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Type 'q()' to quit R. > ## test the stability of cca object and its support functions > suppressPackageStartupMessages(require(vegan)) > suppressPackageStartupMessages(require(parallel)) > set.seed(4711) > op <- options(digits=5) > ## models > data(dune, dune.env) > mcca <- cca(dune ~ Condition(Management) + Manure + A1, dune.env) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mrda <- rda(dune ~ Condition(Management) + Manure + A1, dune.env) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mrda1 <- rda(dune ~ Condition(Management) + Manure + A1, dune.env, scale=TRUE) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mcap <- capscale(dune ~ Condition(Management) + Manure + A1, dune.env, + dist = "bray") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mdb <- dbrda(dune ~ Condition(Management) + Manure + A1, dune.env, + dist = "bray") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mancap <- capscale(dune ~ Condition(Management) + Manure + A1, dune.env, + dist = "manhattan") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mandb <- dbrda(dune ~ Condition(Management) + Manure + A1, dune.env, + dist = "manhattan") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > ## 0-rank constraints > m0cca <- cca(dune ~ Condition(Management) + Management, dune.env) > ## univariate model, rank-1 constraint > H <- diversity(dune) > m1rda <- rda(H ~ A1, dune.env) > > ## general appearance > mcca Call: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) -- Model Summary -- Inertia Proportion Rank Total 2.115 1.000 Conditional 0.604 0.285 3 Constrained 0.450 0.213 4 Unconstrained 1.062 0.502 12 Inertia is scaled Chi-square -- Note -- Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: CCA1 CCA2 CCA3 CCA4 0.2226 0.1226 0.0639 0.0406 Eigenvalues for unconstrained axes: CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 0.318 0.159 0.140 0.116 0.079 0.075 0.045 0.040 0.038 0.028 0.013 0.012 > mrda Call: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) -- Model Summary -- Inertia Proportion Rank Total 84.124 1.000 Conditional 29.231 0.347 3 Constrained 16.791 0.200 4 Unconstrained 38.102 0.453 12 Inertia is variance -- Note -- Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: RDA1 RDA2 RDA3 RDA4 8.54 4.33 2.21 1.72 Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 10.25 8.21 4.95 3.36 3.12 2.49 1.87 1.26 1.02 0.74 0.48 0.35 > mcap Call: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") -- Model Summary -- Inertia Proportion Rank Total 4.299 RealTotal 4.594 1.000 Conditional 1.500 0.327 3 Constrained 1.073 0.233 4 Unconstrained 2.021 0.440 12 Imaginary -0.295 Inertia is squared Bray distance -- Note -- Species scores projected from 'dune' Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: CAP1 CAP2 CAP3 CAP4 0.674 0.243 0.105 0.051 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12 0.749 0.338 0.308 0.213 0.137 0.136 0.062 0.039 0.017 0.011 0.006 0.004 > mdb Call: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") -- Model Summary -- Inertia Proportion Rank RealDims Total 4.299 1.000 Conditional 1.469 0.342 3 Constrained 1.035 0.241 4 4 Unconstrained 1.796 0.418 12 9 Inertia is squared Bray distance -- Note -- Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: dbRDA1 dbRDA2 dbRDA3 dbRDA4 0.667 0.238 0.091 0.038 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 iMDS1 iMDS2 0.742 0.337 0.307 0.206 0.133 0.128 0.040 0.027 0.000 -0.020 -0.046 iMDS3 -0.059 > mancap Call: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") -- Model Summary -- Inertia Proportion Rank Total 994.092 RealTotal 1077.648 1.000 Conditional 412.304 0.383 3 Constrained 213.079 0.198 4 Unconstrained 452.264 0.420 12 Imaginary -83.555 Inertia is mean squared Manhattan distance -- Note -- Species scores projected from 'dune' Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: CAP1 CAP2 CAP3 CAP4 125.7 49.3 24.2 13.9 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12 163.7 97.1 59.0 40.2 34.9 24.9 12.7 10.9 4.7 1.9 1.5 0.8 > mandb Call: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") -- Model Summary -- Inertia Proportion Rank RealDims Total 994.092 1.000 Conditional 404.069 0.406 3 Constrained 202.576 0.204 4 4 Unconstrained 387.447 0.390 12 8 Inertia is mean squared Manhattan distance -- Note -- Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' -- Eigenvalues -- Eigenvalues for constrained axes: dbRDA1 dbRDA2 dbRDA3 dbRDA4 123.2 47.4 21.9 10.1 Eigenvalues for unconstrained axes: MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 iMDS1 iMDS2 iMDS3 iMDS4 161.5 96.1 58.8 38.5 33.4 24.6 9.6 6.0 -0.6 -6.3 -13.3 -20.8 > m0cca Call: cca(formula = dune ~ Condition(Management) + Management, data = dune.env) -- Model Summary -- Inertia Proportion Rank Total 2.115 1.000 Conditional 0.604 0.285 3 Unconstrained 1.511 0.715 16 Inertia is scaled Chi-square -- Eigenvalues -- Eigenvalues for unconstrained axes: CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13 0.447 0.203 0.163 0.135 0.129 0.095 0.079 0.065 0.050 0.043 0.039 0.024 0.018 CA14 CA15 CA16 0.009 0.008 0.004 > m1rda Call: rda(formula = H ~ A1, data = dune.env) -- Model Summary -- Inertia Proportion Rank Total 0.07321 1.00000 Constrained 0.00294 0.04020 1 Unconstrained 0.07027 0.95980 1 Inertia is variance -- Eigenvalues -- Eigenvalues for constrained axes: RDA1 0.002943 Eigenvalues for unconstrained axes: PC1 0.0703 > ## names > sort(names(mcca)) [1] "CA" "CCA" "Ybar" "call" "call" [6] "colsum" "grand.total" "inertia" "method" "pCCA" [11] "rowsum" "terminfo" "terms" "tot.chi" > sort(names(mrda)) [1] "CA" "CCA" "Ybar" "call" [5] "colsum" "inertia" "method" "pCCA" [9] "regularization" "terminfo" "terms" "tot.chi" > sort(names(mcap)) [1] "CA" "CCA" "Ybar" "adjust" "call" "colsum" [7] "inertia" "method" "pCCA" "sqrt.dist" "terminfo" "terms" [13] "tot.chi" "vdata" > sort(names(mdb)) [1] "CA" "CCA" "Ybar" "adjust" "call" "colsum" [7] "inertia" "method" "pCCA" "sqrt.dist" "terminfo" "terms" [13] "tot.chi" > > ## diagnostics > hatvalues(mcca) 1 2 3 4 5 6 7 8 9 10 0.38964 0.66464 0.39824 0.48282 0.43153 0.42904 0.57721 0.85494 0.66464 0.47319 11 12 13 14 15 16 17 18 19 20 0.35412 0.62534 0.39046 0.32811 0.58922 0.38621 0.13392 0.20951 0.29994 0.31726 > hatvalues(mrda) [1] 0.53471 0.65751 0.33777 0.40784 0.40677 0.37400 0.56067 0.84957 0.65751 [10] 0.42120 0.42260 0.64583 0.40092 0.33039 0.62433 0.39796 0.23669 0.20371 [19] 0.25735 0.27267 > hatvalues(mandb) [1] 0.53471 0.65751 0.33777 0.40784 0.40677 0.37400 0.56067 0.84957 0.65751 [10] 0.42120 0.42260 0.64583 0.40092 0.33039 0.62433 0.39796 0.23669 0.20371 [19] 0.25735 0.27267 > hatvalues(m1rda) [1] 0.096575 0.070198 0.053353 0.054682 0.073302 0.053353 0.096575 0.054682 [9] 0.064657 0.076626 0.070198 0.060002 0.064657 0.269467 0.540109 0.058007 [17] 0.058007 0.050693 0.064657 0.070198 > > zapsmall(head(cooks.distance(mcca))) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.2015 0.3909 0.0100 0.3198 0.0036 0.3208 0.1238 0.0004 2 0.1249 0.5641 0.0138 0.5688 0.9650 0.2343 0.0899 0.0372 3 0.0202 0.0466 0.0015 0.7564 0.0005 0.0621 0.3002 0.0001 4 0.0404 0.6089 0.0013 0.3372 0.0005 0.0288 0.9104 0.0001 5 0.0670 0.1356 0.0482 0.1869 0.3149 0.3882 0.2014 0.0265 6 0.0017 0.0004 0.0060 0.1985 0.0043 0.3420 0.3112 0.0546 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 0.4134 0.2264 0.0216 0.5968 0.0064 0.0085 0.0017 0.0017 2 0.0001 0.3117 0.0656 3.3013 0.0089 0.1618 0.8448 1.2765 3 1.0040 0.0347 0.0033 0.0338 0.0010 0.0013 0.0003 0.0003 4 3.0432 0.0290 0.0028 0.1762 0.0008 0.0011 0.0002 0.0002 5 0.0005 1.0857 0.0478 0.1519 0.0308 0.0660 0.0687 0.2020 6 0.0001 0.1346 0.0490 0.7247 0.0038 0.0002 0.0218 0.3566 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 1.5527 0.0010 0.7923 0.0079 0.0073 0.0001 0.1837 0.0485 2 0.6645 2.4053 0.0603 1.1534 0.0331 2.1543 0.0002 0.0667 3 0.0436 0.0002 0.0001 0.0555 0.0011 0.0000 0.3373 0.0074 4 0.6133 0.0001 0.5616 0.0444 0.0009 0.0000 1.1185 0.0062 5 0.0608 0.2669 0.0101 0.0460 0.0075 0.0783 0.0292 0.2324 6 0.2028 0.0874 0.0005 0.1772 0.0294 0.2935 0.0888 0.0288 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 0.1877 0.0003 0.0156 0.0001 0.0578 0.0018 2 0.0461 1.7937 0.0000 1.7194 0.8086 0.0164 3 0.0351 0.0000 0.0389 0.0000 0.0110 0.0003 4 0.0179 0.0000 0.0181 0.0000 0.0053 0.0002 5 0.0184 0.1001 0.3751 0.0695 0.0640 0.0000 6 0.0343 1.6066 0.1480 0.0601 0.2804 0.0179 > zapsmall(head(cooks.distance(mrda))) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.1761 1.5406 0.0313 1.1176 0.0094 1.1835 0.3910 0.0006 2 0.1158 0.3965 0.0116 0.5059 1.4020 0.2724 0.1134 0.0392 3 0.0133 0.0020 0.0030 0.6281 0.0009 0.0920 0.1284 0.0001 4 0.0214 0.8231 0.0030 0.0617 0.0009 0.1397 0.7559 0.0001 5 0.0498 0.0658 0.0365 0.1882 0.3664 0.3256 0.1322 0.0324 6 0.0145 0.0020 0.0029 0.1431 0.0244 0.2416 0.1994 0.0384 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 1.1927 0.6321 0.0316 0.0002 0.0127 0.0207 0.0019 0.0026 2 0.0002 0.2331 0.0341 4.2057 0.0047 0.1666 0.9432 1.3977 3 0.3951 0.0600 0.0030 0.0000 0.0012 0.0020 0.0002 0.0002 4 2.3160 0.0598 0.0030 0.0000 0.0012 0.0020 0.0002 0.0002 5 0.0005 0.7370 0.0110 0.1449 0.0149 0.0456 0.0628 0.2543 6 0.0000 0.0588 0.0178 0.6361 0.0012 0.0001 0.0295 0.2742 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 0.2819 0.0021 0.1434 0.6899 0.0089 0.0000 0.6367 0.0748 2 1.5095 2.7465 0.3092 1.0935 0.0173 1.9478 0.0005 0.0276 3 0.0006 0.0002 0.1187 0.1113 0.0008 0.0000 0.1676 0.0071 4 0.1564 0.0002 0.0268 0.0256 0.0008 0.0000 1.1039 0.0071 5 0.3844 0.2391 0.1254 0.0186 0.0003 0.0398 0.0449 0.0873 6 0.7320 0.1480 0.0063 0.0894 0.0111 0.5069 0.0606 0.0070 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 0.7907 0.0003 0.1297 0.0005 0.2576 0.0023 2 0.0160 1.5145 0.0015 2.1843 0.9008 0.0144 3 0.0632 0.0000 0.0515 0.0000 0.0203 0.0002 4 0.0908 0.0000 0.0010 0.0000 0.0300 0.0002 5 0.0022 0.0881 0.3779 0.0844 0.0838 0.0026 6 0.0092 1.3701 0.1927 0.0551 0.3486 0.0118 > zapsmall(head(cooks.distance(mrda1))) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.1761 1.5406 0.0313 1.1176 0.0094 1.1835 0.3910 0.0006 2 0.1158 0.3965 0.0116 0.5059 1.4020 0.2724 0.1134 0.0392 3 0.0133 0.0020 0.0030 0.6281 0.0009 0.0920 0.1284 0.0001 4 0.0214 0.8231 0.0030 0.0617 0.0009 0.1397 0.7559 0.0001 5 0.0498 0.0658 0.0365 0.1882 0.3664 0.3256 0.1322 0.0324 6 0.0145 0.0020 0.0029 0.1431 0.0244 0.2416 0.1994 0.0384 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 1.1927 0.6321 0.0316 0.0002 0.0127 0.0207 0.0019 0.0026 2 0.0002 0.2331 0.0341 4.2057 0.0047 0.1666 0.9432 1.3977 3 0.3951 0.0600 0.0030 0.0000 0.0012 0.0020 0.0002 0.0002 4 2.3160 0.0598 0.0030 0.0000 0.0012 0.0020 0.0002 0.0002 5 0.0005 0.7370 0.0110 0.1449 0.0149 0.0456 0.0628 0.2543 6 0.0000 0.0588 0.0178 0.6361 0.0012 0.0001 0.0295 0.2742 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 0.2819 0.0021 0.1434 0.6899 0.0089 0.0000 0.6367 0.0748 2 1.5095 2.7465 0.3092 1.0935 0.0173 1.9478 0.0005 0.0276 3 0.0006 0.0002 0.1187 0.1113 0.0008 0.0000 0.1676 0.0071 4 0.1564 0.0002 0.0268 0.0256 0.0008 0.0000 1.1039 0.0071 5 0.3844 0.2391 0.1254 0.0186 0.0003 0.0398 0.0449 0.0873 6 0.7320 0.1480 0.0063 0.0894 0.0111 0.5069 0.0606 0.0070 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 0.7907 0.0003 0.1297 0.0005 0.2576 0.0023 2 0.0160 1.5145 0.0015 2.1843 0.9008 0.0144 3 0.0632 0.0000 0.0515 0.0000 0.0203 0.0002 4 0.0908 0.0000 0.0010 0.0000 0.0300 0.0002 5 0.0022 0.0881 0.3779 0.0844 0.0838 0.0026 6 0.0092 1.3701 0.1927 0.0551 0.3486 0.0118 > zapsmall(head(cooks.distance(mcap, "canoco"))) CAP1 CAP2 CAP3 CAP4 1 0.42135 0.71383 0.52485 0.00003 2 0.35667 0.91934 0.00051 0.34825 3 0.04306 0.05516 0.09142 0.00035 4 0.03627 0.08478 0.01578 0.00073 5 0.23671 0.09188 0.00653 0.26523 6 0.00064 0.00000 0.00033 0.07231 > zapsmall(head(cooks.distance(mdb, "canoco"))) dbRDA1 dbRDA2 dbRDA3 dbRDA4 1 0.45656 0.82736 0.95105 0.09224 2 0.38438 0.83291 0.00814 0.35065 3 0.04581 0.06482 0.16066 0.00937 4 0.04026 0.09692 0.03121 0.00801 5 0.26263 0.06530 0.00010 0.22079 6 0.00141 0.00135 0.00737 0.12833 > zapsmall(head(cooks.distance(mancap, "canoco"))) CAP1 CAP2 CAP3 CAP4 1 0.00378 0.00009 0.18185 0.45549 2 0.32121 1.64122 0.11530 2.20497 3 0.00679 0.01562 0.11032 0.01045 4 0.00349 0.02163 0.01335 0.11390 5 0.20285 0.07583 0.00167 0.29463 6 0.00344 0.03919 0.01336 0.27877 > zapsmall(head(cooks.distance(mandb, "canoco"))) dbRDA1 dbRDA2 dbRDA3 dbRDA4 1 0.00914 0.00480 0.50784 0.16442 2 0.34821 1.48377 0.23249 1.60030 3 0.00735 0.01721 0.17781 0.00061 4 0.00158 0.01292 0.00085 0.06147 5 0.23684 0.04827 0.04109 0.20639 6 0.00207 0.05014 0.04443 0.36650 > zapsmall(head(cooks.distance(m1rda))) col1 1 0.47753 2 0.00170 3 0.00011 4 0.02525 5 0.10022 6 0.01147 > > head(goodness(mcca, display = "sites")) CCA1 CCA2 CCA3 CCA4 1 0.185250 0.224787 0.23190 0.23203 2 0.044206 0.054137 0.13403 0.24479 3 0.168758 0.475105 0.48459 0.48764 4 0.116243 0.295806 0.30207 0.30380 5 0.014540 0.057912 0.19448 0.19467 6 0.067333 0.067749 0.13876 0.13929 > head(goodness(mrda, display = "sites")) RDA1 RDA2 RDA3 RDA4 1 0.42239542 0.432516 0.43329 0.43348 2 0.01888621 0.044210 0.11461 0.17317 3 0.26198074 0.298577 0.30272 0.30748 4 0.23011480 0.259364 0.26228 0.26592 5 0.00027964 0.036798 0.11025 0.11469 6 0.01492482 0.083895 0.09990 0.09993 > head(goodness(mrda1, display = "sites")) RDA1 RDA2 RDA3 RDA4 1 0.4207789 0.451576 0.48069 0.48189 2 0.0552381 0.058798 0.14704 0.22767 3 0.2344181 0.381112 0.39558 0.40051 4 0.0988545 0.153807 0.15997 0.16183 5 0.0049954 0.029302 0.16899 0.17120 6 0.0391326 0.042622 0.10319 0.10322 > head(goodness(m1rda, display = "sites")) RDA1 1 0.0049860 2 0.1415108 3 0.2006191 4 0.0037679 5 0.0089639 6 0.0056118 > goodness(m1rda) # fails in 2.5-3 RDA1 col1 0.040196 > ## head(goodness(mcap, display = "sites")) # currently disabled > ## head(goodness(mdb, display="sites")) # not implemented for partial dbrda > ## head(goodness(mancap, display="sites")) # currently disabled > ## head(goodness(mandb, display="sites")) # not implemneted for partial dbrda > head(goodness(m0cca)) [1,] > > head(inertcomp(mcca)) pCCA CCA CA Achimill 0.0153429 0.0048302 0.030529 Agrostol 0.0381018 0.0161939 0.027816 Airaprae 0.0258133 0.0134079 0.064877 Alopgeni 0.0487892 0.0092962 0.037121 Anthodor 0.0141466 0.0069150 0.072781 Bellpere 0.0069739 0.0106965 0.020072 > head(inertcomp(mrda)) pCCA CCA CA Achimill 0.60877 0.18696 0.74111 Agrostol 2.81228 1.14782 3.23990 Airaprae 0.15351 0.10549 0.35942 Alopgeni 3.26316 0.57391 3.06819 Anthodor 0.53421 0.27983 2.07806 Bellpere 0.21140 0.29749 0.57268 > head(inertcomp(mrda1)) pCCA CCA CA Achimill 0.39612 0.121651 0.48223 Agrostol 0.39059 0.159420 0.44999 Airaprae 0.24823 0.170578 0.58120 Alopgeni 0.47256 0.083113 0.44433 Anthodor 0.18471 0.096757 0.71853 Bellpere 0.19546 0.275055 0.52949 > head(inertcomp(mcap, display="sites")) pCCA CCA CA 1 0.058628 0.105577 0.104294 2 0.100405 0.035067 0.077139 3 0.058628 0.070870 0.039223 4 0.058628 0.072386 0.056966 5 0.043474 0.018994 0.080773 6 0.043474 0.028915 0.066973 > head(inertcomp(mdb, display = "sites")) pCCA CCA CA 1 0.058383 0.103846 0.080795 2 0.093318 0.028218 0.073292 3 0.058383 0.069481 0.024416 4 0.058383 0.070979 0.052324 5 0.042268 0.016687 0.063247 6 0.042268 0.026426 0.051382 > head(inertcomp(mancap, display = "sites")) pCCA CCA CA 1 14.310 19.2048 21.046 2 31.248 11.5984 26.506 3 14.310 15.4320 9.898 4 14.310 15.5966 15.754 5 17.177 5.8883 21.460 6 17.177 8.8555 27.470 > head(inertcomp(mandb, display = "sites")) pCCA CCA CA 1 14.211 18.6264 15.3043 2 29.512 9.8634 25.4006 3 14.211 14.9565 6.8683 4 14.211 15.1159 14.6604 5 16.995 5.4240 16.8238 6 16.995 8.3251 22.2002 > zapsmall(head(inertcomp(m0cca))) pCCA CA Achimill 0.015343 0.035359 Agrostol 0.038102 0.044009 Airaprae 0.025813 0.078285 Alopgeni 0.048789 0.046417 Anthodor 0.014147 0.079696 Bellpere 0.006974 0.030769 > inertcomp(m1rda) CCA CA col1 0.0029428 0.070268 > > abs(zapsmall(intersetcor(mcca))) CCA1 CCA2 CCA3 CCA4 Manure.L 0.16256 0.52974 0.27392 0.47312 Manure.Q 0.31167 0.59055 0.43511 0.14091 Manure.C 0.33679 0.23680 0.08339 0.60440 Manure^4 0.27194 0.16138 0.68107 0.13507 A1 0.64369 0.26617 0.35227 0.08656 > abs(zapsmall(intersetcor(mrda))) RDA1 RDA2 RDA3 RDA4 Manure.L 0.44970 0.45430 0.17052 0.41752 Manure.Q 0.64906 0.44643 0.11403 0.14879 Manure.C 0.47200 0.13567 0.08333 0.60716 Manure^4 0.27404 0.47959 0.51866 0.03987 A1 0.48811 0.17985 0.50083 0.29462 > abs(zapsmall(intersetcor(mrda1))) RDA1 RDA2 RDA3 RDA4 Manure.L 0.35700 0.44353 0.26187 0.50157 Manure.Q 0.47776 0.41663 0.48816 0.13407 Manure.C 0.40999 0.08092 0.01974 0.67542 Manure^4 0.39647 0.04685 0.69440 0.04289 A1 0.53789 0.47469 0.36590 0.13184 > abs(zapsmall(intersetcor(mcap))) CAP1 CAP2 CAP3 CAP4 Manure.L 0.34428 0.46883 0.08077 0.33049 Manure.Q 0.46742 0.48537 0.22608 0.13137 Manure.C 0.35114 0.05579 0.08638 0.48645 Manure^4 0.26818 0.13779 0.62087 0.05599 A1 0.57349 0.28004 0.35091 0.07576 > abs(zapsmall(intersetcor(mdb))) dbRDA1 dbRDA2 dbRDA3 dbRDA4 Manure.L 0.33636 0.46901 0.14552 0.19669 Manure.Q 0.46084 0.46800 0.19559 0.10617 Manure.C 0.35058 0.05075 0.15478 0.29989 Manure^4 0.26690 0.10088 0.53857 0.08724 A1 0.56939 0.29083 0.30919 0.02422 > abs(zapsmall(intersetcor(mancap))) CAP1 CAP2 CAP3 CAP4 Manure.L 0.38584 0.48506 0.00005 0.26424 Manure.Q 0.45373 0.44871 0.24500 0.15056 Manure.C 0.35825 0.09885 0.10252 0.45075 Manure^4 0.41554 0.33601 0.44684 0.03505 A1 0.36136 0.17672 0.51779 0.16025 > abs(zapsmall(intersetcor(mandb))) dbRDA1 dbRDA2 dbRDA3 dbRDA4 Manure.L 0.37496 0.48082 0.08018 0.15731 Manure.Q 0.44695 0.41430 0.22423 0.11920 Manure.C 0.35996 0.11590 0.00423 0.28532 Manure^4 0.41300 0.28868 0.42071 0.06480 A1 0.35511 0.20385 0.49290 0.06262 > abs(zapsmall(intersetcor(m1rda))) RDA1 A1 0.20049 > > tolerance(mcca) Species Tolerance Scaling: 2 CCA1 CCA2 Achimill 0.61007 0.503578 Agrostol 1.35694 1.449646 Airaprae 0.10516 0.078256 Alopgeni 0.98368 1.398741 Anthodor 0.64966 0.568219 Bellpere 0.50902 0.824771 Bromhord 0.51376 0.715512 Chenalbu 0.00000 0.000000 Cirsarve 0.00000 0.000000 Comapalu 0.77116 0.573877 Eleopalu 1.38967 1.527094 Elymrepe 0.63733 0.927844 Empenigr 0.00000 0.000000 Hyporadi 0.75384 0.361128 Juncarti 1.49080 1.279732 Juncbufo 0.83160 0.650590 Lolipere 0.70897 0.735395 Planlanc 0.51563 0.436023 Poaprat 0.77305 0.833332 Poatriv 0.91211 1.060493 Ranuflam 1.55143 1.374797 Rumeacet 0.46610 0.821992 Sagiproc 0.91313 1.312686 Salirepe 0.28643 0.213149 Scorautu 0.97673 0.906578 Trifprat 0.56768 0.419267 Trifrepe 0.95609 0.941391 Vicilath 0.52385 0.187524 Bracruta 1.14404 1.091019 Callcusp 1.57627 1.446282 > tolerance(m0cca) Species Tolerance Scaling: 2 CA1 CA2 Achimill 0.930813 0.67460 Agrostol 0.949483 0.83992 Airaprae 0.249010 1.16549 Alopgeni 0.726918 0.86630 Anthodor 1.160427 1.20647 Bellpere 0.321717 0.58968 Bromhord 0.304392 0.25205 Chenalbu 0.000000 0.00000 Cirsarve 0.000000 0.00000 Comapalu 0.041889 0.39785 Eleopalu 0.278432 0.81396 Elymrepe 0.482478 1.09766 Empenigr 0.000000 0.00000 Hyporadi 1.243512 1.04260 Juncarti 0.551515 0.67321 Juncbufo 0.346137 0.67554 Lolipere 0.472723 1.00637 Planlanc 0.685082 0.75194 Poaprat 0.622322 1.05260 Poatriv 0.539912 0.92947 Ranuflam 0.641115 0.77679 Rumeacet 0.311899 0.85299 Sagiproc 1.005528 1.03234 Salirepe 1.981465 1.87444 Scorautu 1.153131 1.16089 Trifprat 0.063055 0.11141 Trifrepe 0.938071 0.96335 Vicilath 0.266341 1.28007 Bracruta 1.095827 1.12812 Callcusp 0.152449 0.82496 > > vif.cca(mcca) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C 2.6392 59.4353 4.6922 29.4621 20.0580 7.0095 Manure^4 A1 NA 1.4324 > vif.cca(mrda) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C 2.7608 82.0791 5.0631 38.4808 24.0685 7.4689 Manure^4 A1 NA 1.3916 > vif.cca(mcap) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C 2.7608 82.0791 5.0631 38.4808 24.0685 7.4689 Manure^4 A1 NA 1.3916 > vif.cca(mdb) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C 2.7608 82.0791 5.0631 38.4808 24.0685 7.4689 Manure^4 A1 NA 1.3916 > > alias(mcca) Model : dune ~ Condition(Management) + Manure + A1 Complete : ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 Manure^4 8.3666 5.2915 -4.4721 2.6458 > alias(mrda) Model : dune ~ Condition(Management) + Manure + A1 Complete : ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 Manure^4 8.3666 5.2915 -4.4721 2.6458 > alias(mcap) Model : dune ~ Condition(Management) + Manure + A1 Complete : ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 Manure^4 8.3666 5.2915 -4.4721 2.6458 > alias(mdb) Model : dune ~ Condition(Management) + Manure + A1 Complete : ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 Manure^4 8.3666 5.2915 -4.4721 2.6458 > > ## basic statistic > > abs(coef(mcca)) CCA1 CCA2 CCA3 CCA4 ManagementHF 0.62129 0.677568 0.035035 1.54946 ManagementNM 10.01217 2.637011 14.104915 5.41558 ManagementSF 0.61605 2.920127 0.607816 1.98726 Manure.L 5.75979 5.151196 8.203284 5.62141 Manure.Q 4.27478 0.010966 8.396272 3.23064 Manure.C 3.40803 1.878886 4.693036 0.25152 Manure^4 NA NA NA NA A1 0.49572 0.368902 0.137792 0.11238 > abs(coef(mrda)) RDA1 RDA2 RDA3 RDA4 ManagementHF 0.039701 0.196654 0.230283 0.331044 ManagementNM 1.885616 2.733188 2.661844 0.965079 ManagementSF 0.241056 0.566718 0.447380 0.355072 Manure.L 1.502678 1.057175 2.212531 0.972634 Manure.Q 0.627410 1.680780 1.472947 0.605660 Manure.C 0.776016 0.849770 0.855201 0.183062 Manure^4 NA NA NA NA A1 0.045186 0.061152 0.080351 0.056434 > abs(coef(mrda1)) RDA1 RDA2 RDA3 RDA4 ManagementHF 0.047647 0.200658 0.035428 0.398849 ManagementNM 2.609121 0.067114 3.393206 0.846634 ManagementSF 0.138903 0.649111 0.090944 0.506550 Manure.L 1.832076 0.744465 2.036043 1.077390 Manure.Q 1.090769 0.253091 2.052449 0.537289 Manure.C 0.942621 0.147407 1.065170 0.210566 Manure^4 NA NA NA NA A1 0.063521 0.099114 0.029881 0.025905 > abs(coef(mcap)) CAP1 CAP2 CAP3 CAP4 ManagementHF 0.054464 0.196369 0.064230 0.396506 ManagementNM 2.144319 0.975995 3.530608 1.013304 ManagementSF 0.118096 0.671018 0.100124 0.480901 Manure.L 1.510554 0.193391 2.360417 1.151619 Manure.Q 0.831594 0.804571 1.997158 0.653864 Manure.C 0.769068 0.163012 1.201823 0.163625 Manure^4 NA NA NA NA A1 0.074664 0.082683 0.048984 0.024736 > abs(coef(mdb)) dbRDA1 dbRDA2 dbRDA3 dbRDA4 ManagementHF 0.057234 0.205998 0.0072718 0.396366 ManagementNM 2.148560 0.765167 3.3694004 1.577340 ManagementSF 0.112226 0.684272 0.0077129 0.473942 Manure.L 1.506447 0.341904 2.1389592 1.501846 Manure.Q 0.835134 0.685757 1.9060884 0.976819 Manure.C 0.771676 0.100214 1.2176754 0.030482 Manure^4 NA NA NA NA A1 0.074999 0.084173 0.0483834 0.019273 > abs(coef(m1rda)) RDA1 A1 0.10527 > > eigenvals(mcca) CCA1 CCA2 CCA3 CCA4 CA1 CA2 CA3 CA4 CA5 CA6 0.22257 0.12260 0.06390 0.04055 0.31761 0.15858 0.13966 0.11608 0.07907 0.07534 CA7 CA8 CA9 CA10 CA11 CA12 0.04527 0.04018 0.03777 0.02799 0.01267 0.01158 > eigenvals(mrda) RDA1 RDA2 RDA3 RDA4 PC1 PC2 PC3 PC4 PC5 PC6 8.5394 4.3297 2.2061 1.7162 10.2523 8.2069 4.9525 3.3577 3.1226 2.4941 PC7 PC8 PC9 PC10 PC11 PC12 1.8660 1.2579 1.0186 0.7428 0.4814 0.3488 > eigenvals(mrda1) RDA1 RDA2 RDA3 RDA4 PC1 PC2 PC3 PC4 PC5 PC6 PC7 2.6117 1.6991 1.1039 0.7261 3.4684 2.5471 2.1158 1.7223 1.4426 0.9611 0.8089 PC8 PC9 PC10 PC11 PC12 0.7378 0.4903 0.3805 0.2472 0.1570 > eigenvals(mcap) CAP1 CAP2 CAP3 CAP4 MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 0.67370 0.24300 0.10457 0.05136 0.74894 0.33796 0.30841 0.21316 0.13698 0.13604 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12 0.06196 0.03929 0.01687 0.01110 0.00646 0.00402 > eigenvals(mdb) dbRDA1 dbRDA2 dbRDA3 dbRDA4 MDS1 MDS2 MDS3 MDS4 0.66660 0.23850 0.09102 0.03844 0.74204 0.33668 0.30662 0.20627 MDS5 MDS6 MDS7 MDS8 MDS9 iMDS1 iMDS2 iMDS3 0.13339 0.12829 0.03980 0.02745 0.00041 -0.01985 -0.04601 -0.05923 > eigenvals(mancap) CAP1 CAP2 CAP3 CAP4 MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 125.658 49.308 24.239 13.875 163.684 97.079 59.006 40.210 34.854 24.943 MDS7 MDS8 MDS9 MDS10 MDS11 MDS12 12.724 10.918 4.686 1.896 1.506 0.758 > eigenvals(mandb) dbRDA1 dbRDA2 dbRDA3 dbRDA4 MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 123.163 47.370 21.915 10.128 161.475 96.116 58.781 38.534 33.355 24.565 MDS7 MDS8 iMDS1 iMDS2 iMDS3 iMDS4 9.591 5.971 -0.590 -6.265 -13.300 -20.787 > eigenvals(m0cca) CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 0.44737 0.20300 0.16301 0.13457 0.12940 0.09494 0.07904 0.06526 0.05004 0.04321 CA11 CA12 CA13 CA14 CA15 CA16 0.03870 0.02385 0.01773 0.00917 0.00796 0.00416 > eigenvals(m0cca, model = "constrained") NULL > eigenvals(m1rda) RDA1 PC1 0.002943 0.070268 > > nobs(mcca) [1] 20 > nobs(mrda) [1] 20 > nobs(mcap) [1] 20 > nobs(mdb) [1] 20 > nobs(m0cca) [1] 20 > nobs(m1rda) [1] 20 > > RsquareAdj(mcca) $r.squared [1] 0.21256 $adj.r.squared [1] 0.054162 > RsquareAdj(mrda) $r.squared [1] 0.1996 $adj.r.squared [1] 0.057747 > RsquareAdj(mrda1) $r.squared [1] 0.20469 $adj.r.squared [1] 0.044112 > RsquareAdj(mcap) $r.squared [1] 0.23349 $adj.r.squared [1] 0.11198 > RsquareAdj(mdb) $r.squared [1] 0.24065 $adj.r.squared [1] 0.12042 > RsquareAdj(m1rda) $r.squared [1] 0.040196 $adj.r.squared [1] -0.013127 > > head(model.frame(mcca)) Management Manure A1 1 SF 4 2.8 2 BF 2 3.5 3 SF 4 4.3 4 SF 4 4.2 5 HF 2 6.3 6 HF 2 4.3 > head(model.frame(mrda)) Management Manure A1 1 SF 4 2.8 2 BF 2 3.5 3 SF 4 4.3 4 SF 4 4.2 5 HF 2 6.3 6 HF 2 4.3 > head(model.frame(mrda1)) Management Manure A1 1 SF 4 2.8 2 BF 2 3.5 3 SF 4 4.3 4 SF 4 4.2 5 HF 2 6.3 6 HF 2 4.3 > head(model.frame(mcap)) Management Manure A1 1 SF 4 2.8 2 BF 2 3.5 3 SF 4 4.3 4 SF 4 4.2 5 HF 2 6.3 6 HF 2 4.3 > head(model.frame(mdb)) Management Manure A1 1 SF 4 2.8 2 BF 2 3.5 3 SF 4 4.3 4 SF 4 4.2 5 HF 2 6.3 6 HF 2 4.3 > head(model.frame(m0cca)) Management 1 SF 2 BF 3 SF 4 SF 5 HF 6 HF > head(model.frame(m1rda)) A1 1 2.8 2 3.5 3 4.3 4 4.2 5 6.3 6 4.3 > > ## testing and model building - > > deviance(mcca) [1] 727.34 > deviance(mrda) [1] 723.93 > deviance(mrda1) [1] 286.5 > deviance(mcap) [1] 38.402 > deviance(mdb) [1] 34.121 > deviance(m0cca) [1] 1035.3 > deviance(m1rda) [1] 1.3351 > > per <- shuffleSet(nrow(dune), 49) > permutest(mcca, per) Permutation test for cca under reduced model Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 0.45 1.27 0.42 Residual 12 1.06 > permutest(mrda, per) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 16.8 1.32 0.08 . Residual 12 38.1 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mrda1, per) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 6.14 1.22 0.16 Residual 12 15.08 > permutest(mcap, per) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 1.07 1.59 0.08 . Residual 12 2.02 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mdb, per) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 1.03 1.73 0.08 . Residual 12 1.80 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mancap, per) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 213 1.41 0.12 Residual 12 452 > permutest(mandb, per) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 203 1.57 0.08 . Residual 12 387 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(m1rda, per) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 1 0.0029 0.75 0.44 Residual 18 0.0703 > > drop1(mcca, test = "permutation", permutations = per) Df AIC F Pr(>F) 87.9 Condition(Management) 2 87.9 Manure 3 86.5 1.03 0.86 A1 1 89.0 2.00 0.04 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(mrda, test = "permutation", permutations = per) Df AIC F Pr(>F) 87.8 Condition(Management) 2 89.2 Manure 3 87.4 1.30 0.12 A1 1 87.5 1.09 0.40 > drop1(mrda1, test = "permutation", permutations = per) Df AIC F Pr(>F) 69.2 Condition(Management) 2 69.8 Manure 3 68.2 1.12 0.22 A1 1 69.6 1.48 0.14 > drop1(mcap, test = "permutation", permutations = per) Df AIC F Pr(>F) 29.1 Condition(Management) 2 29.4 Manure 3 28.4 1.24 0.22 A1 1 30.4 2.19 0.08 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(mdb, test = "permutation", permutations = per) Df AIC F Pr(>F) 26.7 Condition(Management) 2 27.2 Manure 3 26.4 1.32 0.18 A1 1 28.4 2.46 0.08 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(m1rda, test = "permutation", permutations = per) Df AIC F Pr(>F) -50.1 A1 1 -51.3 0.75 0.44 > > anova(mcca, permutations = per) Permutation test for cca under reduced model Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Model 4 0.45 1.27 0.42 Residual 12 1.06 > anova(mrda, permutations = per) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Model 4 16.8 1.32 0.08 . Residual 12 38.1 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda1, permutations = per) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Model 4 6.14 1.22 0.16 Residual 12 15.08 > anova(mcap, permutations = per) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Model 4 1.07 1.59 0.08 . Residual 12 2.02 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Model 4 1.03 1.73 0.08 . Residual 12 1.80 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Model 4 213 1.41 0.12 Residual 12 452 > anova(mandb, permutations = per) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Model 4 203 1.57 0.08 . Residual 12 387 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(m0cca, permutations = per) No constrained component Model: cca(formula = dune ~ Condition(Management) + Management, data = dune.env) Df ChiSquare F Pr(>F) Model 0 0.00 0 Residual 19 1.51 > > anova(mcca, permutations = per, by="term") Permutation test for cca under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Manure 3 0.273 1.03 1.00 A1 1 0.177 2.00 0.02 * Residual 12 1.062 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda, permutations = per, by="term") Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Manure 3 13.3 1.40 0.1 A1 1 3.5 1.09 0.4 Residual 12 38.1 > anova(mrda1, permutations = per, by="term") Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Manure 3 4.28 1.14 0.20 A1 1 1.86 1.48 0.14 Residual 12 15.08 > anova(mcap, permutations = per, by="term") Permutation test for capscale under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.703 1.39 0.12 A1 1 0.370 2.19 0.08 . Residual 12 2.021 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per, by="term") Permutation test for dbrda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.667 1.49 0.12 A1 1 0.367 2.46 0.08 . Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by="term") Permutation test for capscale under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 173 1.53 0.14 A1 1 40 1.07 0.34 Residual 12 452 > anova(mandb, permutations = per, by="term") Permutation test for dbrda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 163 1.68 0.14 A1 1 39 1.22 0.26 Residual 12 387 > anova(m1rda, permutations = per, by="term") Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) A1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > anova(mcca, permutations = per, by="margin") Permutation test for cca under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Manure 3 0.274 1.03 0.86 A1 1 0.177 2.00 0.04 * Residual 12 1.062 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda, permutations = per, by="margin") Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Manure 3 12.4 1.30 0.12 A1 1 3.5 1.09 0.40 Residual 12 38.1 > anova(mrda1, permutations = per, by="margin") Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Manure 3 4.21 1.12 0.22 A1 1 1.86 1.48 0.14 Residual 12 15.08 > anova(mcap, permutations = per, by="margin") Permutation test for capscale under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.627 1.24 0.22 A1 1 0.370 2.19 0.08 . Residual 12 2.021 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per, by="margin") Permutation test for dbrda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.594 1.32 0.18 A1 1 0.367 2.46 0.08 . Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by="margin") Permutation test for capscale under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 165 1.46 0.12 A1 1 40 1.07 0.34 Residual 12 452 > anova(mandb, permutations = per, by="margin") Permutation test for dbrda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 155 1.60 0.12 A1 1 39 1.22 0.26 Residual 12 387 > anova(m1rda, permutations = per, by="margin") Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) A1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > anova(mcca, permutations = per, by="axis") Permutation test for cca under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) CCA1 1 0.223 2.52 0.26 CCA2 1 0.123 1.39 0.96 CCA3 1 0.064 0.72 1.00 CCA4 1 0.041 0.46 Residual 12 1.062 > anova(mrda, permutations = per, by="axis") Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) RDA1 1 8.5 2.69 0.10 RDA2 1 4.3 1.36 0.62 RDA3 1 2.2 0.69 0.98 RDA4 1 1.7 0.54 0.98 Residual 12 38.1 > anova(mrda1, permutations = per, by="axis") Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) RDA1 1 2.61 2.08 0.22 RDA2 1 1.70 1.35 0.62 RDA3 1 1.10 0.88 0.94 RDA4 1 0.73 0.58 0.98 Residual 12 15.08 > anova(mcap, permutations = per, by="axis") Permutation test for capscale under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) CAP1 1 0.674 4.00 0.12 CAP2 1 0.243 1.44 0.66 CAP3 1 0.105 0.62 0.98 CAP4 1 0.051 0.30 0.98 Residual 12 2.021 > anova(mdb, permutations = per, by="axis") Permutation test for dbrda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) dbRDA1 1 0.667 4.45 0.08 . dbRDA2 1 0.238 1.59 0.58 dbRDA3 1 0.091 0.61 0.98 dbRDA4 1 0.038 0.26 0.98 Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by="axis") Permutation test for capscale under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) CAP1 1 126 3.33 0.18 CAP2 1 49 1.31 0.74 CAP3 1 24 0.64 0.98 CAP4 1 14 0.37 0.98 Residual 12 452 > anova(mandb, permutations = per, by="axis") Permutation test for dbrda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) dbRDA1 1 123 3.81 0.18 dbRDA2 1 47 1.47 0.68 dbRDA3 1 22 0.68 0.98 dbRDA4 1 10 0.31 0.98 Residual 12 387 > anova(m1rda, permutations = per, by="axis") Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) RDA1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > ## permutation tests in parallel > clust <- makeCluster(2) # socket cluster: the only one that works in Windows > > permutest(mcca, per, parallel = clust) # use socket cluster Permutation test for cca under reduced model Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 0.45 1.27 0.42 Residual 12 1.06 > permutest(mrda, per, parallel = clust) # use socket cluster Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 16.8 1.32 0.08 . Residual 12 38.1 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mrda1, per, parallel = clust) # use socket cluster Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 6.14 1.22 0.16 Residual 12 15.08 > permutest(mcap, per, parallel = clust) # use socket cluster Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 1.07 1.59 0.08 . Residual 12 2.02 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mdb, per, parallel = clust) # use socket cluster Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 1.03 1.73 0.08 . Residual 12 1.80 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(mancap, per, parallel = clust) # use socket cluster Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 213 1.41 0.12 Residual 12 452 > permutest(mandb, per, parallel = clust) # use socket cluster Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 4 203 1.57 0.08 . Residual 12 387 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > permutest(m1rda, per, parallel = clust) # use socket cluster Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Permutation test for all constrained eigenvalues Df Inertia F Pr(>F) Model 1 0.0029 0.75 0.44 Residual 18 0.0703 > > > drop1(mcca, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) 87.9 Condition(Management) 2 87.9 Manure 3 86.5 1.03 0.86 A1 1 89.0 2.00 0.04 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(mrda, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) 87.8 Condition(Management) 2 89.2 Manure 3 87.4 1.30 0.12 A1 1 87.5 1.09 0.40 > drop1(mrda1, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) 69.2 Condition(Management) 2 69.8 Manure 3 68.2 1.12 0.22 A1 1 69.6 1.48 0.14 > drop1(mcap, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) 29.1 Condition(Management) 2 29.4 Manure 3 28.4 1.24 0.22 A1 1 30.4 2.19 0.08 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(mdb, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) 26.7 Condition(Management) 2 27.2 Manure 3 26.4 1.32 0.18 A1 1 28.4 2.46 0.08 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > drop1(m1rda, test = "permutation", permutations = per, parallel = clust) Df AIC F Pr(>F) -50.1 A1 1 -51.3 0.75 0.44 > > anova(mcca, permutations = per, parallel = clust) Permutation test for cca under reduced model Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Model 4 0.45 1.27 0.42 Residual 12 1.06 > anova(mrda, permutations = per, parallel = clust) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Model 4 16.8 1.32 0.08 . Residual 12 38.1 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda1, permutations = per, parallel = clust) Permutation test for rda under reduced model Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Model 4 6.14 1.22 0.16 Residual 12 15.08 > anova(mcap, permutations = per, parallel = clust) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Model 4 1.07 1.59 0.08 . Residual 12 2.02 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per, parallel = clust) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Model 4 1.03 1.73 0.08 . Residual 12 1.80 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, parallel = clust) Permutation test for capscale under reduced model Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Model 4 213 1.41 0.12 Residual 12 452 > anova(mandb, permutations = per, parallel = clust) Permutation test for dbrda under reduced model Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Model 4 203 1.57 0.08 . Residual 12 387 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(m0cca, permutations = per, parallel = clust) No constrained component Model: cca(formula = dune ~ Condition(Management) + Management, data = dune.env) Df ChiSquare F Pr(>F) Model 0 0.00 0 Residual 19 1.51 > > anova(mcca, permutations = per, by = "term", parallel = clust) Permutation test for cca under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Manure 3 0.273 1.03 1.00 A1 1 0.177 2.00 0.02 * Residual 12 1.062 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda, permutations = per, by = "term", parallel = clust) Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Manure 3 13.3 1.40 0.1 A1 1 3.5 1.09 0.4 Residual 12 38.1 > anova(mrda1, permutations = per, by = "term", parallel = clust) Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Manure 3 4.28 1.14 0.20 A1 1 1.86 1.48 0.14 Residual 12 15.08 > anova(mcap, permutations = per, by = "term", parallel = clust) Permutation test for capscale under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.703 1.39 0.12 A1 1 0.370 2.19 0.08 . Residual 12 2.021 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per, by = "term", parallel = clust) Permutation test for dbrda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.667 1.49 0.12 A1 1 0.367 2.46 0.08 . Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by = "term", parallel = clust) Permutation test for capscale under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 173 1.53 0.14 A1 1 40 1.07 0.34 Residual 12 452 > anova(mandb, permutations = per, by = "term", parallel = clust) Permutation test for dbrda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 163 1.68 0.14 A1 1 39 1.22 0.26 Residual 12 387 > anova(m1rda, permutations = per, by = "term", parallel = clust) Permutation test for rda under reduced model Terms added sequentially (first to last) Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) A1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > anova(mcca, permutations = per, by = "margin", parallel = clust) Permutation test for cca under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) Manure 3 0.274 1.03 0.86 A1 1 0.177 2.00 0.04 * Residual 12 1.062 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mrda, permutations = per, by = "margin", parallel = clust) Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) Manure 3 12.4 1.30 0.12 A1 1 3.5 1.09 0.40 Residual 12 38.1 > anova(mrda1, permutations = per, by = "margin", parallel = clust) Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) Manure 3 4.21 1.12 0.22 A1 1 1.86 1.48 0.14 Residual 12 15.08 > anova(mcap, permutations = per, by = "margin", parallel = clust) Permutation test for capscale under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.627 1.24 0.22 A1 1 0.370 2.19 0.08 . Residual 12 2.021 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mdb, permutations = per, by = "margin", parallel = clust) Permutation test for dbrda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) Manure 3 0.594 1.32 0.18 A1 1 0.367 2.46 0.08 . Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by = "margin", parallel = clust) Permutation test for capscale under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 165 1.46 0.12 A1 1 40 1.07 0.34 Residual 12 452 > anova(mandb, permutations = per, by = "margin", parallel = clust) Permutation test for dbrda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) Manure 3 155 1.60 0.12 A1 1 39 1.22 0.26 Residual 12 387 > anova(m1rda, permutations = per, by = "margin", parallel = clust) Permutation test for rda under reduced model Marginal effects of terms Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) A1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > anova(mcca, permutations = per, by = "axis", parallel = clust) Permutation test for cca under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: cca(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df ChiSquare F Pr(>F) CCA1 1 0.223 2.52 0.26 CCA2 1 0.123 1.39 0.96 CCA3 1 0.064 0.72 1.00 CCA4 1 0.041 0.46 Residual 12 1.062 > anova(mrda, permutations = per, by = "axis", parallel = clust) Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env) Df Variance F Pr(>F) RDA1 1 8.5 2.69 0.10 RDA2 1 4.3 1.36 0.62 RDA3 1 2.2 0.69 0.98 RDA4 1 1.7 0.54 0.98 Residual 12 38.1 > anova(mrda1, permutations = per, by = "axis", parallel = clust) Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, scale = TRUE) Df Variance F Pr(>F) RDA1 1 2.61 2.08 0.22 RDA2 1 1.70 1.35 0.62 RDA3 1 1.10 0.88 0.94 RDA4 1 0.73 0.58 0.98 Residual 12 15.08 > anova(mcap, permutations = per, by = "axis", parallel = clust) Permutation test for capscale under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) CAP1 1 0.674 4.00 0.12 CAP2 1 0.243 1.44 0.66 CAP3 1 0.105 0.62 0.98 CAP4 1 0.051 0.30 0.98 Residual 12 2.021 > anova(mdb, permutations = per, by = "axis", parallel = clust) Permutation test for dbrda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "bray") Df SumOfSqs F Pr(>F) dbRDA1 1 0.667 4.45 0.08 . dbRDA2 1 0.238 1.59 0.58 dbRDA3 1 0.091 0.61 0.98 dbRDA4 1 0.038 0.26 0.98 Residual 12 1.796 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 > anova(mancap, permutations = per, by = "axis", parallel = clust) Permutation test for capscale under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: capscale(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) CAP1 1 126 3.33 0.18 CAP2 1 49 1.31 0.74 CAP3 1 24 0.64 0.98 CAP4 1 14 0.37 0.98 Residual 12 452 > anova(mandb, permutations = per, by = "axis", parallel = clust) Permutation test for dbrda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: dbrda(formula = dune ~ Condition(Management) + Manure + A1, data = dune.env, distance = "manhattan") Df Variance F Pr(>F) dbRDA1 1 123 3.81 0.18 dbRDA2 1 47 1.47 0.68 dbRDA3 1 22 0.68 0.98 dbRDA4 1 10 0.31 0.98 Residual 12 387 > anova(m1rda, permutations = per, by = "axis", parallel = clust) Permutation test for rda under reduced model Forward tests for axes Permutation: free Number of permutations: 49 Model: rda(formula = H ~ A1, data = dune.env) Df Variance F Pr(>F) RDA1 1 0.0029 0.75 0.44 Residual 18 0.0703 > > # stop the cluster as we are finished > stopCluster(clust) > > ## the following do not all work with partial models > > mcca <- cca(dune ~ Management + Manure + A1, dune.env) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mrda <- rda(dune ~ Management + Manure + A1, dune.env) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mrda1 <- rda(dune ~ Management + Manure + A1, dune.env, scale = TRUE) Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mcap <- capscale(dune ~ Management + Manure + A1, dune.env, dist = "bray") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mdb <- dbrda(dune ~ Management + Manure + A1, dune.env, dist = "bray") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mancap <- capscale(dune ~ Management + Manure + A1, dune.env, dist = "man") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > mandb <- dbrda(dune ~ Management + Manure + A1, dune.env, dist = "man") Some constraints or conditions were aliased because they were redundant. This can happen if terms are linearly dependent (collinear): 'Manure^4' > > head(calibrate(mcca)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.883602 -0.178091 0.36500 0.7922315 0.78355 0.57312 1.3024 2 -0.257595 -0.069182 0.46841 0.2174342 -0.27084 0.18996 4.2482 3 -0.055206 -0.068419 1.04034 0.6060948 0.21723 0.09595 4.2281 4 -0.304369 0.132054 1.21814 0.5919769 0.71695 0.40928 4.8630 5 0.961520 0.091569 -0.12497 -0.0029732 -0.49357 -0.11564 4.9642 6 1.449919 0.075602 -0.42298 -0.0535267 -0.68574 -0.40418 4.7055 > head(calibrate(mrda)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.29401 0.408403 0.440828 0.34294 0.960228 0.25907 3.1367 2 -0.32193 -0.210940 0.525258 0.25761 -0.426433 0.32086 4.7427 3 -0.06329 -0.382552 1.295568 0.88816 0.051051 0.20844 3.0244 4 -0.23072 -0.025851 1.263604 0.66627 0.592289 0.48117 5.1389 5 1.09411 0.333615 -0.086119 0.10122 -0.218722 -0.47284 5.6906 6 1.54974 0.128610 -0.647474 -0.24172 -0.688913 -0.24163 4.9992 > head(calibrate(mrda1)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.4195172 0.195602 0.489930 0.408945 0.63673 0.144131 3.2457 2 -0.2555697 -0.159948 0.512425 0.339689 -0.36751 0.098438 4.1415 3 0.0088535 -0.149923 1.023768 0.659460 0.19479 0.184543 3.8499 4 -0.4283707 -0.045680 1.486302 0.828962 0.77205 0.620009 4.2044 5 0.8791230 0.132390 -0.061982 0.027179 -0.43322 -0.200905 4.8834 6 1.5945412 0.099612 -0.601114 -0.217085 -0.77311 -0.325635 4.9080 > head(calibrate(mcap)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.35351 0.1299840 0.73772 0.83346 1.150928 0.34051 1.5819 2 -0.27328 -0.1807926 0.57688 0.36108 -0.389540 0.11337 4.5618 3 -0.11521 -0.1378795 1.07627 0.50500 0.078248 0.33544 5.1742 4 -0.23830 0.0078955 1.18601 0.55891 0.374392 0.27273 4.5439 5 1.06102 0.4254412 0.13710 0.28460 0.040009 -0.59962 4.9298 6 1.21797 0.1198752 -0.54117 -0.26797 -0.686710 -0.18564 4.7785 > head(calibrate(mdb)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 1.05945 0.29233 0.45310 0.87163 1.686742 0.39091 0.84665 2 -0.34870 -0.23103 0.57147 0.27070 -0.480259 0.28872 4.72537 3 -0.47320 -0.16902 1.26895 0.52349 -0.119173 0.25712 5.74253 4 -0.58626 -0.12331 1.27795 0.50225 0.035999 0.30065 4.71082 5 1.33269 0.79710 0.31597 0.70260 0.684932 -1.30598 5.00807 6 1.02170 0.04279 -0.45446 -0.19833 -0.946468 -0.41926 4.83736 > head(calibrate(mancap)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.1281240 0.49145 0.55983 0.38470 1.01262 0.05207 2.9860 2 -0.2879151 -0.26123 0.60310 0.35832 -0.34121 0.38002 4.7783 3 0.0069596 -0.30212 1.12067 0.67840 -0.01814 0.29692 5.0313 4 -0.1350836 -0.18933 1.31950 0.83427 0.60909 0.59970 3.2827 5 1.0725483 0.45851 0.19538 0.40446 0.19234 -0.66824 4.1253 6 1.2857657 0.17740 -0.75589 -0.46798 -0.90859 -0.30014 5.4699 > head(calibrate(mandb)) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 1 0.66644 0.97505 0.47379 0.66422 2.09891 -0.22874 1.9653 2 -0.38822 -0.34825 0.53059 0.12940 -0.50764 0.77856 5.6362 3 -0.32924 -0.58837 1.19831 0.53616 -0.68211 0.41320 5.7036 4 -0.33720 -0.38668 1.32790 0.69699 0.18678 0.76423 3.6311 5 1.79594 1.19611 0.29836 1.11710 1.76562 -1.62223 2.1351 6 0.94598 -0.19327 -0.59457 -0.39197 -1.80062 -0.68601 5.2833 > > head(calibrate(mcca, newdata=dune[11:15,])) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 11 0.233858 0.13861 -0.32589 -0.67553 -0.10659 0.96310 2.3507 12 -0.260642 -0.13316 1.17152 -0.18386 -0.69386 0.46843 5.9871 13 -0.081594 -0.30854 1.18097 0.46491 -0.63881 -0.47286 4.3479 14 -0.502316 1.06326 0.16060 -0.79843 0.41515 -0.15379 12.3207 15 0.559981 0.90354 -0.15982 -0.50872 0.61926 -0.36272 10.2026 > head(calibrate(mrda, newdata=dune[11:15,])) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 11 0.201986 0.28140 -0.260060 -0.57309 0.052355 0.6582554 1.8489 12 -0.321917 -0.25129 1.208336 -0.11711 -0.804022 0.3936137 4.4676 13 -0.032546 -0.39458 1.249458 0.62224 -0.686520 -0.5422918 3.4391 14 -0.351860 0.97669 -0.021120 -0.83760 0.391761 0.0098888 12.3969 15 0.294308 1.01918 -0.072777 -0.57590 0.635282 -0.4504748 9.7828 > head(calibrate(mrda1, newdata=dune[11:15,])) ManagementHF ManagementNM ManagementSF Manure.L Manure.Q Manure.C A1 11 0.155890 0.255231 -0.302207 -0.73488 -0.013315 0.90939 2.6425 12 -0.218095 -0.072054 1.150670 -0.14978 -0.564254 0.42810 5.9670 13 -0.049615 -0.458412 1.292784 0.66760 -0.789885 -0.72793 4.0747 14 -0.234464 1.037849 0.029142 -0.75498 0.435175 -0.26652 11.9376 15 0.298221 1.006569 -0.102984 -0.60489 0.611461 -0.36576 10.3615 > ## head(calibrate(m1rda, newdata=dune[11:15,]))## fails > > head(predict(mcca, newdata = dune.env)) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.24866 1.73029 0.136732 1.56567 0.176689 0.69298 0.51661 -0.0066751 2 3.38022 -1.33575 0.103100 0.65804 1.869170 2.61940 3.71719 -0.0403493 3 0.34629 4.86894 -0.077815 3.49921 -0.100555 1.55684 1.16941 0.0037988 4 0.40505 5.40077 -0.058917 3.93512 -0.076134 1.75018 1.31398 0.0028763 5 1.40721 1.39415 -0.409583 1.63738 1.727211 0.95730 1.09875 -0.0725665 6 1.90089 -0.08191 0.153453 1.79584 2.717164 1.04159 1.19230 -0.1108163 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 0.3441739 -0.23208 -0.45345 2.1049 0.057806 0.185956 -0.099929 0.061638 2 -0.0040270 -0.17500 -0.50790 1.1357 0.043588 0.520382 -1.422491 -1.092553 3 0.7797384 0.13208 0.25806 4.6558 -0.032898 -0.105828 0.056870 -0.035078 4 0.8760877 0.10000 0.19539 5.2394 -0.024908 -0.080127 0.043059 -0.026559 5 0.0159981 0.69521 0.92329 2.6923 -0.173160 -0.707302 0.863780 0.925259 6 -0.0059938 -0.26047 -0.99453 3.0401 0.064876 0.040954 0.517926 1.308129 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 3.3037 0.084120 2.3947 2.2427 -0.13537 0.0087837 0.960535 0.29588 2 6.5539 2.660088 3.7155 5.1158 -0.18507 0.9898833 0.020732 0.22310 3 6.9004 -0.047873 4.9787 5.0651 0.07704 -0.0049989 1.892379 -0.16839 4 7.7960 -0.036247 5.6267 5.6922 0.05833 -0.0037848 2.147087 -0.12749 5 3.0005 3.113739 2.2475 5.1987 0.18798 4.5983853 0.523997 -0.88631 6 4.0551 3.851490 3.0574 5.6731 -0.39474 5.1723104 0.972355 0.33206 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 0.82391 0.0197135 0.31089 0.0069474 0.74251 -0.088855 2 5.26245 0.9904728 4.99741 0.5754882 1.78447 -0.172948 3 1.48233 -0.0112191 1.28649 -0.0039538 1.52865 0.050568 4 1.69376 -0.0084944 1.40263 -0.0029936 1.72884 0.038287 5 2.64699 2.4979708 4.08460 -0.2462139 3.06843 -0.011518 6 3.51252 2.8764755 3.60655 -0.2438153 3.61943 -0.409696 > predict(mrda, newdata = dune.env[1:4,]) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.42151 3.5776 0.169954 2.96527 0.22365 1.3183 0.98345 -0.0071714 2 3.31141 -1.2047 0.068507 0.67566 1.81429 2.5802 3.64850 -0.0373735 3 0.28468 4.2330 -0.093768 3.01916 -0.12339 1.3416 1.00913 0.0039566 4 0.29380 4.1893 -0.076186 3.01557 -0.10026 1.3401 1.00742 0.0032148 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 0.6537462 -0.25642 -0.48190 3.9671 0.071560 0.23272 -0.093331 0.071945 2 -0.0052081 -0.10336 -0.33218 1.0902 0.028845 0.43864 -1.382448 -1.108931 3 0.6737952 0.14147 0.26587 4.0182 -0.039481 -0.12840 0.051493 -0.039694 4 0.6724586 0.11495 0.21602 4.0147 -0.032078 -0.10432 0.041838 -0.032251 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 6.1341 0.110732 4.4479 4.2624 -0.132118 0.0061432 1.75867 0.36823 2 6.3299 2.630842 3.5634 5.1093 -0.122221 1.0714418 -0.03188 0.14843 3 5.9260 -0.061094 4.2701 4.3725 0.072893 -0.0033894 1.61591 -0.20316 4 5.9399 -0.049639 4.2820 4.3651 0.059225 -0.0027539 1.62542 -0.16507 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 1.5171 0.0205888 0.77684 0.0121579 1.4156 -0.082638 2 5.1431 1.0427819 4.94453 0.5221421 1.7573 -0.136759 3 1.2319 -0.0113594 1.12312 -0.0067078 1.2879 0.045593 4 1.2509 -0.0092295 1.10004 -0.0054501 1.2965 0.037045 > predict(mrda1, newdata = dune.env[1:4,]) Achimill Agrostol Airaprae Alopgeni Anthodor Bellpere Bromhord Chenalbu 1 0.42151 3.5776 0.169954 2.96527 0.22365 1.3183 0.98345 -0.0071714 2 3.31141 -1.2047 0.068507 0.67566 1.81429 2.5802 3.64850 -0.0373735 3 0.28468 4.2330 -0.093768 3.01916 -0.12339 1.3416 1.00913 0.0039566 4 0.29380 4.1893 -0.076186 3.01557 -0.10026 1.3401 1.00742 0.0032148 Cirsarve Comapalu Eleopalu Elymrepe Empenigr Hyporadi Juncarti Juncbufo 1 0.6537462 -0.25642 -0.48190 3.9671 0.071560 0.23272 -0.093331 0.071945 2 -0.0052081 -0.10336 -0.33218 1.0902 0.028845 0.43864 -1.382448 -1.108931 3 0.6737952 0.14147 0.26587 4.0182 -0.039481 -0.12840 0.051493 -0.039694 4 0.6724586 0.11495 0.21602 4.0147 -0.032078 -0.10432 0.041838 -0.032251 Lolipere Planlanc Poaprat Poatriv Ranuflam Rumeacet Sagiproc Salirepe 1 6.1341 0.110732 4.4479 4.2624 -0.132118 0.0061432 1.75867 0.36823 2 6.3299 2.630842 3.5634 5.1093 -0.122221 1.0714418 -0.03188 0.14843 3 5.9260 -0.061094 4.2701 4.3725 0.072893 -0.0033894 1.61591 -0.20316 4 5.9399 -0.049639 4.2820 4.3651 0.059225 -0.0027539 1.62542 -0.16507 Scorautu Trifprat Trifrepe Vicilath Bracruta Callcusp 1 1.5171 0.0205888 0.77684 0.0121579 1.4156 -0.082638 2 5.1431 1.0427819 4.94453 0.5221421 1.7573 -0.136759 3 1.2319 -0.0113594 1.12312 -0.0067078 1.2879 0.045593 4 1.2509 -0.0092295 1.10004 -0.0054501 1.2965 0.037045 > predict(mcap, newdata = dune.env[1:4,]) 1 2 3 2 0.4410552 3 0.1132396 0.4889419 4 0.1056903 0.4850746 0.0075493 > predict(mdb, newdata = dune.env[1:4,]) 1 2 3 2 0.4101123 3 0.1129160 0.4608659 4 0.1053882 0.4567899 0.0075277 > predict(mancap, newdata = dune.env[1:4,]) 1 2 3 2 8.064421 3 1.182080 8.684763 4 1.103275 8.639762 0.078805 > predict(mandb, newdata = dune.env[1:4,]) 1 2 3 2 7.600620 3 1.170623 8.248343 4 1.092581 8.201556 0.078042 > predict(m1rda, newdata = dune.env[1:4,]) col1 1 2.2142 2 2.1968 3 2.1769 4 2.1794 > > ## the sign is arbitrary > abs(predict(mcca, newdata = dune[1:4,], type="wa")) CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 1 1.62466 0.11383 1.78622 0.47896 2.12030 1.39557 3.08506 2 1.06133 0.20433 1.30825 0.37139 0.76061 1.33289 1.37522 3 0.71033 0.77748 0.81637 0.93095 0.66982 0.70982 0.35968 4 0.64819 0.72329 1.44737 1.14892 1.84672 0.30213 0.86769 > abs(predict(mrda, newdata = dune[1:4,], type="wa")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 1 0.18535 0.022419 0.40394 0.28201 0.401393 0.0089804 0.17894 2 0.34065 0.015217 0.15589 0.29898 0.481298 0.3110483 0.19056 3 0.20203 0.385177 0.21607 0.16160 0.166526 0.0095877 0.15736 4 0.11484 0.349445 0.34771 0.24737 0.051871 0.3004530 0.11508 > abs(predict(mrda1, newdata = dune[1:4,], type="wa")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 1 0.095172 0.104425 0.22637 0.17358 0.30374 0.152736 0.308891 2 0.365200 0.034168 0.28660 0.18871 0.10331 0.478363 0.288641 3 0.141907 0.261778 0.22356 0.14939 0.13501 0.111882 0.075503 4 0.147735 0.348054 0.43875 0.29730 0.40430 0.024991 0.312684 > > ## the sign is arbitrary > abs(predict(mcca, newdata = dune[,1:4], type="sp")) CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 Achimill 0.84687 1.0701 0.23634 0.82289 1.50817 1.01313 0.550205 Agrostol 0.77155 1.3011 0.42471 0.53466 0.71397 0.13494 0.051337 Airaprae 2.15432 3.1226 0.52500 2.50252 1.15845 1.26403 0.033265 Alopgeni 0.14431 1.6487 0.42805 1.37756 0.61985 0.93554 0.806710 > abs(predict(mrda, newdata = dune[,1:4], type="sp")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 Achimill 0.156357 0.109758 0.051875 0.155370 0.192307 0.021201 0.05592900 Agrostol 0.245787 0.453963 0.162518 0.018188 0.172589 0.119162 0.01098465 Airaprae 0.052576 0.094503 0.014547 0.129765 0.049461 0.117122 0.08994751 Alopgeni 0.024101 0.516842 0.132875 0.087468 0.396101 0.104365 0.00093612 > abs(predict(mrda1, newdata = dune[,1:4], type="sp")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 Achimill 0.257242 0.15508 0.023807 0.12405544 0.167384 0.214501 0.028569 Agrostol 0.196652 0.30574 0.103167 0.00039545 0.045675 0.006216 0.010224 Airaprae 0.117004 0.25667 0.028025 0.25799706 0.066905 0.086901 0.012198 Alopgeni 0.031859 0.35291 0.051066 0.12889917 0.190337 0.197400 0.181314 > > abs(predict(mcca, newdata = dune.env[1:4,], type="lc")) CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 1 0.98522 0.18116 1.06369 1.50574 1.3173 0.28751 0.141532 2 1.13730 1.06013 0.82297 0.86803 1.2579 1.77285 1.123271 3 0.80654 0.70480 1.31221 0.80414 1.4683 0.13208 0.010662 4 0.81845 0.66990 1.29564 0.85092 1.4582 0.14245 0.000516 > abs(predict(mrda, newdata = dune.env[1:4,], type="lc")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 1 0.18523 0.20623 0.316247 0.30245 0.049422 0.025451 0.16224 2 0.36803 0.22424 0.027466 0.34105 0.454235 0.099307 0.29753 3 0.15757 0.25460 0.326062 0.19053 0.121045 0.141679 0.14397 4 0.15942 0.25137 0.325408 0.19799 0.116270 0.133930 0.14518 > abs(predict(mrda1, newdata = dune.env[1:4,], type="lc")) RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 1 0.14664 0.19636 0.27651 0.30865 0.25564 0.021393 0.012121 2 0.41723 0.24524 0.14249 0.22618 0.15529 0.482284 0.212404 3 0.11814 0.26110 0.30710 0.15053 0.29502 0.020007 0.034328 4 0.12004 0.25679 0.30507 0.16108 0.29239 0.017247 0.032847 > abs(predict(mcap, newdata = dune.env[1:4,], type="lc")) CAP1 CAP2 CAP3 CAP4 CAP5 CAP6 CAP7 1 0.27462 0.15007 0.21773 0.28034 0.099690 0.245828 0.058665 2 0.32370 0.22297 0.10532 0.25592 0.502586 0.084408 0.341630 3 0.21968 0.23163 0.27891 0.14326 0.097533 0.278465 0.020906 4 0.22334 0.22620 0.27483 0.15240 0.097677 0.276289 0.023424 > abs(predict(mdb, newdata = dune.env[1:4,], type="lc")) dbRDA1 dbRDA2 dbRDA3 dbRDA4 dbRDA5 dbRDA6 dbRDA7 1 0.27666 0.14786 0.21628 0.27463 0.10935 0.23099 0.108744 2 0.32039 0.21931 0.11598 0.22087 0.13951 0.50786 0.342277 3 0.22194 0.22991 0.27926 0.13863 0.14881 0.24437 0.080877 4 0.22559 0.22444 0.27506 0.14769 0.14618 0.24348 0.082735 > abs(predict(mancap, newdata = dune.env[1:4,], type="lc")) CAP1 CAP2 CAP3 CAP4 CAP5 CAP6 CAP7 1 0.15906 0.20999 0.26950 0.273591 0.085989 0.26571 0.053587 2 0.38906 0.25364 0.20949 0.094601 0.472958 0.21394 0.263674 3 0.13353 0.26897 0.28834 0.159016 0.012678 0.29389 0.050910 4 0.13523 0.26503 0.28708 0.166654 0.017565 0.29201 0.043944 > abs(predict(m1rda, newdata = dune.env[1:4,], type="lc")) RDA1 1 0.215813 2 0.142121 3 0.057901 4 0.068429 > abs(predict(mandb, newdata = dune.env[1:4,])) 1 2 3 2 7.600620 3 1.170623 8.248343 4 1.092581 8.201556 0.078042 > ## reset > options(op) > > proc.time() user system elapsed 3.17 0.46 5.34