How to only test select pairwise comparisons using emmeans?

I have seen several examples how it might be possible to select desired pairwise comparisons, but unfortunately do not know how to apply that to my data.

Here is my abbreviated data set: https://www.dropbox.com/s/x9xjc9o0222rg0w/df.csv?dl=0

# FIXED effects: age and brain_area

df$age <- factor(df$age)
df$brain_area <- factor(df$brain_area)

# RANDOM effects: subject_ID and section

df$subject_ID <- factor(df$subject_ID)
df$section <- factor(df$section)

# dependent variable: DV

# ___________________ mixed TWO-way ANOVA 
require(lme4)
require(lmerTest)
require(emmeans)

model = lmer(DV ~ age * brain_area + (1 | subject_ID), data = df)  

anova(model) #  significant interaction and both main effects

# ____________________ ALL pairwise comparisons

emmeans(model, pairwise~brain_area|age, adj='fdr')

# ____________________ I marked below comparisons that I would like to exclude (but keep all others)

$contrasts
age = old:
  contrast    estimate     SE  df t.ratio p.value
a - b         0.0412 0.0158 174   2.603  0.0125
a - c        -0.0566 0.0158 174  -3.572  0.0007
a - control   0.3758 0.0158 174  23.736  <.0001    # exclude
a - d        -0.0187 0.0158 174  -1.182  0.2387
b - c        -0.0978 0.0158 174  -6.175  <.0001
b - control   0.3346 0.0158 174  21.132  <.0001    # exclude
b - d        -0.0599 0.0158 174  -3.786  0.0004
c - control   0.4324 0.0158 174  27.308  <.0001
c - d         0.0378 0.0158 174   2.389  0.0199
control - d  -0.3946 0.0158 174 -24.918  <.0001    # exclude

age = young:
  contrast    estimate     SE  df t.ratio p.value
a - b         0.0449 0.0147 174   3.063  0.0032
a - c        -0.0455 0.0147 174  -3.105  0.0032
a - control   0.2594 0.0147 174  17.694  <.0001    # exclude
a - d         0.0202 0.0147 174   1.377  0.1702
b - c        -0.0904 0.0147 174  -6.169  <.0001
b - control   0.2145 0.0147 174  14.631  <.0001    # exclude
b - d        -0.0247 0.0147 174  -1.686  0.1040
c - control   0.3049 0.0147 174  20.799  <.0001
c - d         0.0657 0.0147 174   4.483  <.0001
control - d  -0.2392 0.0147 174 -16.317  <.0001    # exclude

# ____________________ The line below seems to work BUT completely excludes 'control' level from factor 'brain_area'. I do not wish to completely exclude it...

emmeans(model, specs=pairwise~brain_area| age,
               at = list(brain_area = c("a", "b", "c", "d")), adj='fdr' )

Solution 1:

You need to provide the contrast coefficients manually. In this case, it's fairly simple to obtain all of them, then remove the ones you don't want; something like this:

EMM <- emmeans(model, ~ brain_area | age)
EMM     # show the means

coef <- emmeans:::pairwise.emmc(levels(EMM)[["brain_area"]])
coef <- coef[-c(3, 6, 10)]

contrast(EMM, coef, adjust = "fdr")