After fitting a LMM I am using the emmeans() function to extract the estimated marginal means, SE and Confidence Intervals. However, depending if I directly extract the means, or save the as a data frame the estimates, their SE and their Confidence Intervals differ. Any insight would be appreciated.
Example (was not able to use dput and provide raw data due to character limit):
> summary(model)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: asin(sqrt(r_index)) ~ year + prov_season + factor_month + group + prov_season * year * group + (1 | individual)
Extraction of emmeans directly:
mm <- emmeans(model, pairwise ~ prov_season*year | group, at = list(year = c(1:8))) # extract estimates, sems and and CIs
> print(mm$emmeans)
group = naive:
prov_season year emmean SE df lower.CL upper.CL
in 1 0.0112 0.1587 309 -0.3011 0.324
off 1 0.0872 0.1768 378 -0.2604 0.435
in 2 0.0229 0.1437 253 -0.2600 0.306
off 2 0.1186 0.1577 313 -0.1916 0.429
in 3 0.0345 0.1305 203 -0.2228 0.292
off 3 0.1500 0.1405 247 -0.1268 0.427
in 4 0.0461 0.1199 162 -0.1906 0.283
off 4 0.1814 0.1261 189 -0.0674 0.430
in 5 0.0577 0.1125 136 -0.1647 0.280
off 5 0.2128 0.1155 148 -0.0155 0.441
in 6 0.0693 0.1090 125 -0.1465 0.285
off 6 0.2442 0.1098 128 0.0268 0.462
in 7 0.0810 0.1099 129 -0.1364 0.298
off 7 0.2756 0.1098 129 0.0584 0.493
in 8 0.0926 0.1149 149 -0.1345 0.320
off 8 0.3070 0.1154 151 0.0790 0.535
group = provisioned:
prov_season year emmean SE df lower.CL upper.CL
in 1 0.4076 0.0924 314 0.2258 0.589
off 1 0.2519 0.1043 413 0.0469 0.457
in 2 0.4422 0.0907 307 0.2638 0.621
off 2 0.2528 0.1000 381 0.0561 0.449
in 3 0.4768 0.0899 305 0.2999 0.654
off 3 0.2538 0.0970 355 0.0630 0.444
in 4 0.5114 0.0902 308 0.3339 0.689
off 4 0.2547 0.0952 337 0.0674 0.442
in 5 0.5461 0.0915 315 0.3659 0.726
off 5 0.2557 0.0949 329 0.0690 0.442
in 6 0.5807 0.0938 325 0.3961 0.765
off 6 0.2566 0.0959 331 0.0680 0.445
in 7 0.6153 0.0970 339 0.4245 0.806
off 7 0.2576 0.0983 342 0.0643 0.451
in 8 0.6499 0.1010 355 0.4512 0.849
off 8 0.2585 0.1019 361 0.0581 0.459
Results are averaged over the levels of: factor_month
Degrees-of-freedom method: kenward-roger
Results are given on the asin(sqrt(mu)) (not the response) scale.
Confidence level used: 0.95
Extraction of emmeans as.data.frame():
> as.data.frame(mm)
group prov_season year contrast emmean SE df lower.CL upper.CL
naive in 1 . 0.011232 0.15872 309 -0.5897 0.61217
naive off 1 . 0.087219 0.17677 378 -0.5806 0.75500
naive in 2 . 0.022854 0.14365 253 -0.5225 0.56821
naive off 2 . 0.118613 0.15767 313 -0.4783 0.71550
naive in 3 . 0.034476 0.13049 203 -0.4628 0.53172
naive off 3 . 0.150007 0.14053 247 -0.3837 0.68374
naive in 4 . 0.046098 0.11986 162 -0.4128 0.50498
naive off 4 . 0.181401 0.12615 189 -0.2999 0.66275
naive in 5 . 0.057720 0.11249 136 -0.3749 0.49036
naive off 5 . 0.212795 0.11555 148 -0.2306 0.65616
naive in 6 . 0.069342 0.10904 125 -0.3511 0.48977
naive off 6 . 0.244189 0.10984 128 -0.1790 0.66738
naive in 7 . 0.080964 0.10988 129 -0.3423 0.50419
naive off 7 . 0.275583 0.10979 129 -0.1473 0.69850
naive in 8 . 0.092586 0.11491 149 -0.3483 0.53345
naive off 8 . 0.306977 0.11541 151 -0.1356 0.74957
provisioned in 1 . 0.407628 0.09240 314 0.0578 0.75742
provisioned off 1 . 0.251854 0.10425 413 -0.1416 0.64535
provisioned in 2 . 0.442235 0.09067 307 0.0989 0.78555
provisioned off 2 . 0.252805 0.10002 381 -0.1250 0.63063
provisioned in 3 . 0.476842 0.08994 305 0.1363 0.81742
provisioned off 3 . 0.253756 0.09698 355 -0.1128 0.62035
provisioned in 4 . 0.511450 0.09023 308 0.1698 0.85310
provisioned off 4 . 0.254708 0.09524 337 -0.1055 0.61493
provisioned in 5 . 0.546057 0.09154 315 0.1995 0.89257
provisioned off 5 . 0.255659 0.09488 329 -0.1033 0.61460
provisioned in 6 . 0.580665 0.09382 325 0.2257 0.93566
provisioned off 6 . 0.256610 0.09590 331 -0.1062 0.61941
provisioned in 7 . 0.615272 0.09700 339 0.2484 0.98214
provisioned off 7 . 0.257561 0.09827 342 -0.1141 0.62920
provisioned in 8 . 0.649879 0.10101 355 0.2681 1.03170
provisioned off 8 . 0.258513 0.10190 361 -0.1266 0.64363
as.data.frame is making a Bonferroni correction to the confidence intervals by default based on both the contrasts and the means in the table.
You can use change this behaviour using eg adjust="none" as an argument.
There is more detail on this behaviour (with respect to p-value adjustment) in answers to this question.
Why is converting emmeans contrasts to a data.frame not reporting correct p-values?
It can be difficult to predict what adjustment to p-values and confidence intervals emmeans makes and its not always obvious from the documentation so its usually better to control it explicitly.
By the way, even though you couldn't paste your full data it would have been easy enough to make a small reproducible dataset to demonstrate your problem. emmeans with any linear model will behave in the same way.