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  1. PO
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    <p>If I understood correctly, your model is already doing the interpretation you would wish to have inside <code>target==TRUE</code>. If I am correct, you could translate the model terms in your example as follows:</p> <pre><code>"(Intercept)" -&gt; target==TRUE, cond==0 (even if model matrix contains all conds) "cond1" -&gt; target==TRUE, cond==1 on top of cond==0 "cond2" -&gt; target==TRUE, cond==2 on top of cond==0 "targetFALSE" -&gt; target==FALSE, cond==0 (even if model matrix contains all conds) "cond1:targetFALSE" -&gt; target==FALSE, cond==1 on top of cond==0 "cond2:targetFALSE" -&gt; target==FALSE, cond==2 on top of cond==0 </code></pre> <p>So isn't the interesting difference detected in terms <code>"(Intercept)"</code>, <code>"cond1"</code> and <code>"cond2"</code>? Taking a look at the fixed effects' model matrix structure in <code>getME(stu3,'X')</code> may be helpful.</p> <p>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 <code>target==TRUE</code> effect, and one with an effect for <code>target==TRUE</code> and an interaction effect with <code>target==TRUE</code> and the different levels of <code>cond</code>. The artificially introduced effect is detected in <code>fit1</code> and <code>fit2</code>:</p> <pre><code>set.seed(0) struct &lt;- expand.grid(target = c(FALSE,TRUE), cond = as.factor(0:2), patient = LETTERS[1:20]) attach(struct) ranpatient &lt;- rep(rnorm(20), each=6) rerror &lt;- rnorm(120) # Just random noise response0 &lt;- ranpatient + rerror # When target==TRUE we increment the response by 1 and add errors response1 &lt;- 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 &lt;- 1*target + target*(as.numeric(cond)-1) + ranpatient + rerror dat &lt;- data.frame(cond, target, patient, response0, response1, response2) detach(struct) require(lme4) fit0 &lt;- lmer(response0 ~ cond*target + (1|patient), data=dat) fit1 &lt;- lmer(response1 ~ cond*target + (1|patient), data=dat) fit2 &lt;- 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 </code></pre> <p>With the output</p> <pre><code>&gt; 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 &gt; 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 &gt; 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 &gt; 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 &gt; # Notice how cond==1 adds 1, and cond==2 adds 2 in comparison to cond==0 when targetTRUE &gt; # Notice also that coefficient "cond2:targetTRUE" is incremental to term "targetTRUE", not "cond1:targetTRUE" &gt; 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 </code></pre>
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    1. POChange intercept using lmer function in R
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    1. USTeemu Daniel Laajala
    1. USTeemu Daniel Laajala
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      1. VTUpMod
    1. COthis all makes sense to me. The only adjustment I would ask is to use the accessor methods: `coef(summary())` rather than `@coefs` and `getME(.,"X")` rather than `@X`
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      1. USBen Bolker
    2. COThanks for the suggestion! I'll keep it in mind. I now corrected the above code and output.
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      1. USTeemu Daniel Laajala
 

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