Statistical analysis results of baseline (pre-training) measures: NMR mass, lean mass, fat mass, % lean mass, % fat mass; absolute VO\(_2\)max; relative VO\(_2\)max; and maximum run speed.
BASELINE_STATSAn object of class data.frame with 112 rows and 23 columns:
character; the measurement being tested.
factor; the age of the rat at the beginning of the training protocol. Levels: "6M" (Adult) and "18M" (Aged).
factor; the sex of the rat with levels "Female" and "Male".
character; the comparison between groups. Either ratios or differences between trained and sedentary group means.
character; interpretation of the value in the
estimate column. Either "ratio" (ratio between means) or "location
shift" (difference of signed mean ranks).
numeric; the value of the estimate under the null hypothesis.
numeric; ratio or difference between the means of the
groups as specified by contrast.
numeric; the standard error of the estimate.
numeric; lower bound of the 95% confidence interval.
numeric; upper bound of the 95% confidence interval.
character; the type of statistical test. Either "t" (Student's t-statistic), "z" (Z-statistic), or "W" (Wilcox's W-statistic).
numeric; the value of the test statistic.
integer; the number of samples in the SED (sedentary) group.
integer; the number of samples in the trained group
(specified by contrast).
numeric; the number of residual degrees of freedom.
numeric; the p-value associated with the statistical test.
numeric; the adjusted p-value.
logical; TRUE if p.adj < 0.05.
character; the statistical model used for testing.
character; the probability distribution and link function for the generalized linear model.
character; the model formula. Includes the response variable, any transformations, and predictors.
character; if reciprocal group variances were used as weights to account for heteroscedasticity (nonconstant residual variance), this is noted here.
character; if any observations were omitted during the analysis, they are listed here.
unique(BASELINE_STATS$response) # available measures
#> [1] "NMR Body Mass" "NMR Lean Mass" "NMR Fat Mass"
#> [4] "NMR % Lean" "NMR % Fat" "Absolute VO2max"
#> [7] "Relative VO2max" "Maximum Run Speed"
head(BASELINE_STATS)
#> response age sex contrast estimate_type null estimate SE
#> 1 NMR Body Mass 6M Female 1W / SED ratio 1 1.0602759 0.01362695
#> 2 NMR Body Mass 6M Female 2W / SED ratio 1 1.0542059 0.01396689
#> 3 NMR Body Mass 6M Female 4W / SED ratio 1 0.9932244 0.01480814
#> 4 NMR Body Mass 6M Female 8W / SED ratio 1 1.0040924 0.01246170
#> 5 NMR Body Mass 18M Female 1W / SED ratio 1 0.9447209 0.01387118
#> 6 NMR Body Mass 18M Female 2W / SED ratio 1 0.9598096 0.01492037
#> lower.CL upper.CL statistic_type statistic n_SED n_trained df
#> 1 1.0271734 1.0944452 t 4.5539975 NA NA 271
#> 2 1.0202941 1.0892448 t 3.9843646 NA NA 271
#> 3 0.9573432 1.0304504 t -0.4560039 NA NA 271
#> 4 0.9738041 1.0353228 t 0.3290707 NA NA 271
#> 5 0.9111007 0.9795817 t -3.8729390 NA NA 271
#> 6 0.9236847 0.9973474 t -2.6387900 NA NA 271
#> p.value p.adj signif model_type family
#> 1 7.961740e-06 3.156009e-05 TRUE glm gaussian("log")
#> 2 8.702181e-05 3.412170e-04 TRUE glm gaussian("log")
#> 3 1.351248e+00 9.446422e-01 FALSE glm gaussian("log")
#> 4 7.423564e-01 9.743686e-01 FALSE glm gaussian("log")
#> 5 1.999865e+00 5.271524e-04 TRUE glm gaussian("log")
#> 6 1.991198e+00 3.168499e-02 TRUE glm gaussian("log")
#> formula weights
#> 1 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> 2 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> 3 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> 4 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> 5 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> 6 pre_body_mass ~ age + group + sex + age:group reciprocal group variances
#> obs_removed
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>