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Type 'q()' to quit R. > library(testthat) > library(RRPP) > > test_check("geomorph") Loading required package: geomorph Loading required package: rgl Loading required package: Matrix Call: bilat.symmetry(A = wingshape, ind = ind, side = side, replicate = replicate, object.sym = FALSE, data = gdf, iter = 3, RRPP = TRUE, print.progress = F) Symmetry (data) type: Matching Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 4 Permutations Shape ANOVA Df SS MS Rsq F Z Pr(>F) ind 9 0.104888 0.0116542 0.45533 2.6901 1.42668 0.25 side 1 0.003221 0.0032209 0.01398 0.7435 -0.87935 0.75 ind:side 9 0.038990 0.0043323 0.16926 1.0407 0.84797 0.50 ind:side:replicate 20 0.083259 0.0041629 0.36143 Total 39 0.230358 Centroid Size ANOVA Df SS MS Rsq F Z Pr(>F) ind 9 4.1496e-09 4.6107e-10 0.18555 0.8452 -0.46137 0.75 side 1 3.4740e-10 3.4738e-10 0.01553 0.6368 1.64429 0.25 ind:side 9 6.9569e-09 7.7299e-10 0.31108 1.4171 1.66837 0.25 ind:side:replicate 20 1.0910e-08 5.4549e-10 0.48784 Total 39 2.2364e-08 Call: bilat.symmetry(A = Y.gpa, ind = mosquito$ind, side = mosquito$side, replicate = mosquito$replicate, object.sym = FALSE, iter = 3, RRPP = TRUE, print.progress = F) Symmetry (data) type: Matching Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 4 Permutations Shape ANOVA Df SS MS Rsq F Z Pr(>F) ind 9 0.104888 0.0116542 0.45533 2.6901 1.42668 0.25 side 1 0.003221 0.0032209 0.01398 0.7435 -0.87935 0.75 ind:side 9 0.038990 0.0043323 0.16926 1.0407 0.84797 0.50 ind:side:replicate 20 0.083259 0.0041629 0.36143 Total 39 0.230358 Centroid Size ANOVA Df SS MS Rsq F Z Pr(>F) ind 9 4.1496e-09 4.6107e-10 0.18555 0.8452 -0.46137 0.75 side 1 3.4740e-10 3.4738e-10 0.01553 0.6368 1.64429 0.25 ind:side 9 6.9569e-09 7.7299e-10 0.31108 1.4171 1.66837 0.25 ind:side:replicate 20 1.0910e-08 5.4549e-10 0.48784 Total 39 2.2364e-08 Call: bilat.symmetry(A = shape, ind = ind, replicate = rep, object.sym = TRUE, land.pairs = lizards$lm.pairs, data = gdf, iter = 3, RRPP = TRUE, print.progress = F) Symmetry (data) type: Object Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 4 Permutations Shape ANOVA Df SS MS Rsq F Z Pr(>F) ind 48 0.236788 0.0049331 0.83194 7.3721 0.36987 0.50 side 1 0.009432 0.0094317 0.03314 14.0951 1.14287 0.25 ind:side 48 0.032119 0.0006692 0.11285 10.4367 1.73205 0.25 ind:side:replicate 98 0.006283 0.0000641 0.02208 Total 195 0.284622 Call: bilat.symmetry(A = shape, ind = ind, object.sym = TRUE, land.pairs = scallops$land.pairs, data = gdf, iter = 3, RRPP = TRUE, print.progress = F, curves = scallops$curvslide, surfaces = scallops$surfslide) Symmetry (data) type: Object Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 4 Permutations Shape ANOVA Df SS MS Rsq F Z Pr(>F) ind 4 0.060838 0.015209 0.48036 8.1184 1.712 0.25 side 1 0.058318 0.058318 0.46047 31.1287 1.661 0.25 ind:side 4 0.007494 0.001873 0.05917 Total 9 0.126650 No CS sets input. Final configurations will not be scaled No CS sets input. Final configurations will not be scaled A total of 2 subsets were combined head tail Number of points in subset 26.0 64.0 Mean centroid size 1.0 1.0 Mean relative size 0.5 0.5 A total of 2 subsets were combined head tail Number of points in subset 26.0000000 64.0000000 Mean normalized centroid size 1.1198598 3.0297141 Mean relative size 0.2698734 0.7301266 A total of 2 subsets were combined head tail Number of points in subset 26.00000000 64.000000 Mean weighted centroid size 1.71305611 16.966399 Mean relative size 0.09170803 0.908292 NOTE: more negative effects represent stronger modular signal! Effect sizes Marsh.F Marsh.M Sinkhole.F Sinkhole.M -1.407080 -1.476801 -1.531436 -1.454726 Effect sizes for pairwise differences in CR effect size Marsh.F Marsh.M Sinkhole.F Sinkhole.M Marsh.F 0.0000000 1.0012823 1.1251284 0.6658208 Marsh.M 1.0012823 0.0000000 0.2018017 0.5088345 Sinkhole.F 1.1251284 0.2018017 0.0000000 0.6883741 Sinkhole.M 0.6658208 0.5088345 0.6883741 0.0000000 P-values Marsh.F Marsh.M Sinkhole.F Sinkhole.M Marsh.F 1.0000000 0.3166903 0.2605346 0.5055256 Marsh.M 0.3166903 1.0000000 0.8400718 0.6108682 Sinkhole.F 0.2605346 0.8400718 1.0000000 0.4912172 Sinkhole.M 0.5055256 0.6108682 0.4912172 1.0000000 NOTE: more negative effects represent stronger modular signal! Effect sizes No_Modules modul.tests$Marsh.F m3.test m4.test 0.000000 -1.407080 -1.667935 -1.725208 Effect sizes for pairwise differences in CR effect size No_Modules modul.tests$Marsh.F m3.test m4.test No_Modules 0.000000 1.407080 1.6679352 1.7252084 modul.tests$Marsh.F 1.407080 0.000000 1.0489238 1.3919299 m3.test 1.667935 1.048924 0.0000000 0.6990028 m4.test 1.725208 1.391930 0.6990028 0.0000000 P-values No_Modules modul.tests$Marsh.F m3.test m4.test No_Modules 1.00000000 0.1594038 0.09532859 0.08448993 modul.tests$Marsh.F 0.15940377 1.0000000 0.29421321 0.16394363 m3.test 0.09532859 0.2942132 1.00000000 0.48455029 m4.test 0.08448993 0.1639436 0.48455029 1.00000000 Call: Observed Rate Ratio: 1.8 P-value: 0.5 Effect Size: 0.8 Based on 4 random permutations The rate for group 0 is 1.79641730182344e-06 The rate for group 1 is 3.30041132572866e-06 Call: Observed Rate Ratio: 1.3 P-value: 0.2 Effect Size: 1 Based on 4 random permutations The rate for group A is 1.97486252507008e-06 The rate for group B is 2.5668204081711e-06 Effect sizes (Z-scores) jaw cranium NaN NaN Effect sizes for pairwise differences in phylogenetic signal. jaw cranium jaw 0 0 cranium 0 0 P-values jaw cranium jaw 1 1 cranium 1 1 Effect sizes Marsh.F Marsh.M Sinkhole.F Sinkhole.M 1.7029015 1.6050926 0.7329789 1.6652218 Effect sizes for pairwise differences in PLS effect size Marsh.F Marsh.M Sinkhole.F Sinkhole.M Marsh.F 0.0000000 1.0196175 0.55349166 1.22101418 Marsh.M 1.0196175 0.0000000 0.66904439 1.51181251 Sinkhole.F 0.5534917 0.6690444 0.00000000 0.01378201 Sinkhole.M 1.2210142 1.5118125 0.01378201 0.00000000 P-values Marsh.F Marsh.M Sinkhole.F Sinkhole.M Marsh.F 1.0000000 0.3079099 0.5799268 0.2220806 Marsh.M 0.3079099 1.0000000 0.5034672 0.1305816 Sinkhole.F 0.5799268 0.5034672 1.0000000 0.9890039 Sinkhole.M 0.2220806 0.1305816 0.9890039 1.0000000 Effect sizes Vrel.gp$Jord Vrel.gp$Teyah -0.2978931 -0.2642648 Effect sizes for pairwise differences in rel.eig effect size Vrel.gp$Jord Vrel.gp$Teyah Vrel.gp$Jord 0.00000000 0.09804249 Vrel.gp$Teyah 0.09804249 0.00000000 P-values Vrel.gp$Jord Vrel.gp$Teyah Vrel.gp$Jord 1.0000000 0.9218986 Vrel.gp$Teyah 0.9218986 1.0000000 Performing GPA | | | 0% | |================== | 25% | |=================================== | 50% | |======================================================================| 100% Making projections... Finished! Df SS MS Rsq EtaSq.ME SNR Z Pr(>SNR) Subjects 59 1.20209 0.0203744 0.96941 34.453 1.7166 0.25 Systematic ME 1 0.00303 0.0030348 0.00245 0.08002 0.087 1.7315 0.25 Random ME 59 0.03489 0.0005914 0.02814 0.91998 Total 119 1.24002 Df SS MS Rsq EtaSq.ME SNR Z Subjects 59 1.18779 0.0201320 0.95788 39.534 1.7321 Systematic ME 1 0.00303 0.0030348 0.00245 0.08002 0.101 1.7315 Systematic ME:Groups 2 0.00485 0.0024231 0.00391 0.12778 0.161 1.6841 Random ME 57 0.03004 0.0005271 0.02423 0.79220 Total 119 1.24002 Pr(>SNR) Subjects 0.25 Systematic ME 0.25 Systematic ME:Groups 0.25 Random ME Total If not apparent in the plot, the order of subjects on axes from 1 to 60 is: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" "16" 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31" "32" 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48" 49 50 51 52 53 54 55 56 57 58 59 60 "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60" Ordination type: Principal Component Analysis Centering by OLS mean Orthogonal projection of OLS residuals Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 0.0002720474 0.0001120524 0.0001084758 0.0000568924 Proportion of Variance 0.4564029477 0.1879858086 0.1819855633 0.0954461044 Cumulative Proportion 0.4564029477 0.6443887563 0.8263743196 0.9218204240 Comp5 Comp6 Comp7 Comp8 Eigenvalues 0.0000264508 1.260516e-05 5.959622e-06 1.584785e-06 Proportion of Variance 0.0443754550 2.114717e-02 9.998220e-03 2.658730e-03 Cumulative Proportion 0.9661958790 9.873431e-01 9.973413e-01 1.000000e+00 Ordination type: Principal Component Analysis Centering by OLS mean Orthogonal projection of OLS residuals Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 0.0002720474 0.0001120524 0.0001084758 0.0000568924 Proportion of Variance 0.4564029477 0.1879858086 0.1819855633 0.0954461044 Cumulative Proportion 0.4564029477 0.6443887563 0.8263743196 0.9218204240 Comp5 Comp6 Comp7 Comp8 Eigenvalues 0.0000264508 1.260516e-05 5.959622e-06 1.584785e-06 Proportion of Variance 0.0443754550 2.114717e-02 9.998220e-03 2.658730e-03 Cumulative Proportion 0.9661958790 9.873431e-01 9.973413e-01 1.000000e+00 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 2.720474e-04 1.120524e-04 1.084758e-04 Proportion Tips Dispersion 4.564029e-01 1.879858e-01 1.819856e-01 Cumulative Tips Dispersion 4.564029e-01 6.443888e-01 8.263743e-01 Ancestors Dispersion 3.073342e-05 2.320038e-05 2.039005e-05 Proportion Ancestors Dispersion 3.854327e-01 2.909597e-01 2.557149e-01 Cumulative Ancestors Dispersion 3.854327e-01 6.763923e-01 9.321072e-01 Comp4 Comp5 Comp6 Tips Dispersion 5.689240e-05 2.645080e-05 1.260516e-05 Proportion Tips Dispersion 9.544610e-02 4.437545e-02 2.114717e-02 Cumulative Tips Dispersion 9.218204e-01 9.661959e-01 9.873431e-01 Ancestors Dispersion 3.171758e-06 1.748508e-06 3.945777e-07 Proportion Ancestors Dispersion 3.977752e-02 2.192831e-02 4.948462e-03 Cumulative Ancestors Dispersion 9.718847e-01 9.938130e-01 9.987615e-01 Comp7 Comp8 Tips Dispersion 5.959622e-06 1.584785e-06 Proportion Tips Dispersion 9.998220e-03 2.658730e-03 Cumulative Tips Dispersion 9.973413e-01 1.000000e+00 Ancestors Dispersion 8.354868e-08 1.520561e-08 Proportion Ancestors Dispersion 1.047797e-03 1.906960e-04 Cumulative Ancestors Dispersion 9.998093e-01 1.000000e+00 Ordination type: Principal Component Analysis Centering by GLS mean Oblique projection of GLS-centered residuals Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 2.761405e-05 9.322868e-06 7.889847e-06 6.605120e-06 Proportion of Variance 4.855705e-01 1.639351e-01 1.387365e-01 1.161457e-01 Cumulative Proportion 4.855705e-01 6.495056e-01 7.882421e-01 9.043878e-01 Comp5 Comp6 Comp7 Comp8 Eigenvalues 3.017886e-06 1.203779e-06 9.895110e-07 2.262231e-07 Proportion of Variance 5.306707e-02 2.116747e-02 1.739975e-02 3.977950e-03 Cumulative Proportion 9.574548e-01 9.786223e-01 9.960221e-01 1.000000e+00 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 0.0002498092 8.395185e-05 1.057114e-04 Proportion Tips Dispersion 0.4190949497 1.408427e-01 1.773479e-01 Cumulative Tips Dispersion 0.4190949497 5.599376e-01 7.372855e-01 Ancestors Dispersion 0.0000169982 9.862682e-06 2.194379e-05 Proportion Ancestors Dispersion 0.2131770701 1.236895e-01 2.752006e-01 Cumulative Ancestors Dispersion 0.2131770701 3.368665e-01 6.120671e-01 Comp4 Comp5 Comp6 Tips Dispersion 0.0001027383 2.765308e-05 1.675548e-05 Proportion Tips Dispersion 0.1723598707 4.639247e-02 2.811000e-02 Cumulative Tips Dispersion 0.9096453329 9.560378e-01 9.841478e-01 Ancestors Dispersion 0.0000272722 1.114653e-06 1.772175e-06 Proportion Ancestors Dispersion 0.3420249711 1.397905e-02 2.222513e-02 Cumulative Ancestors Dispersion 0.9540920945 9.680711e-01 9.902963e-01 Comp7 Comp8 Tips Dispersion 7.598972e-06 1.850019e-06 Proportion Tips Dispersion 1.274849e-02 3.103703e-03 Cumulative Tips Dispersion 9.968963e-01 1.000000e+00 Ancestors Dispersion 7.068511e-07 6.689930e-08 Proportion Ancestors Dispersion 8.864732e-03 8.389947e-04 Cumulative Ancestors Dispersion 9.991610e-01 1.000000e+00 Ordination type: Principal Component Analysis Centering by GLS mean GLS residuals transformed for orthogonal projection Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 2.761405e-05 9.322868e-06 7.889847e-06 6.605120e-06 Proportion of Variance 4.855705e-01 1.639351e-01 1.387365e-01 1.161457e-01 Cumulative Proportion 4.855705e-01 6.495056e-01 7.882421e-01 9.043878e-01 Comp5 Comp6 Comp7 Comp8 Eigenvalues 3.017886e-06 1.203779e-06 9.895110e-07 2.262231e-07 Proportion of Variance 5.306707e-02 2.116747e-02 1.739975e-02 3.977950e-03 Cumulative Proportion 9.574548e-01 9.786223e-01 9.960221e-01 1.000000e+00 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 2.740155e-05 8.385080e-06 7.889441e-06 Proportion Tips Dispersion 4.933393e-01 1.509655e-01 1.420420e-01 Cumulative Tips Dispersion 4.933393e-01 6.443048e-01 7.863468e-01 Ancestors Dispersion 1.604482e-06 8.942699e-07 1.578102e-06 Proportion Ancestors Dispersion 3.034061e-01 1.691056e-01 2.984176e-01 Cumulative Ancestors Dispersion 3.034061e-01 4.725117e-01 7.709293e-01 Comp4 Comp5 Comp6 Tips Dispersion 6.590826e-06 2.909294e-06 1.175603e-06 Proportion Tips Dispersion 1.186617e-01 5.237912e-02 2.116563e-02 Cumulative Tips Dispersion 9.050085e-01 9.573876e-01 9.785532e-01 Ancestors Dispersion 1.007063e-06 6.196138e-08 1.114791e-07 Proportion Ancestors Dispersion 1.904346e-01 1.171684e-02 2.108060e-02 Cumulative Ancestors Dispersion 9.613640e-01 9.730808e-01 9.941614e-01 Comp7 Comp8 Tips Dispersion 9.878523e-07 2.033663e-07 Proportion Tips Dispersion 1.778536e-02 3.661419e-03 Cumulative Tips Dispersion 9.963386e-01 1.000000e+00 Ancestors Dispersion 2.711996e-08 3.755919e-09 Proportion Ancestors Dispersion 5.128360e-03 7.102409e-04 Cumulative Ancestors Dispersion 9.992898e-01 1.000000e+00 Ordination type: Alignment to an alternative matrix Alignment matrix: phy Centering by OLS mean OLS residuals Alignment to phy means residual projection is not orthogonal. Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Singular Value 0.0003931629 0.0001155198 5.091258e-05 2.660786e-05 Proportion of Covariance 0.6595936040 0.1938028696 8.541399e-02 4.463894e-02 Cumulative Proportion 0.6595936040 0.8533964737 9.388105e-01 9.834494e-01 RV by Component 0.0993211764 0.0291827102 1.286158e-02 6.721702e-03 Cumulative RV 0.0993211764 0.1285038867 1.413655e-01 1.480872e-01 Comp5 Comp6 Comp7 Comp8 Singular Value 6.598675e-06 2.351469e-06 7.108871e-07 2.042572e-07 Proportion of Covariance 1.107033e-02 3.944966e-03 1.192627e-03 3.426742e-04 Cumulative Proportion 9.945197e-01 9.984647e-01 9.996573e-01 1.000000e+00 RV by Component 1.666963e-03 5.940304e-04 1.795850e-04 5.159965e-05 Cumulative RV 1.497541e-01 1.503482e-01 1.505278e-01 1.505794e-01 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 2.301865e-04 1.124810e-04 1.133102e-04 Proportion Tips Dispersion 3.861747e-01 1.887049e-01 1.900960e-01 Cumulative Tips Dispersion 3.861747e-01 5.748795e-01 7.649755e-01 Ancestors Dispersion 4.069158e-05 2.437196e-05 8.067595e-06 Proportion Ancestors Dispersion 5.103196e-01 3.056526e-01 1.011770e-01 Cumulative Ancestors Dispersion 5.103196e-01 8.159722e-01 9.171492e-01 Comp4 Comp5 Comp6 Tips Dispersion 7.526878e-05 4.046474e-05 1.571235e-05 Proportion Tips Dispersion 1.262754e-01 6.788606e-02 2.635999e-02 Cumulative Tips Dispersion 8.912509e-01 9.591370e-01 9.854970e-01 Ancestors Dispersion 6.244464e-06 3.539330e-07 3.508799e-09 Proportion Ancestors Dispersion 7.831282e-02 4.438730e-03 4.400441e-05 Cumulative Ancestors Dispersion 9.954620e-01 9.999008e-01 9.999448e-01 Comp7 Comp8 Tips Dispersion 6.396433e-06 2.248360e-06 Proportion Tips Dispersion 1.073104e-02 3.771984e-03 Cumulative Tips Dispersion 9.962280e-01 1.000000e+00 Ancestors Dispersion 2.594059e-09 1.810230e-09 Proportion Ancestors Dispersion 3.253251e-05 2.270238e-05 Cumulative Ancestors Dispersion 9.999773e-01 1.000000e+00 Ordination type: Alignment to an alternative matrix Alignment matrix: phy Centering by GLS mean GLS-centered residuals, not transformed Alignment to phy means residual projection is not orthogonal. Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Singular Value 4.093955e-05 8.496891e-06 3.846098e-06 1.712760e-06 Proportion of Covariance 7.198888e-01 1.494109e-01 6.763051e-02 3.011750e-02 Cumulative Proportion 7.198888e-01 8.692997e-01 9.369302e-01 9.670477e-01 RV by Component 3.478334e-01 2.941678e-02 1.180673e-02 7.103513e-03 Cumulative RV 3.478334e-01 3.772502e-01 3.890569e-01 3.961604e-01 Comp5 Comp6 Comp7 Comp8 Singular Value 1.409129e-06 3.002188e-07 1.044332e-07 6.019398e-08 Proportion of Covariance 2.477838e-02 5.279104e-03 1.836373e-03 1.058462e-03 Cumulative Proportion 9.918261e-01 9.971052e-01 9.989415e-01 1.000000e+00 RV by Component 1.521884e-03 5.394338e-04 1.631937e-04 4.681627e-05 Cumulative RV 3.976823e-01 3.982218e-01 3.983850e-01 3.984318e-01 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 1.984349e-04 1.385956e-04 1.134850e-04 Proportion Tips Dispersion 3.329063e-01 2.325162e-01 1.903892e-01 Cumulative Tips Dispersion 3.329063e-01 5.654225e-01 7.558117e-01 Ancestors Dispersion 2.670575e-05 2.563105e-05 1.045586e-05 Proportion Ancestors Dispersion 3.349211e-01 3.214430e-01 1.311286e-01 Cumulative Ancestors Dispersion 3.349211e-01 6.563641e-01 7.874927e-01 Comp4 Comp5 Comp6 Tips Dispersion 6.989984e-05 5.027941e-05 1.608390e-05 Proportion Tips Dispersion 1.172682e-01 8.435176e-02 2.698331e-02 Cumulative Tips Dispersion 8.730799e-01 9.574317e-01 9.844150e-01 Ancestors Dispersion 9.518248e-06 6.011721e-06 9.181389e-07 Proportion Ancestors Dispersion 1.193699e-01 7.539395e-02 1.151453e-02 Cumulative Ancestors Dispersion 9.068625e-01 9.822565e-01 9.937710e-01 Comp7 Comp8 Tips Dispersion 6.451805e-06 2.837941e-06 Proportion Tips Dispersion 1.082393e-02 4.761099e-03 Cumulative Tips Dispersion 9.952389e-01 1.000000e+00 Ancestors Dispersion 3.629411e-07 1.337427e-07 Proportion Ancestors Dispersion 4.551702e-03 1.677288e-03 Cumulative Ancestors Dispersion 9.983227e-01 1.000000e+00 Ordination type: Alignment to an alternative matrix Alignment matrix: phy Centering by GLS mean GLS residuals transformed Alignment to phy means residual projection is not orthogonal. Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Singular Value 4.093955e-05 8.496891e-06 3.846098e-06 1.712760e-06 Proportion of Covariance 7.198888e-01 1.494109e-01 6.763051e-02 3.011750e-02 Cumulative Proportion 7.198888e-01 8.692997e-01 9.369302e-01 9.670477e-01 RV by Component 1.248320e-01 2.590853e-02 1.172744e-02 5.222511e-03 Cumulative RV 1.248320e-01 1.507405e-01 1.624680e-01 1.676905e-01 Comp5 Comp6 Comp7 Comp8 Singular Value 1.409129e-06 3.002188e-07 1.044332e-07 6.019398e-08 Proportion of Covariance 2.477838e-02 5.279104e-03 1.836373e-03 1.058462e-03 Cumulative Proportion 9.918261e-01 9.971052e-01 9.989415e-01 1.000000e+00 RV by Component 4.296684e-03 9.154207e-04 3.184354e-04 1.835422e-04 Cumulative RV 1.719872e-01 1.729026e-01 1.732210e-01 1.734046e-01 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 1.444116e-05 1.274219e-05 1.272668e-05 Proportion Tips Dispersion 2.599995e-01 2.294112e-01 2.291320e-01 Cumulative Tips Dispersion 2.599995e-01 4.894107e-01 7.185427e-01 Ancestors Dispersion 1.735351e-06 2.256490e-06 6.767762e-07 Proportion Ancestors Dispersion 3.281533e-01 4.267002e-01 1.279778e-01 Cumulative Ancestors Dispersion 3.281533e-01 7.548535e-01 8.828313e-01 Comp4 Comp5 Comp6 Tips Dispersion 7.046498e-06 4.980748e-06 2.127778e-06 Proportion Tips Dispersion 1.268656e-01 8.967371e-02 3.830865e-02 Cumulative Tips Dispersion 8.454083e-01 9.350821e-01 9.733907e-01 Ancestors Dispersion 2.276556e-07 3.818644e-07 6.284842e-09 Proportion Ancestors Dispersion 4.304947e-02 7.221021e-02 1.188458e-03 Cumulative Ancestors Dispersion 9.258808e-01 9.980910e-01 9.992794e-01 Comp7 Comp8 Tips Dispersion 1.093977e-06 3.839834e-07 Proportion Tips Dispersion 1.969603e-02 6.913263e-03 Cumulative Tips Dispersion 9.930867e-01 1.000000e+00 Ancestors Dispersion 8.928781e-10 2.917607e-09 Proportion Ancestors Dispersion 1.688424e-04 5.517169e-04 Cumulative Ancestors Dispersion 9.994483e-01 1.000000e+00 Call: gpagen(A = plethodon$land, PrinAxes = FALSE, print.progress = F) Generalized Procrustes Analysis with Partial Procrustes Superimposition 12 fixed landmarks 0 semilandmarks (sliders) 2-dimensional landmarks 2 GPA iterations to converge Consensus (mean) Configuration X Y 1 0.15263287 -0.023350400 2 0.19445064 -0.092643121 3 -0.03361223 -0.007346212 4 -0.28069721 -0.092850147 5 -0.30998751 -0.061672264 6 -0.32557841 -0.036112255 7 -0.31804390 0.036125318 8 -0.18947114 0.098010902 9 0.02036829 0.099112877 10 0.18852641 0.077278061 11 0.35134314 0.065866346 12 0.55006905 -0.062419105 Call: gpagen(A = hummingbirds$land, curves = hummingbirds$curvepts, ProcD = FALSE, print.progress = F) Generalized Procrustes Analysis with Partial Procrustes Superimposition 10 fixed landmarks 15 semilandmarks (sliders) 2-dimensional landmarks 11 GPA iterations to converge Minimized Bending Energy used Consensus (mean) Configuration X Y 1 -0.23340453 -0.004150126 2 -0.23437642 -0.008073562 3 -0.13537856 0.009746653 4 -0.10887364 0.011238582 5 0.26393927 0.015493692 6 0.24590730 0.015048673 7 0.27566044 0.023298007 8 0.26748616 -0.029997538 9 0.29156223 -0.006599804 10 0.35241626 -0.024707450 11 -0.20987595 0.005563094 12 -0.17992668 0.012558224 13 -0.15199371 0.015891895 14 0.04417872 0.024479691 15 0.15109354 0.025897899 16 -0.20917012 -0.004128421 17 -0.17798781 -0.001793039 18 -0.15107528 -0.001286613 19 0.04022402 -0.001224994 20 0.14967214 -0.004713315 21 -0.20967275 -0.011393041 22 -0.17729851 -0.014580239 23 -0.14937194 -0.015398253 24 0.03963382 -0.011429485 25 0.14897835 -0.016626651 Call: gpagen(A = hummingbirds$land, curves = hummingbirds$curvepts, ProcD = TRUE, print.progress = F) Generalized Procrustes Analysis with Partial Procrustes Superimposition 10 fixed landmarks 15 semilandmarks (sliders) 2-dimensional landmarks 11 GPA iterations to converge Minimized squared Procrustes Distance used Consensus (mean) Configuration X Y 1 -0.23028526 -0.003994116 2 -0.23124541 -0.007839311 3 -0.13357583 0.009669288 4 -0.10742820 0.011126953 5 0.26035775 0.015162379 6 0.24256782 0.014731020 7 0.27192260 0.022857806 8 0.26382105 -0.029710933 9 0.28759112 -0.006639457 10 0.34760860 -0.024530122 11 -0.21094248 0.004175427 12 -0.18219203 0.011505800 13 -0.15202026 0.015605897 14 0.06302129 0.024709217 15 0.15648134 0.025342288 16 -0.21055088 -0.004151996 17 -0.18098052 -0.001971807 18 -0.15153441 -0.001191697 19 0.06135175 -0.001527170 20 0.15617421 -0.005369971 21 -0.21097576 -0.010667752 22 -0.18032943 -0.013802158 23 -0.15015648 -0.015202822 24 0.06092370 -0.011474702 25 0.15555003 -0.017250320 Call: gpagen(A = scallops$coorddata, curves = scallops$curvslide, surfaces = scallops$surfslide, print.progress = F) Generalized Procrustes Analysis with Partial Procrustes Superimposition 5 fixed landmarks 41 semilandmarks (sliders) 3-dimensional landmarks 11 GPA iterations to converge Minimized Bending Energy used Consensus (mean) Configuration X Y Z 1 -0.081780117 -0.120649720 -0.013977818 2 -0.039953533 -0.177916095 -0.024234317 3 0.039604000 -0.160440163 -0.022417763 4 0.132919606 -0.133490989 -0.025807230 5 0.127198190 -0.067987876 -0.014134425 6 0.169258695 -0.016217526 -0.013593941 7 0.168368662 0.043219300 -0.013823570 8 0.140157576 0.098977257 -0.015511859 9 0.096017436 0.142176222 -0.015176381 10 0.025136754 0.171655851 -0.017453082 11 -0.064407941 0.167571107 -0.018609625 12 -0.129462084 0.128067060 -0.020231740 13 -0.169757091 0.071191136 -0.018060075 14 -0.187363618 0.007019624 -0.014599501 15 -0.174360560 -0.057347543 -0.010431263 16 -0.131721141 -0.101318708 -0.011123347 17 -0.017066338 0.097038046 0.019050904 18 0.014631984 -0.105369309 0.028294452 19 0.096193027 0.123213676 -0.006467546 20 -0.065218360 -0.113075793 0.002323316 21 -0.151912664 0.058249006 -0.005885301 22 0.054706372 0.086399926 0.017076445 23 -0.053042901 -0.004104540 0.031188472 24 -0.076985953 0.059797766 0.019932924 25 -0.116534701 0.109289479 -0.007326455 26 0.006345201 0.034537566 0.034535349 27 0.145650922 0.069974238 -0.007130645 28 0.123931601 -0.069173833 -0.005679803 29 0.054120100 -0.143484306 -0.005942573 30 -0.169032816 -0.009087492 -0.005884073 31 0.067644310 0.011389561 0.029396400 32 -0.048256352 -0.070434564 0.026056576 33 0.108173627 -0.121082480 -0.020241788 34 0.120421491 -0.015801737 0.010215008 35 0.161204502 0.003381768 -0.007139072 36 0.076978828 -0.102337642 0.011509812 37 0.017805486 -0.036118780 0.040677039 38 0.110866923 0.050077560 0.010180639 39 -0.051963323 0.149216191 -0.005928480 40 0.031432691 0.150662670 -0.004791532 41 -0.104029785 -0.046857281 0.013478368 42 -0.112261648 -0.098358799 -0.002420617 43 -0.150982107 -0.064993539 -0.005060494 44 -0.017128647 -0.147179813 -0.012242780 45 -0.117052539 0.009475282 0.013937921 46 0.077494472 -0.053728947 0.027903885 Call: integration.test(A = Y.gpa$coords, partition.gp = land.gps, iter = 3, print.progress = F) r-PLS: 1 Effect Size (Z): 1.2 P-value: 0.25 Based on 4 random permutations $Re.obs [1] 0.2048115 $Z.obs [1] -0.6782447 $ZR [1] 1.092893 $ZR.var [1] 0.02702703 attr(,"class") [1] "rel.eig" Call: modularity.test(A = Y.gpa$coords, partition.gp = land.gps, iter = 3, CI = FALSE, opt.rot = FALSE, print.progress = F) CR: 0.9 P-value: 0.2 Effect Size: -1.7 Based on 4 random permutationsNo factor in formula or model terms from which to define groups. Procrustes variance: 0.004923493 No factor in formula or model terms from which to define groups. Procrustes variance: 0.004663261 Call: phylo.integration(A = Y.gpa$coords, phy = plethspecies$phy, partition.gp = land.gps, iter = 3, print.progress = F) r-PLS: 1 Effect Size (Z): 1.1 P-value: 0.25 Based on 4 random permutations Call: phylo.modularity(A = Y.gpa$coords, partition.gp = land.gps, phy = plethspecies$phy, CI = FALSE, iter = 3, print.progress = F) CR: 1.2 P-value: 0.8 Effect Size: 1 Based on 4 random permutations Call: physignal(A = Y.gpa$coords, phy = plethspecies$phy, iter = 3, print.progress = F) Observed Phylogenetic Signal (K): 1 P-value: 0.2 Based on 4 random permutations Use physignal.z to estimate effect size.Data in either A1 or A2 do not have names. It is assumed data in both A1 and A2 are ordered the same. Coefficients test not available because you either turbo-charged your model fit or used verbose = FALSE. Go back to lm.rrpp and choose turbo = FALSE & verbose = TRUE if you wish to test coefficients. Performimg pairwise comparisons of disparity | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Please be aware that printing progress slows down the analysis (perhaps slightly). Preliminary Model Fit... Sums of Squares calculations: 4 permutations. | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Data in either A1 or A2 do not have names. It is assumed data in both A1 and A2 are ordered the same. Ordination type: Principal Component Analysis Centering by OLS mean Orthogonal projection of OLS residuals Number of observations: 9 Number of vectors 8 Importance of Components: Comp1 Comp2 Comp3 Comp4 Eigenvalues 0.0002720474 0.0001120524 0.0001084758 0.0000568924 Proportion of Variance 0.4564029477 0.1879858086 0.1819855633 0.0954461044 Cumulative Proportion 0.4564029477 0.6443887563 0.8263743196 0.9218204240 Comp5 Comp6 Comp7 Comp8 Eigenvalues 0.0000264508 1.260516e-05 5.959622e-06 1.584785e-06 Proportion of Variance 0.0443754550 2.114717e-02 9.998220e-03 2.658730e-03 Cumulative Proportion 0.9661958790 9.873431e-01 9.973413e-01 1.000000e+00 Dispersion (variance) of points, after projection: Comp1 Comp2 Comp3 Tips Dispersion 2.720474e-04 1.120524e-04 1.084758e-04 Proportion Tips Dispersion 4.564029e-01 1.879858e-01 1.819856e-01 Cumulative Tips Dispersion 4.564029e-01 6.443888e-01 8.263743e-01 Ancestors Dispersion 3.073342e-05 2.320038e-05 2.039005e-05 Proportion Ancestors Dispersion 3.854327e-01 2.909597e-01 2.557149e-01 Cumulative Ancestors Dispersion 3.854327e-01 6.763923e-01 9.321072e-01 Comp4 Comp5 Comp6 Tips Dispersion 5.689240e-05 2.645080e-05 1.260516e-05 Proportion Tips Dispersion 9.544610e-02 4.437545e-02 2.114717e-02 Cumulative Tips Dispersion 9.218204e-01 9.661959e-01 9.873431e-01 Ancestors Dispersion 3.171758e-06 1.748508e-06 3.945777e-07 Proportion Ancestors Dispersion 3.977752e-02 2.192831e-02 4.948462e-03 Cumulative Ancestors Dispersion 9.718847e-01 9.938130e-01 9.987615e-01 Comp7 Comp8 Tips Dispersion 5.959622e-06 1.584785e-06 Proportion Tips Dispersion 9.998220e-03 2.658730e-03 Cumulative Tips Dispersion 9.973413e-01 1.000000e+00 Ancestors Dispersion 8.354868e-08 1.520561e-08 Proportion Ancestors Dispersion 1.047797e-03 1.906960e-04 Cumulative Ancestors Dispersion 9.998093e-01 1.000000e+00 Data in either A1 or A2 do not have names. It is assumed data in both A1 and A2 are ordered the same. Call: two.b.pls(A1 = Y.gpa$coords, A2 = plethShapeFood$food, iter = 3, print.progress = F) r-PLS: 1 Effect Size (Z): 1.7 P-value: 0.25 Based on 4 random permutations Observed Phylogenetic Signal (traceK): 5 Observed Effect Size (Z-traceK): 1.7 P-value (traceK): 0.2 Observed Phylogenetic Signal (detK): 0.04 Observed Effect Size (Z-detK): 1.2 P-value (detK): 0.2 Observed Phylogenetic Signal (Kmult): 0.5 Observed Effect Size (Z-Kmult): 1.4 P-value (Kmult): 0.2 Based on 4 random permutations[ FAIL 0 | WARN 0 | SKIP 1 | PASS 248 ] ══ Skipped tests (1) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-geomorph.R:580:3' [ FAIL 0 | WARN 0 | SKIP 1 | PASS 248 ] > > proc.time() user system elapsed 25.46 1.45 26.90