A list of emmGrid objects, one for each muscle-specific
measures: terminal muscle mass, mean cross-sectional area (CSA), glycogen,
capillary contacts, and citrate synthase.
MUSCLES_EMMAn object of class list of length 5.
names(MUSCLES_EMM) # available measures
#> [1] "Terminal Muscle Mass" "Mean CSA" "Glycogen"
#> [4] "Capillary Contacts" "Citrate Synthase"
str(MUSCLES_EMM)
#> List of 5
#> $ Terminal Muscle Mass:'emmGrid' object with variables:
#> group = SED, 1W, 2W, 4W, 8W
#> age = 6M, 18M
#> sex = Female, Male
#> muscle = LG, MG, PL, SOL
#> Transformation: “log”
#> $ Mean CSA :'emmGrid' object with variables:
#> group = SED, 8W
#> age = 6M, 18M
#> sex = Female, Male
#> muscle = LG, MG, PL, SOL
#> Transformation: “log”
#> $ Glycogen :'emmGrid' object with variables:
#> group = SED, 1W, 2W, 4W, 8W
#> age = 6M, 18M
#> sex = Female, Male
#> muscle = LG, MG, PL, SOL
#> Transformation: “log”
#> $ Capillary Contacts :'emmGrid' object with variables:
#> group = SED, 8W
#> age = 6M, 18M
#> sex = Female, Male
#> muscle = LG, MG, PL, SOL
#> Transformation: “log”
#> $ Citrate Synthase :'emmGrid' object with variables:
#> group = SED, 1W, 2W, 4W, 8W
#> age = 6M, 18M
#> sex = Female, Male
#> muscle = LG, MG, PL, SOL
#> Transformation: “log”
# Print one of the emmGrid objects
MUSCLES_EMM[["Glycogen"]]
#> Warning: Bias adjustment skipped: No valid 'sigma' provided
#> (And perhaps bias.adjust should NOT be used; see ? summary.emmGrid)
#> age = 6M, sex = Female, muscle = LG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 0.362 0.156 194 0.155 0.848 1 -2.353 0.0196
#> 1W 0.452 0.166 175 0.219 0.932 1 -2.166 0.0316
#> 2W 0.619 0.194 175 0.334 1.149 1 -1.531 0.1276
#> 4W 0.568 0.186 175 0.298 1.083 1 -1.730 0.0854
#> 8W 2.494 0.389 175 1.833 3.394 1 5.859 <.0001
#>
#> age = 18M, sex = Female, muscle = LG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 1.743 0.343 175 1.182 2.570 1 2.823 0.0053
#> 1W 1.707 0.322 175 1.176 2.476 1 2.834 0.0051
#> 2W 1.504 0.302 175 1.012 2.237 1 2.033 0.0435
#> 4W 3.042 0.430 175 2.302 4.020 1 7.874 <.0001
#> 8W 5.972 0.602 175 4.895 7.287 1 17.725 <.0001
#>
#> age = 6M, sex = Male, muscle = LG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 2.160 0.473 175 1.403 3.326 1 3.520 0.0005
#> 1W 1.134 0.342 175 0.625 2.058 1 0.416 0.6776
#> 2W 1.382 0.378 175 0.805 2.370 1 1.182 0.2389
#> 4W 1.267 0.362 175 0.721 2.227 1 0.830 0.4079
#> 8W 4.539 0.722 175 3.316 6.213 1 9.510 <.0001
#>
#> age = 18M, sex = Male, muscle = LG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 2.194 0.532 175 1.359 3.542 1 3.237 0.0014
#> 1W 2.376 0.496 175 1.574 3.586 1 4.148 0.0001
#> 2W 2.536 0.512 175 1.702 3.777 1 4.609 <.0001
#> 4W 3.525 0.604 175 2.514 4.942 1 7.357 <.0001
#> 8W 7.329 0.870 175 5.798 9.265 1 16.773 <.0001
#>
#> age = 6M, sex = Female, muscle = MG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 1.362 0.475 194 0.684 2.712 1 0.885 0.3771
#> 1W 1.208 0.403 175 0.625 2.334 1 0.567 0.5716
#> 2W 1.688 0.477 175 0.967 2.946 1 1.854 0.0654
#> 4W 2.480 0.578 175 1.566 3.927 1 3.899 0.0001
#> 8W 4.902 0.812 175 3.535 6.798 1 9.595 <.0001
#>
#> age = 18M, sex = Female, muscle = MG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 2.054 0.554 175 1.206 3.498 1 2.668 0.0083
#> 1W 2.824 0.616 175 1.836 4.345 1 4.757 <.0001
#> 2W 3.051 0.641 175 2.016 4.618 1 5.312 <.0001
#> 4W 6.101 0.906 175 4.551 8.179 1 12.179 <.0001
#> 8W 8.383 1.062 175 6.529 10.765 1 16.784 <.0001
#>
#> age = 6M, sex = Male, muscle = MG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 4.040 0.855 175 2.660 6.134 1 6.598 <.0001
#> 1W 3.469 0.792 175 2.210 5.444 1 5.447 <.0001
#> 2W 3.019 0.739 175 1.862 4.894 1 4.513 <.0001
#> 4W 3.561 0.803 175 2.283 5.556 1 5.635 <.0001
#> 8W 7.740 1.247 175 5.631 10.638 1 12.698 <.0001
#>
#> age = 18M, sex = Male, muscle = MG:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 2.534 0.757 175 1.405 4.569 1 3.112 0.0022
#> 1W 5.856 1.029 175 4.140 8.285 1 10.056 <.0001
#> 2W 4.036 0.854 175 2.657 6.129 1 6.590 <.0001
#> 4W 4.690 0.921 175 3.183 6.911 1 7.869 <.0001
#> 8W 7.551 1.169 175 5.563 10.248 1 13.061 <.0001
#>
#> age = 6M, sex = Female, muscle = PL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 1.836 0.582 194 0.982 3.431 1 1.916 0.0569
#> 1W 3.614 0.693 175 2.475 5.276 1 6.700 <.0001
#> 2W 2.434 0.569 175 1.535 3.860 1 3.806 0.0002
#> 4W 2.474 0.573 175 1.566 3.909 1 3.909 0.0001
#> 8W 6.663 0.941 175 5.042 8.804 1 13.430 <.0001
#>
#> age = 18M, sex = Female, muscle = PL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 4.069 0.775 175 2.794 5.926 1 7.368 <.0001
#> 1W 3.289 0.661 175 2.212 4.890 1 5.923 <.0001
#> 2W 6.461 0.927 175 4.868 8.575 1 13.010 <.0001
#> 4W 6.974 0.963 175 5.311 9.158 1 14.071 <.0001
#> 8W 18.067 1.549 175 15.254 21.399 1 33.748 <.0001
#>
#> age = 6M, sex = Male, muscle = PL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 2.457 0.608 175 1.507 4.006 1 3.630 0.0004
#> 1W 5.479 0.909 175 3.949 7.600 1 10.256 <.0001
#> 2W 4.527 0.826 175 3.159 6.490 1 8.278 <.0001
#> 4W 4.836 0.896 175 3.355 6.971 1 8.506 <.0001
#> 8W 8.714 1.208 175 6.629 11.456 1 15.620 <.0001
#>
#> age = 18M, sex = Male, muscle = PL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 3.014 0.753 175 1.840 4.936 1 4.413 <.0001
#> 1W 5.134 0.880 175 3.661 7.200 1 9.549 <.0001
#> 2W 4.953 0.864 175 3.510 6.988 1 9.173 <.0001
#> 4W 5.789 0.934 175 4.210 7.959 1 10.883 <.0001
#> 8W 11.069 1.291 175 8.792 13.935 1 20.606 <.0001
#>
#> age = 6M, sex = Female, muscle = SOL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 14.261 1.517 194 11.562 17.590 1 24.982 <.0001
#> 1W 10.525 1.236 175 8.347 13.271 1 20.036 <.0001
#> 2W 12.894 1.368 175 10.458 15.899 1 24.091 <.0001
#> 4W 11.841 1.311 175 9.516 14.734 1 22.316 <.0001
#> 8W 16.616 1.553 175 13.816 19.983 1 30.060 <.0001
#>
#> age = 18M, sex = Female, muscle = SOL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 37.586 2.463 175 33.027 42.775 1 55.347 <.0001
#> 1W 19.217 1.671 175 16.187 22.814 1 34.000 <.0001
#> 2W 17.746 1.605 175 14.844 21.215 1 31.791 <.0001
#> 4W 19.725 1.693 175 16.652 23.365 1 34.750 <.0001
#> 8W 46.244 2.592 175 41.402 51.653 1 68.412 <.0001
#>
#> age = 6M, sex = Male, muscle = SOL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 19.257 1.911 175 15.832 23.423 1 29.811 <.0001
#> 1W 13.503 1.600 175 10.687 17.060 1 21.967 <.0001
#> 2W 13.591 1.605 175 10.765 17.159 1 22.094 <.0001
#> 4W 12.913 1.565 175 10.166 16.401 1 21.113 <.0001
#> 8W 23.381 2.219 175 19.387 28.198 1 33.207 <.0001
#>
#> age = 18M, sex = Male, muscle = SOL:
#> group response SE df lower.CL upper.CL null t.ratio p.value
#> SED 19.588 2.155 175 15.766 24.337 1 27.047 <.0001
#> 1W 22.398 2.061 175 18.679 26.857 1 33.793 <.0001
#> 2W 21.665 2.027 175 18.012 26.057 1 32.879 <.0001
#> 4W 14.720 1.671 175 11.766 18.416 1 23.697 <.0001
#> 8W 22.904 2.084 175 19.139 27.409 1 34.418 <.0001
#>
#> Degrees-of-freedom method: inherited from containment when re-gridding
#> Confidence level used: 0.95
#> Intervals are back-transformed from the log scale
#> Tests are performed on the log scale