Here's my data:
subject arm treat bline change
'subject1' 'L' N 6.3597 4.9281
'subject1' 'R' T 10.3499 1.8915
'subject3' 'L' N 12.4108 -0.9008
'subject3' 'R' T 13.2422 -0.7357
'subject4' 'L' T 8.7383 2.756
'subject4' 'R' N 10.8257 -0.531
'subject5' 'L' N 7.1766 2.0536
'subject5' 'R' T 8.1369 1.9841
'subject6' 'L' T 10.3978 9.0743
'subject6' 'R' N 11.3184 3.381
'subject8' 'L' T 10.7251 2.9658
'subject8' 'R' N 10.9818 2.9908
'subject9' 'L' T 7.3745 2.9143
'subject9' 'R' N 9.4863 -3.0847
'subject10' 'L' T 11.8132 -2.1629
'subject10' 'R' N 9.5287 0.1401
'subject11' 'L' T 8.2977 6.2219
'subject11' 'R' N 9.3691 0.7408
'subject12' 'L' T 12.6003 -0.7645
'subject12' 'R' N 11.7329 0.0342
'subject13' 'L' N 9.4918 2.0716
'subject13' 'R' T 9.6205 1.5705
'subject14' 'L' T 9.3945 4.6176
'subject14' 'R' N 11.0176 1.445
'subject16' 'L' T 8.0221 1.4751
'subject16' 'R' N 9.8307 -2.3697
When I fit a mixed model with treat and arm as factors:
m <- lmer(change ~ bline + treat + arm + (1|subject), data=change1)
ls_means(m, which = NULL, level=0.95, ddf="Kenward-Roger")
The ls_means statement returns no result. Can anyone help with what is going wrong?
I too see empty results:
> ls_means(m, which = NULL, level=0.95, ddf="Kenward-Roger")
Least Squares Means table:
Estimate Std. Error df t value lower upper Pr(>|t|)
Confidence level: 95%
Degrees of freedom method: Kenward-Roger
However, the emmeans package works fine. You can use emmeans() or lsmeans() -- the latter just re-labels the emmeans() results. "Estimated marginal means" is a more generally-appropriate term.
> library(emmeans)
> lsmeans(m, "treat")
treat lsmean SE df lower.CL upper.CL
N 0.996 0.72 15 -0.539 2.53
T 2.290 0.72 15 0.755 3.82
Results are averaged over the levels of: arm
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
> lsmeans(m, "arm")
arm lsmean SE df lower.CL upper.CL
L 1.97 0.737 15.6 0.403 3.53
R 1.32 0.737 15.6 -0.248 2.88
Results are averaged over the levels of: treat
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
I suspect that lmerTest::ls_means() does not support predictors of class "character". If you change treat and arm to factors, it may work.