emmeans - control vs treatment for more than one factor - r

Question

I'm using emmeans to perform custom comparisons to a control group. The trt.vs.ctrl approach works perfectly for me if I'm only interested in comparing one factor, but then fails (or I fail) when I set the comparison to be more complicated (i.e., the control group is described by a specific combination of 2+ variables).
Example code below. Say that using the pigs data, I want to compare all diets to the low percent fish diet. Note how in the nd data frame, "fish" only has 9% associated with it. However, when I run emmeans, the function does not pick up on the nesting, and while the control is correct, the treatment groups also include various values of fish and percents. Which means that the p-value adjustment is wrong.
So the two approaches I can think of:
How do I make emmeans pick up on the nesting in this case, or
How do I do the dunnettx adjustment manually (=I can use adjustment "none", then pull out the tests I actually want, and adjust the p-value myself?).
library(emmeans)
library(dplyr)
pigs.lm <- lm(log(conc) ~ source + factor(percent), data = pigs)
nd <- expand.grid(source = levels(pigs$source), percent = unique(pigs$percent)) %>%
filter(percent == 9 | source != "fish")
emmeans(pigs.lm, trt.vs.ctrl ~ source + percent,
data = nd, covnest = TRUE, cov.reduce = FALSE)
Appreciate your help.
The suggestion to use include worked perfectly. Posting my code here in case anyone else has the same issue in the future.
library(emmeans)
library(dplyr)
library(tidyr)
pigs.lm <- lm(log(conc) ~ source + factor(percent), data = pigs)
nd <- expand.grid(source = levels(pigs$source), percent = unique(pigs$percent)) %>%
filter(percent == 9 | source != "fish")
ems <- emmeans(pigs.lm, trt.vs.ctrl ~ source + percent,
data = nd, covnest = TRUE, cov.reduce = FALSE)
# to identify which levels to exclude - in this case,
# I only want the low-percent fish to remain as the ref level
aux <- as.data.frame(ems[[1]]) %>%
mutate(ID = 1:n()) %>%
filter(!grepl("fish", source) | ID == 1)
emmeans(pigs.lm, trt.vs.ctrl ~ source + percent,
data = nd, covnest = TRUE, cov.reduce = FALSE, include = aux$ID)

Answer 1

I'm not totally clear on what you are trying to accomplish, but I don't think filtering the data is the solution.
If your goal is to compare the marginal means for source with the (fish, 9 percent) combination, you can do it by constructing two sets of emmeans, then subsetting and combining:
emm1 = emmeans(pigs.lm, "source")
emm2 = emmeans(pigs.lm, ~source*percent)
emm3 = emm2[1] + emm1 # or rbind(emm2[1], emm1)
Then you get
> confint(emm3, adjust ="none")
source percent emmean SE df lower.CL upper.CL
fish 9 3.22 0.0536 23 3.11 3.33
fish . 3.39 0.0367 23 3.32 3.47
soy . 3.67 0.0374 23 3.59 3.74
skim . 3.80 0.0394 23 3.72 3.88
Results are averaged over some or all of the levels of: percent
Results are given on the log (not the response) scale.
Confidence level used: 0.95
> contrast(emm3, "trt.vs.ctrl1")
contrast estimate SE df t.ratio p.value
fish,. - fish,9 0.174 0.0366 23 4.761 0.0002
soy,. - fish,9 0.447 0.0678 23 6.595 <.0001
skim,. - fish,9 0.576 0.0696 23 8.286 <.0001
Results are averaged over some or all of the levels of: percent
Results are given on the log (not the response) scale.
P value adjustment: dunnettx method for 3 tests
Another (much more tedious, more error-prone) way to do the same thing is to get the EMMs for the factor combinations, and then use custom contrasts:
> contrast(emm2, list(con1 = c(-3,0,0, 1,0,0, 1,0,0, 1,0,0)/4,
+ con2 = c(-4,1,0, 0,1,0, 0,1,0, 0,1,0)/4,
+ con3 = c(-4,0,1, 0,0,1, 0,0,1, 0,0,1)/4),
+ adjust = "mvt")
contrast estimate SE df t.ratio p.value
con1 0.174 0.0366 23 4.761 0.0002
con2 0.447 0.0678 23 6.595 <.0001
con3 0.576 0.0696 23 8.286 <.0001
Results are given on the log (not the response) scale.
P value adjustment: mvt method for 3 tests
(The mvt adjustment is the exact correction for which dunnettx is only an approximation. It doesn't default to mvt because it is computationally heavy for a large number of tests.)
In answer to the last part of the question, you may use exclude (or include) to focus on a subset of the levels; see ? pairwise.emmc.