lizard_brain_evolution/testoutput.doc at main · ljohnso14/lizard_brain_evolution · GitHub

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Whole Brain

Analysis of Variance, using Residual Randomization
Permutation procedure: Randomization of null model residuals
Number of permutations: 1000
Estimation method: Generalized Least-Squares (via OLS projection)
Sums of Squares and Cross-products: Type III
Effect sizes (Z) based on F distributions

                  Df        SS         MS     Rsq      F      Z Pr(>F)
Csize              1 0.0012817 0.00128171 0.12320 4.2041 2.6477  0.002 **
hatchlingSVL       1 0.0006377 0.00063767 0.06129 2.0916 1.6202  0.055 .
clutch_size        1 0.0002613 0.00026126 0.02511 0.8570 0.0079  0.493
reproductive_mode  1 0.0005107 0.00051072 0.04909 1.6752 1.2865  0.101
Residuals         24 0.0073169 0.00030487 0.70328
Total             28 0.0104039
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: procD.lm(f1 = coords ~ Csize + hatchlingSVL + clutch_size + reproductive_mode,
    iter = iter, seed = seed, RRPP = TRUE, SS.type = SS.type,
    effect.type = effect.type, int.first = int.first, Cov = Cov,
    data = data, print.progress = print.progress)

Analysis of Variance, using Residual Randomization
Permutation procedure: Randomization of null model residuals
Number of permutations: 1000
Estimation method: Generalized Least-Squares (via OLS projection)
Sums of Squares and Cross-products: Type III
Effect sizes (Z) based on F distributions

              Df        SS         MS     Rsq      F        Z Pr(>F)
Csize          1 0.0005756 0.00057563 0.05533 1.8788  1.46690  0.081 .
activity_time  2 0.0005845 0.00029225 0.05618 0.9538  0.04149  0.493
microhabitat   2 0.0004260 0.00021302 0.04095 0.6953 -0.78756  0.766
foraging_mode  3 0.0009962 0.00033208 0.09576 1.0839  0.43242  0.327
Residuals     20 0.0061278 0.00030639 0.58899
Total         28 0.0104039
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: procD.lm(f1 = coords ~ Csize + activity_time + microhabitat +
    foraging_mode, iter = iter, seed = seed, RRPP = TRUE, SS.type = SS.type,
    effect.type = effect.type, int.first = int.first, Cov = Cov,
    data = data, print.progress = print.progress)