[Vegan-commits] r2480 - in pkg/vegan: R tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Mar 16 22:01:00 CET 2013
Author: jarioksa
Date: 2013-03-16 22:01:00 +0100 (Sat, 16 Mar 2013)
New Revision: 2480
Modified:
pkg/vegan/R/print.cca.R
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
zapsmall eigenvalues in cca/rda/capscale
Modified: pkg/vegan/R/print.cca.R
===================================================================
--- pkg/vegan/R/print.cca.R 2013-03-16 20:47:06 UTC (rev 2479)
+++ pkg/vegan/R/print.cca.R 2013-03-16 21:01:00 UTC (rev 2480)
@@ -42,18 +42,18 @@
"deleted due to missingness\n")
if (!is.null(x$CCA) && x$CCA$rank > 0) {
cat("\nEigenvalues for constrained axes:\n")
- print(x$CCA$eig, digits = digits, ...)
+ print(zapsmall(x$CCA$eig, digits = digits), ...)
}
if (!is.null(x$CA) && x$CA$rank > 0) {
ax.lim <- 8
ax.trig <- 16
cat("\nEigenvalues for unconstrained axes:\n")
if (x$CA$rank > ax.trig) {
- print(x$CA$eig[1:ax.lim], digits = digits, ...)
+ print(zapsmall(x$CA$eig[1:ax.lim], digits = digits), ...)
cat("(Showed only", ax.lim, "of all", x$CA$rank,
"unconstrained eigenvalues)\n")
}
- else print(x$CA$eig, digits = digits, ...)
+ else print(zapsmall(x$CA$eig, digits = digits), ...)
}
cat("\n")
invisible(x)
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-03-16 20:47:06 UTC (rev 2479)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2013-03-16 21:01:00 UTC (rev 2480)
@@ -425,14 +425,14 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3
-0.41868 0.13304 0.07659
+ CCA1 CCA2 CCA3
+0.4187 0.1330 0.0766
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.409782 0.225913 0.176062 0.123389 0.108171 0.090751 0.085878 0.060894
- CA9 CA10 CA11 CA12 CA13 CA14 CA15 CA16
-0.056606 0.046688 0.041926 0.020103 0.014335 0.009917 0.008505 0.008033
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
+0.4098 0.2259 0.1761 0.1234 0.1082 0.0908 0.0859 0.0609 0.0566 0.0467 0.0419
+ CA12 CA13 CA14 CA15 CA16
+0.0201 0.0143 0.0099 0.0085 0.0080
> ## see ?ordistep to do the same, but based on permutation P-values
> ## Not run:
@@ -1545,10 +1545,10 @@
0.5413 0.3265 0.1293
Eigenvalues for unconstrained axes:
- MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
-0.906518 0.512743 0.337915 0.262598 0.203220 0.161762 0.124174 0.085570
- MDS9 MDS10 MDS11 MDS12 MDS13 MDS14 MDS15
-0.068881 0.058346 0.050083 0.027738 0.020839 0.007306 0.001345
+ MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8 MDS9 MDS10 MDS11
+0.9065 0.5127 0.3379 0.2626 0.2032 0.1618 0.1242 0.0856 0.0689 0.0583 0.0501
+ MDS12 MDS13 MDS14 MDS15
+0.0277 0.0208 0.0073 0.0013
> plot(vare.cap)
> anova(vare.cap)
@@ -1691,16 +1691,14 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
-0.438870 0.291775 0.162847 0.142130 0.117952 0.089029 0.070295 0.058359
- CCA9 CCA10 CCA11 CCA12 CCA13 CCA14
-0.031141 0.013294 0.008364 0.006538 0.006156 0.004733
+ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10 CCA11
+0.4389 0.2918 0.1628 0.1421 0.1180 0.0890 0.0703 0.0584 0.0311 0.0133 0.0084
+ CCA12 CCA13 CCA14
+0.0065 0.0062 0.0047
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.197765 0.141926 0.101174 0.070787 0.053303 0.033299 0.018868 0.015104
- CA9
-0.009488
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9
+0.19776 0.14193 0.10117 0.07079 0.05330 0.03330 0.01887 0.01510 0.00949
> plot(vare.cca)
> ## Formula interface and a better model
@@ -1716,8 +1714,8 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
-0.37563 0.23419 0.14067 0.13229 0.10675 0.05614
+ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6
+0.3756 0.2342 0.1407 0.1323 0.1068 0.0561
Eigenvalues for unconstrained axes:
CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
@@ -1736,12 +1734,12 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1
-0.1572
+ CCA1
+0.15722
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.47455 0.29389 0.21403 0.19541 0.17482 0.11711 0.11207 0.08797
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
+0.4745 0.2939 0.2140 0.1954 0.1748 0.1171 0.1121 0.0880
(Showed only 8 of all 22 unconstrained eigenvalues)
> cca(varespec ~ Ca + Condition(pH), varechem)
@@ -1755,12 +1753,12 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1
-0.1827
+ CCA1
+0.18269
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.38343 0.27487 0.21233 0.17599 0.17013 0.11613 0.10892 0.08797
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
+0.3834 0.2749 0.2123 0.1760 0.1701 0.1161 0.1089 0.0880
(Showed only 8 of all 21 unconstrained eigenvalues)
> ## RDA
@@ -2320,14 +2318,14 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3
-0.41868 0.13304 0.07659
+ CCA1 CCA2 CCA3
+0.4187 0.1330 0.0766
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.409782 0.225913 0.176062 0.123389 0.108171 0.090751 0.085878 0.060894
- CA9 CA10 CA11 CA12 CA13 CA14 CA15 CA16
-0.056606 0.046688 0.041926 0.020103 0.014335 0.009917 0.008505 0.008033
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
+0.4098 0.2259 0.1761 0.1234 0.1082 0.0908 0.0859 0.0609 0.0566 0.0467 0.0419
+ CA12 CA13 CA14 CA15 CA16
+0.0201 0.0143 0.0099 0.0085 0.0080
>
>
@@ -2969,10 +2967,10 @@
Some constraints were aliased because they were collinear (redundant)
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
-0.46713 0.34102 0.17606 0.15317 0.09528 0.07027 0.05887 0.04993 0.03183 0.02596
- CCA11 CCA12
-0.02282 0.01082
+ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10 CCA11
+0.4671 0.3410 0.1761 0.1532 0.0953 0.0703 0.0589 0.0499 0.0318 0.0260 0.0228
+ CCA12
+0.0108
Eigenvalues for unconstrained axes:
CA1 CA2 CA3 CA4 CA5 CA6 CA7
@@ -3929,8 +3927,8 @@
Inertia is mean squared contingency coefficient
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.36621 0.13278 0.07232 0.06579 0.05587 0.04812 0.04183 0.03907
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
+0.3662 0.1328 0.0723 0.0658 0.0559 0.0481 0.0418 0.0391
(Showed only 8 of all 34 unconstrained eigenvalues)
mso variogram:
@@ -3961,10 +3959,10 @@
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8
-0.312067 0.066011 0.041167 0.029377 0.024385 0.015907 0.012008 0.007517
- CCA9 CCA10 CCA11
-0.006115 0.003732 0.002837
+ CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCA7 CCA8 CCA9 CCA10
+0.31207 0.06601 0.04117 0.02938 0.02438 0.01591 0.01201 0.00752 0.00612 0.00373
+ CCA11
+0.00284
Eigenvalues for unconstrained axes:
CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
@@ -4689,10 +4687,8 @@
21.588 14.075 4.123 3.163 2.369 1.107
Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
-8.2409 7.1380 5.3547 4.4086 3.1430 2.7697 1.8779 1.7409 0.9517 0.9088 0.6265
- PC12 PC13
-0.3107 0.2273
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
+8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
>
> ## Example without scope. Default direction is "backward"
@@ -4745,10 +4741,8 @@
20.538 15.067 5.585 3.327 1.972 1.428 1.244
Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
-8.0368 6.1066 5.1262 3.5404 3.4157 2.6188 2.1608 1.3604 1.1717 0.8532 0.3513
- PC12
-0.2208
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12
+8.037 6.107 5.126 3.540 3.416 2.619 2.161 1.360 1.172 0.853 0.351 0.221
>
> ## Example of ordistep, forward
@@ -4908,7 +4902,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x109f51310>
+<environment: 0x1093ac510>
Estimated degrees of freedom:
6.45 total = 7.45
@@ -4924,7 +4918,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10ad8d908>
+<environment: 0x109b1a9f0>
Estimated degrees of freedom:
6.12 total = 7.12
@@ -5092,7 +5086,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10a846c38>
+<environment: 0x108f97b20>
Estimated degrees of freedom:
8.93 total = 9.93
@@ -5105,7 +5099,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10ad6a668>
+<environment: 0x108cdacd0>
Estimated degrees of freedom:
7.75 total = 8.75
@@ -5118,7 +5112,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10aab6478>
+<environment: 0x109fc3c00>
Estimated degrees of freedom:
8.9 total = 9.9
@@ -5619,16 +5613,14 @@
Inertia is variance
Eigenvalues for constrained axes:
- RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10
-25.2823 8.2969 6.0442 4.7662 4.1482 3.8568 3.5875 3.3341 3.0874 2.5513
- RDA11 RDA12 RDA13 RDA14 RDA15 RDA16 RDA17 RDA18 RDA19 RDA20
- 2.4664 2.2089 2.1287 1.9409 1.7987 1.6216 1.5787 1.4403 1.3984 1.2839
- RDA21 RDA22 RDA23 RDA24 RDA25 RDA26 RDA27 RDA28 RDA29 RDA30
- 1.2108 1.1335 1.0011 0.9230 0.8625 0.7880 0.7497 0.7122 0.6847 0.6110
- RDA31 RDA32 RDA33 RDA34 RDA35 RDA36 RDA37 RDA38 RDA39 RDA40
- 0.5844 0.5369 0.5159 0.4416 0.4174 0.4036 0.3678 0.3397 0.3391 0.3055
- RDA41 RDA42 RDA43 RDA44
- 0.2788 0.2714 0.2046 0.1790
+ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
+25.282 8.297 6.044 4.766 4.148 3.857 3.587 3.334 3.087 2.551 2.466
+ RDA12 RDA13 RDA14 RDA15 RDA16 RDA17 RDA18 RDA19 RDA20 RDA21 RDA22
+ 2.209 2.129 1.941 1.799 1.622 1.579 1.440 1.398 1.284 1.211 1.133
+ RDA23 RDA24 RDA25 RDA26 RDA27 RDA28 RDA29 RDA30 RDA31 RDA32 RDA33
+ 1.001 0.923 0.862 0.788 0.750 0.712 0.685 0.611 0.584 0.537 0.516
+ RDA34 RDA35 RDA36 RDA37 RDA38 RDA39 RDA40 RDA41 RDA42 RDA43 RDA44
+ 0.442 0.417 0.404 0.368 0.340 0.339 0.306 0.279 0.271 0.205 0.179
Eigenvalues for unconstrained axes:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
@@ -5753,10 +5745,10 @@
0.24932 0.12090 0.08160 0.05904
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.306366 0.131911 0.115157 0.109469 0.077242 0.075754 0.048714 0.037582
- CA9 CA10 CA11 CA12
-0.031058 0.021024 0.012542 0.009277
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10
+0.30637 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871 0.03758 0.03106 0.02102
+ CA11 CA12
+0.01254 0.00928
> cca(fitted(mod))
Call: cca(X = fitted(mod))
@@ -5779,10 +5771,10 @@
Inertia is mean squared contingency coefficient
Eigenvalues for unconstrained axes:
- CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8
-0.306366 0.131911 0.115157 0.109469 0.077242 0.075754 0.048714 0.037582
- CA9 CA10 CA11 CA12
-0.031058 0.021024 0.012542 0.009277
+ CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10
+0.30637 0.13191 0.11516 0.10947 0.07724 0.07575 0.04871 0.03758 0.03106 0.02102
+ CA11 CA12
+0.01254 0.00928
> # Remove rare species (freq==1) from 'cca' and find their scores
> # 'passively'.
@@ -6467,10 +6459,8 @@
26.706 16.538 8.096 4.667 3.374 2.877
Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
-5.2767 4.0703 3.2553 2.4618 2.3414 1.9173 1.6654 1.3028 1.0773 0.7701 0.6705
- PC12 PC13
-0.5398 0.1766
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
+5.277 4.070 3.255 2.462 2.341 1.917 1.665 1.303 1.077 0.770 0.670 0.540 0.177
> ## An impression of confidence regions of site scores
> plot(mod, display="sites")
@@ -7572,14 +7562,14 @@
Some constraints were aliased because they were collinear (redundant)
Eigenvalues for constrained axes:
- RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10
-22.3955 16.2076 7.0389 4.0380 3.7602 2.6087 2.1669 1.8033 1.4042 0.9174
- RDA11 RDA12
- 0.5815 0.2839
+ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8 RDA9 RDA10 RDA11
+22.396 16.208 7.039 4.038 3.760 2.609 2.167 1.803 1.404 0.917 0.582
+ RDA12
+ 0.284
Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7
-6.6269 4.3091 3.5491 2.5465 2.3403 0.9335 0.6121
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7
+6.627 4.309 3.549 2.546 2.340 0.934 0.612
> plot(mod1)
> ## Automatic selection of variables by permutation P-values
@@ -7638,10 +7628,8 @@
21.588 14.075 4.123 3.163 2.369 1.107
Eigenvalues for unconstrained axes:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11
-8.2409 7.1380 5.3547 4.4086 3.1430 2.7697 1.8779 1.7409 0.9517 0.9088 0.6265
- PC12 PC13
-0.3107 0.2273
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13
+8.241 7.138 5.355 4.409 3.143 2.770 1.878 1.741 0.952 0.909 0.627 0.311 0.227
> plot(mod)
> ## Permutation test for all variables
@@ -7684,7 +7672,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x1095f6430>
+<environment: 0x10b1dddf8>
Estimated degrees of freedom:
2 total = 3
@@ -8236,7 +8224,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 90.285 2.151 101.802 0 0
+Time elapsed: 84.211 1.577 87.547 0 0
> grDevices::dev.off()
null device
1
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