post-hoc test that will show interactions

Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: score ~ group * condition + (1 | subject) + (1 | token_set)
   Data: EN_JT_1

REML criterion at convergence: 512.7

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2745 -0.3231  0.1870  0.6697  1.8834 

Random effects:
 Groups    Name        Variance Std.Dev.
 subject   (Intercept) 0.021850 0.14782 
 token_set (Intercept) 0.003202 0.05658 
 Residual              0.091070 0.30178 
Number of obs: 852, groups:  subject, 71; token_set, 24

Fixed effects:
                               Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                     1.00047    0.03727  85.51605  26.844  < 2e-16 ***
groupHS                        -0.06865    0.05296 106.58618  -1.296  0.19773    
groupSB                        -0.03172    0.05862 106.58450  -0.541  0.58959    
conditionEN-GJT-R-RES          -0.28400    0.03813  48.55384  -7.449 1.42e-09 ***
groupHS:conditionEN-GJT-R-RES  -0.08721    0.04796 756.84544  -1.818  0.06938 .  
groupSB:conditionEN-GJT-R-RES   0.14858    0.05308 756.82431   2.799  0.00525 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) gropHS gropSB cEN-GJ gHS:EN
groupHS     -0.569                            
groupSB     -0.514  0.361                     
cEN-GJT-R-R -0.512  0.228  0.206              
gHS:EN-GJT-  0.257 -0.453 -0.164 -0.503       
gSB:EN-GJT-  0.233 -0.164 -0.453 -0.455  0.361

I have run the mixed-model above, where I crossed two fixed variables (group and condition) with each other to look for any effects of each as well as any interactions between the two.

I have three groups (EN, HS, and SB) and two conditions (EN-GJT-R-RES and EN-GJT-R-GAP).

I have two questions:

  1. I'm not sure how to understand the output under "Fixed Effects" -- specifically the final two rows. I believe in the three rows below (Intercept) each group or condition is compared to the reference group or condition: in other words, in the second row HS is compared to EN, in the third row SB is compared to EN, and in the fourth row EN-GJT-R-RES is compared to EN-GJT-R-GAP. Is that correct? I'm not sure about the last two rows. What is being compared to what?

  2. How do I run a post-hoc test that will compare all groups and all conditions with each other, as well as show all interactions? I found a line of code here that will let me run a Tukey test for groups, and a separate one for conditions (see below), but I'm not sure how I can run a Tukey test to show interactions.

> summary(glht(model_1, linfct = mcp(group = "Tukey")), test = adjusted("holm"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lmer(formula = score ~ group * condition + (1 | subject) + (1 | 
    token_set), data = EN_JT_1)

Linear Hypotheses:
             Estimate Std. Error z value Pr(>|z|)
HS - EN == 0 -0.06865    0.05296  -1.296    0.585
SB - EN == 0 -0.03172    0.05862  -0.541    1.000
SB - HS == 0  0.03693    0.06322   0.584    1.000
(Adjusted p values reported -- holm method)
> summary(glht(model_1, linfct = mcp(condition = "Tukey")), test = adjusted("holm"))

	 Simultaneous Tests for General Linear Hypotheses

Multiple Comparisons of Means: Tukey Contrasts


Fit: lmer(formula = score ~ group * condition + (1 | subject) + (1 | 
    token_set), data = EN_JT_1)

Linear Hypotheses:
                                 Estimate Std. Error z value Pr(>|z|)    
EN-GJT-R-RES - EN-GJT-R-GAP == 0 -0.28400    0.03813  -7.449 9.41e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- holm method)