is incremental to term "targetTRUE", not "cond1:targetTRUE"
> head(getME(fit2,'X')) # Columns correspond to the fixed effect terms
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 0 0 0 0 0
[2,] 1 0 0 1 0 0
[3,] 1 1 0 0 0 0
[4,] 1 1 0 1 1 0
[5,] 1 0 1 0 0 0
[6,] 1 0 1 1 0 1
