R Under development (unstable) (2024-06-22 r86814 ucrt) -- "Unsuffered Consequences"
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> library(testthat)
> library(RRPP)
>
> test_check("geomorph")
Loading required package: geomorph
Loading required package: rgl
Loading required package: Matrix
Initial GPA
Performing GPA
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Shape Analysis
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Size Analysis
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Call:
bilat.symmetry(A = wingshape, ind = ind, side = side, replicate = replicate,
object.sym = FALSE, data = gdf, iter = 3, RRPP = TRUE)
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
Performing GPA
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Shape Analysis
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Size Analysis
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Call:
bilat.symmetry(A = Y.gpa, ind = mosquito$ind, side = mosquito$side,
replicate = mosquito$replicate, object.sym = FALSE, iter = 3,
RRPP = TRUE)
Symmetry (data) type: Matching
Type I (Sequential) Sums of Squares and Cross-products
Randomized Residual Permutation Procedure Used
4 Permutations
Shape ANOVA
Df SS MS Rsq F Z Pr(>F)
ind 9 0.104888 0.0116542 0.45533 2.6901 1.42668 0.25
side 1 0.003221 0.0032209 0.01398 0.7435 -0.87935 0.75
ind:side 9 0.038990 0.0043323 0.16926 1.0407 0.84797 0.50
ind:side:replicate 20 0.083259 0.0041629 0.36143
Total 39 0.230358
Centroid Size ANOVA
Df SS MS Rsq F Z Pr(>F)
ind 9 4.1496e-09 4.6107e-10 0.18555 0.8452 -0.46137 0.75
side 1 3.4740e-10 3.4738e-10 0.01553 0.6368 1.64429 0.25
ind:side 9 6.9569e-09 7.7299e-10 0.31108 1.4171 1.66837 0.25
ind:side:replicate 20 1.0910e-08 5.4549e-10 0.48784
Total 39 2.2364e-08
Initial GPA
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Object Symmetry GPA
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Shape Analysis
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Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Call:
bilat.symmetry(A = shape, ind = ind, replicate = rep, object.sym = TRUE,
land.pairs = lizards$lm.pairs, data = gdf, iter = 3, RRPP = TRUE)
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
Initial GPA
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Making projections... Finished!
Object Symmetry GPA
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Shape Analysis
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Call:
bilat.symmetry(A = shape, ind = ind, object.sym = TRUE, land.pairs = scallops$land.pairs,
data = gdf, iter = 3, RRPP = TRUE, curves = scallops$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
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Making projections... Finished!
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Making projections... Finished!
No CS sets input. Final configurations will not be scaled
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Making projections... Finished!
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
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Making projections... Finished!
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
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Making projections... Finished!
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
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Making projections... Finished!
NOTE: more negative effects represent stronger modular signal!
Effect sizes
Marsh.F Marsh.M Sinkhole.F Sinkhole.M
-1.220186 -1.494807 -1.449663 -1.447810
Effect sizes for pairwise differences in CR effect size
Marsh.F Marsh.M Sinkhole.F Sinkhole.M
Marsh.F 0.0000000 1.213806390 1.178244938 0.9950705
Marsh.M 1.2138064 0.000000000 0.001333799 0.4840085
Sinkhole.F 1.1782449 0.001333799 0.000000000 0.4723248
Sinkhole.M 0.9950705 0.484008508 0.472324771 0.0000000
P-values
Marsh.F Marsh.M Sinkhole.F Sinkhole.M
Marsh.F 1.0000000 0.2248217 0.2386990 0.3197020
Marsh.M 0.2248217 1.0000000 0.9989358 0.6283798
Sinkhole.F 0.2386990 0.9989358 1.0000000 0.6366950
Sinkhole.M 0.3197020 0.6283798 0.6366950 1.0000000
Performing GPA
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NOTE: more negative effects represent stronger modular signal!
Effect sizes
No_Modules modul.tests$Marsh.F m3.test m4.test
0.000000 -1.220186 -1.619638 -1.717251
Effect sizes for pairwise differences in CR effect size
No_Modules modul.tests$Marsh.F m3.test m4.test
No_Modules 0.000000 1.220186 1.6196383 1.7172513
modul.tests$Marsh.F 1.220186 0.000000 1.2973233 1.5333825
m3.test 1.619638 1.297323 0.0000000 0.6565321
m4.test 1.717251 1.533383 0.6565321 0.0000000
P-values
No_Modules modul.tests$Marsh.F m3.test m4.test
No_Modules 1.00000000 0.2223945 0.1053100 0.08593326
modul.tests$Marsh.F 0.22239445 1.0000000 0.1945200 0.12518163
m3.test 0.10531000 0.1945200 1.0000000 0.51148183
m4.test 0.08593326 0.1251816 0.5114818 1.00000000
Performing GPA
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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
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Call:
Observed Rate Ratio: 1.2997
P-value: 0.461
Effect Size: 0.112
Based on 1000 random permutations
The rate for group A is 1.97486252507008e-06
The rate for group B is 2.5668204081711e-06
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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
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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
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Df SS MS Rsq EtaSq.ME SNR Z Pr(>SNR)
Subjects 59 1.20209 0.0203744 0.96941 34.453 20.6317 0.001
Systematic ME 1 0.00303 0.0030348 0.00245 0.08002 0.087 4.5374 0.001
Random ME 59 0.03489 0.0005914 0.02814 0.91998
Total 119 1.24002
Subjects ***
Systematic ME ***
Random ME
Total
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df SS MS Rsq EtaSq.ME SNR Z
Subjects 59 1.18779 0.0201320 0.95788 39.534 17.7458
Systematic ME 1 0.00303 0.0030348 0.00245 0.08002 0.101 4.6930
Systematic ME:Groups 2 0.00485 0.0024231 0.00391 0.12778 0.161 6.5764
Random ME 57 0.03004 0.0005271 0.02423 0.79220
Total 119 1.24002
Pr(>SNR)
Subjects 0.001 ***
Systematic ME 0.001 ***
Systematic ME:Groups 0.001 ***
Random ME
Total
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Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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"
Performing GPA
|
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Making projections... Finished!
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
Performing GPA
|
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Making projections... Finished!
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 5.562296e-05 6.115844e-06 1.946905e-06
Proportion Ancestors Dispersion 7.523783e-01 8.272534e-02 2.633462e-02
Cumulative Ancestors Dispersion 7.523783e-01 8.351036e-01 8.614383e-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 7.984540e-06 1.524978e-06 2.962999e-07
Proportion Ancestors Dispersion 1.080021e-01 2.062746e-02 4.007870e-03
Cumulative Ancestors Dispersion 9.694403e-01 9.900678e-01 9.940757e-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 1.945159e-07 2.434675e-07
Proportion Ancestors Dispersion 2.631099e-03 3.293238e-03
Cumulative Ancestors Dispersion 9.967068e-01 1.000000e+00
Performing GPA
|
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Making projections... Finished!
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 2.498092e-04 8.395185e-05 1.057114e-04
Proportion Tips Dispersion 4.190949e-01 1.408427e-01 1.773479e-01
Cumulative Tips Dispersion 4.190949e-01 5.599376e-01 7.372855e-01
Ancestors Dispersion 5.035969e-05 6.581394e-06 6.772535e-06
Proportion Ancestors Dispersion 6.811852e-01 8.902256e-02 9.160800e-02
Cumulative Ancestors Dispersion 6.811852e-01 7.702078e-01 8.618158e-01
Comp4 Comp5 Comp6
Tips Dispersion 1.027383e-04 2.765308e-05 1.675548e-05
Proportion Tips Dispersion 1.723599e-01 4.639247e-02 2.811000e-02
Cumulative Tips Dispersion 9.096453e-01 9.560378e-01 9.841478e-01
Ancestors Dispersion 8.668589e-06 7.070514e-07 3.112562e-07
Proportion Ancestors Dispersion 1.172548e-01 9.563859e-03 4.210175e-03
Cumulative Ancestors Dispersion 9.790705e-01 9.886344e-01 9.928446e-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 2.791855e-07 2.498109e-07
Proportion Ancestors Dispersion 3.776375e-03 3.379042e-03
Cumulative Ancestors Dispersion 9.966210e-01 1.000000e+00
Performing GPA
|
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Making projections... Finished!
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 6.320992e-06 4.853117e-07 4.951270e-07
Proportion Ancestors Dispersion 7.646264e-01 5.870631e-02 5.989363e-02
Cumulative Ancestors Dispersion 7.646264e-01 8.233327e-01 8.832264e-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 8.025265e-07 6.382265e-08 1.498384e-08
Proportion Ancestors Dispersion 9.707858e-02 7.720384e-03 1.812539e-03
Cumulative Ancestors Dispersion 9.803049e-01 9.880253e-01 9.898379e-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 6.135027e-08 2.265777e-08
Proportion Ancestors Dispersion 7.421309e-03 2.740825e-03
Cumulative Ancestors Dispersion 9.972592e-01 1.000000e+00
Performing GPA
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Making projections... Finished!
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 3.820665e-05 9.237298e-06 9.698713e-06
Proportion Ancestors Dispersion 5.167983e-01 1.249474e-01 1.311886e-01
Cumulative Ancestors Dispersion 5.167983e-01 6.417457e-01 7.729343e-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 9.827479e-06 5.409542e-06 8.233072e-07
Proportion Ancestors Dispersion 1.329304e-01 7.317162e-02 1.113638e-02
Cumulative Ancestors Dispersion 9.058647e-01 9.790363e-01 9.901727e-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 4.755226e-07 2.510041e-07
Proportion Ancestors Dispersion 6.432108e-03 3.395182e-03
Cumulative Ancestors Dispersion 9.966048e-01 1.000000e+00
Performing GPA
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Making projections... Finished!
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 0.0001984349 1.385956e-04 1.134850e-04
Proportion Tips Dispersion 0.3329062846 2.325162e-01 1.903892e-01
Cumulative Tips Dispersion 0.3329062846 5.654225e-01 7.558117e-01
Ancestors Dispersion 0.0000336853 2.195529e-05 4.151043e-06
Proportion Ancestors Dispersion 0.4556407589 2.969761e-01 5.614866e-02
Cumulative Ancestors Dispersion 0.4556407589 7.526168e-01 8.087655e-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 8.579230e-06 4.046362e-06 1.082408e-06
Proportion Ancestors Dispersion 1.160461e-01 5.473270e-02 1.464108e-02
Cumulative Ancestors Dispersion 9.248116e-01 9.795443e-01 9.941853e-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 1.788164e-07 2.510583e-07
Proportion Ancestors Dispersion 2.418742e-03 3.395914e-03
Cumulative Ancestors Dispersion 9.966041e-01 1.000000e+00
Performing GPA
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Making projections... Finished!
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 3.289509e-06 2.105133e-06 9.812396e-07
Proportion Ancestors Dispersion 3.979194e-01 2.546500e-01 1.186968e-01
Cumulative Ancestors Dispersion 3.979194e-01 6.525694e-01 7.712662e-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 1.160321e-06 5.436370e-07 1.088818e-07
Proportion Ancestors Dispersion 1.403597e-01 6.576170e-02 1.317102e-02
Cumulative Ancestors Dispersion 9.116259e-01 9.773876e-01 9.905586e-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 6.245051e-08 1.559935e-08
Proportion Ancestors Dispersion 7.554401e-03 1.886995e-03
Cumulative Ancestors Dispersion 9.981130e-01 1.000000e+00
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Call:
gpagen(A = plethodon$land, PrinAxes = FALSE)
Generalized Procrustes Analysis
with Partial Procrustes Superimposition
12 fixed landmarks
0 semilandmarks (sliders)
2-dimensional landmarks
2 GPA iterations to converge
Consensus (mean) Configuration
X Y
1 0.15263287 -0.023350400
2 0.19445064 -0.092643121
3 -0.03361223 -0.007346212
4 -0.28069721 -0.092850147
5 -0.30998751 -0.061672264
6 -0.32557841 -0.036112255
7 -0.31804390 0.036125318
8 -0.18947114 0.098010902
9 0.02036829 0.099112877
10 0.18852641 0.077278061
11 0.35134314 0.065866346
12 0.55006905 -0.062419105
Performing GPA
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Making projections... Finished!
Call:
gpagen(A = hummingbirds$land, curves = hummingbirds$curvepts,
ProcD = FALSE)
Generalized Procrustes Analysis
with Partial Procrustes Superimposition
10 fixed landmarks
15 semilandmarks (sliders)
2-dimensional landmarks
11 GPA iterations to converge
Minimized Bending Energy used
Consensus (mean) Configuration
X Y
1 -0.23340453 -0.004150126
2 -0.23437642 -0.008073562
3 -0.13537856 0.009746653
4 -0.10887364 0.011238582
5 0.26393927 0.015493692
6 0.24590730 0.015048673
7 0.27566044 0.023298007
8 0.26748616 -0.029997538
9 0.29156223 -0.006599804
10 0.35241626 -0.024707450
11 -0.20987595 0.005563094
12 -0.17992668 0.012558224
13 -0.15199371 0.015891895
14 0.04417872 0.024479691
15 0.15109354 0.025897899
16 -0.20917012 -0.004128421
17 -0.17798781 -0.001793039
18 -0.15107528 -0.001286613
19 0.04022402 -0.001224994
20 0.14967214 -0.004713315
21 -0.20967275 -0.011393041
22 -0.17729851 -0.014580239
23 -0.14937194 -0.015398253
24 0.03963382 -0.011429485
25 0.14897835 -0.016626651
Performing GPA
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Making projections... Finished!
Call:
gpagen(A = hummingbirds$land, curves = hummingbirds$curvepts,
ProcD = TRUE)
Generalized Procrustes Analysis
with Partial Procrustes Superimposition
10 fixed landmarks
15 semilandmarks (sliders)
2-dimensional landmarks
11 GPA iterations to converge
Minimized squared Procrustes Distance used
Consensus (mean) Configuration
X Y
1 -0.23028526 -0.003994116
2 -0.23124541 -0.007839311
3 -0.13357583 0.009669288
4 -0.10742820 0.011126953
5 0.26035775 0.015162379
6 0.24256782 0.014731020
7 0.27192260 0.022857806
8 0.26382105 -0.029710933
9 0.28759112 -0.006639457
10 0.34760860 -0.024530122
11 -0.21094248 0.004175427
12 -0.18219203 0.011505800
13 -0.15202026 0.015605897
14 0.06302129 0.024709217
15 0.15648134 0.025342288
16 -0.21055088 -0.004151996
17 -0.18098052 -0.001971807
18 -0.15153441 -0.001191697
19 0.06135175 -0.001527170
20 0.15617421 -0.005369971
21 -0.21097576 -0.010667752
22 -0.18032943 -0.013802158
23 -0.15015648 -0.015202822
24 0.06092370 -0.011474702
25 0.15555003 -0.017250320
Performing GPA
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Making projections... Finished!
Call:
gpagen(A = scallops$coorddata, curves = scallops$curvslide, surfaces = scallops$surfslide)
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
Performing GPA
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Making projections... Finished!
Random PLS calculations: 1000 permutations.
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Call:
integration.test(A = Y.gpa$coords, partition.gp = land.gps)
r-PLS: 0.911
Effect Size (Z): 5.2379
P-value: 0.001
Based on 1000 random permutations
Performing GPA
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|======================================================================| 100%
Making projections... Finished!
$Re.obs
[1] 0.2048115
$Z.obs
[1] -0.6782447
$ZR
[1] 1.092893
$ZR.var
[1] 0.02702703
attr(,"class")
[1] "rel.eig"
Finding the optimal rotation for CR
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Performing permutations
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Call:
modularity.test(A = Y.gpa$coords, partition.gp = land.gps, iter = 3,
CI = FALSE)
CR: 0.9
P-value: 0.2
Effect Size: -1.5
Based on 4 random permutations
Performing GPA
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Making projections... Finished!
Random PLS calculations: 4 permutations.
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Call:
phylo.integration(A = Y.gpa$coords, phy = plethspecies$phy, partition.gp = land.gps,
iter = 3)
r-PLS: 1
Effect Size (Z): 1.1
P-value: 0.25
Based on 4 random permutations
Performing GPA
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Making projections... Finished!
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Call:
phylo.modularity(A = Y.gpa$coords, partition.gp = land.gps, phy = plethspecies$phy,
CI = FALSE, iter = 3)
CR: 1.2
P-value: 0.8
Effect Size: 1
Based on 4 random permutations
Performing GPA
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Making projections... Finished!
Call:
physignal(A = Y.gpa$coords, phy = plethspecies$phy, iter = 3)
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.
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
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.
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Performing GPA
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Making projections... Finished!
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Performing GPA
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Making projections... Finished!
Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Please be aware that printing progress slows down the analysis (perhaps slightly).
Preliminary Model Fit...
Sums of Squares calculations: 4 permutations.
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Data in either A1 or A2 do not have names. It is assumed data in both A1 and A2 are ordered the same.
Random PLS calculations: 4 permutations.
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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 5.562296e-05 6.115844e-06 1.946905e-06
Proportion Ancestors Dispersion 7.523783e-01 8.272534e-02 2.633462e-02
Cumulative Ancestors Dispersion 7.523783e-01 8.351036e-01 8.614383e-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 7.984540e-06 1.524978e-06 2.962999e-07
Proportion Ancestors Dispersion 1.080021e-01 2.062746e-02 4.007870e-03
Cumulative Ancestors Dispersion 9.694403e-01 9.900678e-01 9.940757e-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 1.945159e-07 2.434675e-07
Proportion Ancestors Dispersion 2.631099e-03 3.293238e-03
Cumulative Ancestors Dispersion 9.967068e-01 1.000000e+00
Performing GPA
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Data in either A1 or A2 do not have names. It is assumed data in both A1 and A2 are ordered the same.
Random PLS calculations: 4 permutations.
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Call:
two.b.pls(A1 = Y.gpa$coords, A2 = plethShapeFood$food, iter = 3)
r-PLS: 1
Effect Size (Z): 1.7
P-value: 0.25
Based on 4 random permutations
[ FAIL 0 | WARN 0 | SKIP 1 | PASS 229 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• On CRAN (1): 'test-geomorph.R:563:3'
[ FAIL 0 | WARN 0 | SKIP 1 | PASS 229 ]
>
> proc.time()
user system elapsed
69.10 6.85 76.09