post hoc test for linear mixed model with two variables

1
votes

I built a linear mixed model and did a post hoc test for it. Fixed factors are the phase numbers (time) and the group.

statistic_of_comp <- function (x, df) {
x.full.1 <- lmer(x ~  phase_num + group + (1|mouse), data=df, REML = FALSE)
x_phase.null.1 <- lmer(x ~ group + (1|mouse), data=df, REML = FALSE)
print(anova (x.full.1, x_phase.null.1))
summary(glht(x.full.1, linfct=mcp(phase_num="Tukey")))
}

Now my problem is, that I want to do a post hoc test with more than one fixed factor. I found the following

linfct=mcp(phase_num="Tukey", group="Tukey)

but that doesn't give the result I want. At the moment I get the comparison for the groups with Tukey (every group with every other group) and the comparison between the two phases.

What I want is a comparison of the phase_numbers for every group. e.g. group1 phase1-phase2 ..., group2 phase1-phase2 etc.

1
rlinear-programminglme4mixed-modelsposthoc

1 Answers

2
votes

I'm sure you can do this with multcomp, but let me illustrate how to do it with the emmeans package. I'm going to use a regular linear model (since you haven't given a reproducible example), but the recipe below should work just as well with a mixed model.

Linear model from ?emmeans (using a built-in data set):

warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)

Apply emmeans(), followed by the pairs() function:

pairs(emmeans(warp.lm , ~tension|wool))
wool = A:
 contrast estimate   SE df t.ratio p.value
 L - M      20.556 5.16 48  3.986  0.0007 
 L - H      20.000 5.16 48  3.878  0.0009 
 M - H      -0.556 5.16 48 -0.108  0.9936 

wool = B:
 contrast estimate   SE df t.ratio p.value
 L - M      -0.556 5.16 48 -0.108  0.9936 
 L - H       9.444 5.16 48  1.831  0.1704 
 M - H      10.000 5.16 48  1.939  0.1389

For more information, see ?pairs.emmGrid or vignette("comparisons",package="emmeans") (which clarifies that these tests do indeed use Tukey comparisons by default).