[Vegan-commits] r1589 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Apr 11 20:54:48 CEST 2011
Author: jarioksa
Date: 2011-04-11 20:54:47 +0200 (Mon, 11 Apr 2011)
New Revision: 1589
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2011-04-11 18:45:15 UTC (rev 1588)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2011-04-11 18:54:47 UTC (rev 1589)
@@ -22,7 +22,7 @@
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('vegan')
-This is vegan 1.18-28
+This is vegan 1.18-29
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
> cleanEx()
@@ -539,37 +539,43 @@
> plot(mite.xy, main="l3", col=as.numeric(levsm$l3)+1)
> par(mfrow=c(1,1))
> ## Additive diversity partitioning
-> adipart(mite ~., levsm, index="richness", nsimul=9)
-adipart with 9 simulations
+> adipart(mite ~., levsm, index="richness", nsimul=19)
+adipart with 19 simulations
with index richness, weights unif
- statistic z 2.5% 50% 97.5% Pr(sim.)
-alpha.1 15.114 -44.931 22.217 22.386 22.594 0.1
-alpha.2 29.750 -28.284 34.500 34.750 35.000 0.1
-alpha.3 33.000 0.000 35.000 35.000 35.000 0.1
-gamma 35.000 0.000 35.000 35.000 35.000 1.0
-beta.1 14.636 11.309 12.156 12.286 12.710 0.1
-beta.2 3.250 16.971 0.000 0.250 0.500 0.1
-beta.3 2.000 0.000 0.000 0.000 0.000 0.1
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 15.1143 -38.7550 22.0321 22.3000 22.608 0.05 *
+alpha.2 29.7500 -27.1142 34.5000 34.7500 35.000 0.05 *
+alpha.3 33.0000 0.0000 35.0000 35.0000 35.000 0.05 *
+gamma 35.0000 0.0000 35.0000 35.0000 35.000 1.00
+beta.1 14.6357 9.0433 12.1629 12.4500 12.955 0.05 *
+beta.2 3.2500 16.4371 0.0000 0.2500 0.500 0.05 *
+beta.3 2.0000 0.0000 0.0000 0.0000 0.000 0.05 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Hierarchical null model testing
> ## diversity analysis (similar to adipart)
-> hiersimu(mite ~., levsm, diversity, relative=TRUE, nsimul=9)
-hiersimu with 9 simulations
+> hiersimu(mite ~., levsm, diversity, relative=TRUE, nsimul=19)
+hiersimu with 19 simulations
- statistic z 2.5% 50% 97.5% Pr(sim.)
-l1 0.76064 -61.57993 0.93421 0.93718 0.9418 0.1
-l2 0.89736 -98.45713 0.99629 0.99723 0.9992 0.1
-l3 0.92791 -550.28201 0.99914 0.99931 0.9995 0.1
-l4 1.00000 0.00000 1.00000 1.00000 1.0000 1.0
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+l1 0.76064 -65.47286 0.93511 0.93959 0.9437 0.05 *
+l2 0.89736 -127.77766 0.99635 0.99815 0.9989 0.05 *
+l3 0.92791 -516.33891 0.99921 0.99948 0.9997 0.05 *
+l4 1.00000 0.00000 1.00000 1.00000 1.0000 1.00
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Hierarchical testing with the Morisita index
> morfun <- function(x) dispindmorisita(x)$imst
-> hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=9)
-hiersimu with 9 simulations
+> hiersimu(mite ~., levsm, morfun, drop.highest=TRUE, nsimul=19)
+hiersimu with 19 simulations
- statistic z 2.5% 50% 97.5% Pr(sim.)
-l1 0.520702 9.445924 0.336022 0.363409 0.3815 0.1
-l2 0.602337 9.398210 0.098335 0.135400 0.2364 0.1
-l3 0.675085 18.347360 -0.293851 -0.189212 -0.1565 0.1
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+l1 0.52070 4.98527 0.31016 0.36570 0.4227 0.05 *
+l2 0.60234 12.33099 0.11979 0.17096 0.2283 0.05 *
+l3 0.67509 19.37352 -0.24164 -0.16761 -0.0895 0.05 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -3047,7 +3053,7 @@
>
> # Reproduce the results shown in Table 2 of Legendre (2005), a single group
> mite.small <- mite.hel[c(4,9,14,22,31,34,45,53,61,69),c(13:15,23)]
-> kendall.global(mite.small, nperm=99)
+> kendall.global(mite.small, nperm=49)
$Concordance_analysis
Group.1
W 0.44160305
@@ -3058,13 +3064,13 @@
attr(,"class")
[1] "kendall.global"
-> kendall.post(mite.small, mult="holm", nperm=99)
+> kendall.post(mite.small, mult="holm", nperm=49)
$A_posteriori_tests
TVEL ONOV SUCT Trhypch1
Spearman.mean 0.3265678 0.3965503 0.4570402 -0.1681251
W.per.species 0.4949258 0.5474127 0.5927802 0.1239061
-Prob 0.0500000 0.0200000 0.0100000 0.6700000
-Corrected prob 0.1000000 0.0600000 0.0400000 0.6700000
+Prob 0.1400000 0.0200000 0.0200000 0.7200000
+Corrected prob 0.2800000 0.0800000 0.0800000 0.7200000
$Correction.type
[1] "holm"
@@ -3074,7 +3080,7 @@
>
> # Reproduce the results shown in Tables 3 and 4 of Legendre (2005), 2 groups
> group <-c(1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,2,1,2,1,1,1,1,2,1,2,1,1,1,1,1,2,2,2,2,2)
-> kendall.global(mite.hel, group=group, nperm=99)
+> kendall.global(mite.hel, group=group, nperm=49)
$Concordance_analysis
Group.1 Group.2
W 3.097870e-01 2.911888e-01
@@ -3082,49 +3088,49 @@
Prob.F 1.177138e-85 4.676566e-22
Corrected prob.F 2.354275e-85 4.676566e-22
Chi2 5.130073e+02 2.210123e+02
-Prob.perm 1.000000e-02 1.000000e-02
-Corrected prob.perm 2.000000e-02 2.000000e-02
+Prob.perm 2.000000e-02 2.000000e-02
+Corrected prob.perm 4.000000e-02 4.000000e-02
$Correction.type
[1] "holm"
attr(,"class")
[1] "kendall.global"
-> kendall.post(mite.hel, group=group, mult="holm", nperm=99)
+> kendall.post(mite.hel, group=group, mult="holm", nperm=49)
$A_posteriori_tests_Group
$A_posteriori_tests_Group[[1]]
Brachy PHTH RARD SSTR Protopl MEGR
Spearman.mean 0.1851177 0.4258111 0.359058 0.2505486 0.1802160 0.2833298
W.per.species 0.2190711 0.4497357 0.385764 0.2817757 0.2143736 0.3131911
-Prob 0.0100000 0.0100000 0.010000 0.0100000 0.0400000 0.0100000
-Corrected prob 0.3500000 0.3500000 0.350000 0.3500000 0.3500000 0.3500000
+Prob 0.0200000 0.0200000 0.020000 0.0200000 0.0200000 0.0200000
+Corrected prob 0.7000000 0.7000000 0.700000 0.7000000 0.7000000 0.7000000
MPRO HMIN HMIN2 NPRA TVEL ONOV
Spearman.mean 0.09248024 0.2444656 0.4138494 0.1263751 0.4177343 0.3301159
W.per.species 0.13029357 0.2759462 0.4382723 0.1627761 0.4419954 0.3580278
-Prob 0.08000000 0.0100000 0.0100000 0.0400000 0.0100000 0.0100000
-Corrected prob 0.35000000 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000
+Prob 0.22000000 0.0200000 0.0200000 0.0400000 0.0200000 0.0200000
+Corrected prob 0.70000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
SUCT Oribatl1 PWIL Galumna1 Stgncrs2 HRUF
Spearman.mean 0.2185421 0.421216 0.2574779 0.4180699 0.3623428 0.1250230
W.per.species 0.2511028 0.445332 0.2884163 0.4423170 0.3889118 0.1614804
-Prob 0.0100000 0.010000 0.0100000 0.0100000 0.0100000 0.0500000
-Corrected prob 0.3500000 0.350000 0.3500000 0.3500000 0.3500000 0.3500000
+Prob 0.0200000 0.020000 0.0200000 0.0200000 0.0200000 0.0400000
+Corrected prob 0.7000000 0.700000 0.7000000 0.7000000 0.7000000 0.7000000
PPEL SLAT FSET Lepidzts Eupelops Miniglmn
Spearman.mean 0.2188216 0.3016159 0.4217606 0.2577037 0.1108022 0.2301430
W.per.species 0.2513707 0.3307153 0.4458539 0.2886327 0.1478521 0.2622203
-Prob 0.0100000 0.0100000 0.0100000 0.0100000 0.0700000 0.0100000
-Corrected prob 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000
+Prob 0.0200000 0.0200000 0.0200000 0.0200000 0.0600000 0.0200000
+Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
$A_posteriori_tests_Group[[2]]
HPAV TVIE LCIL Ceratoz1 Trhypch1 NCOR
Spearman.mean 0.1222579 0.2712078 0.1906408 0.1375601 0.1342409 0.3342345
W.per.species 0.2020527 0.3374616 0.2642189 0.2159637 0.2129463 0.3947586
-Prob 0.0500000 0.0100000 0.0200000 0.0200000 0.0400000 0.0100000
-Corrected prob 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000
+Prob 0.0800000 0.0200000 0.0200000 0.0200000 0.0400000 0.0200000
+Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
LRUG PLAG2 Ceratoz3 Oppiminu Trimalc2
Spearman.mean 0.3446561 0.1833099 0.3188922 0.1764232 0.2498877
W.per.species 0.4042328 0.2575544 0.3808111 0.2512938 0.3180797
-Prob 0.0100000 0.0100000 0.0100000 0.0100000 0.0100000
-Corrected prob 0.3500000 0.3500000 0.3500000 0.3500000 0.3500000
+Prob 0.0200000 0.0200000 0.0200000 0.0200000 0.0200000
+Corrected prob 0.7000000 0.7000000 0.7000000 0.7000000 0.7000000
$Correction.type
@@ -3133,7 +3139,7 @@
attr(,"class")
[1] "kendall.post"
>
-> # NOTE: 'nperm' argument usually needs to be larger than 99.
+> # NOTE: 'nperm' argument usually needs to be larger than 49.
> # It was set to this low value for demonstration purposes.
>
>
@@ -3795,42 +3801,48 @@
+ l3=cutter(mite.xy$y, cut = seq(0, 10, by = 5)),
+ l4=cutter(mite.xy$y, cut = seq(0, 10, by = 10)))
> ## Multiplicative diversity partitioning
-> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=9)
-multipart with 9 simulations
+> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19)
+multipart with 19 simulations
with index renyi, scales 1, global TRUE
- statistic z 2.5% 50% 97.5% Pr(sim.)
-alpha.1 8.05548 -51.02503 12.11957 12.20548 12.3611 0.1
-alpha.2 11.23526 -97.75402 14.06702 14.09107 14.1493 0.1
-alpha.3 12.00644 -351.14109 14.13038 14.13932 14.1476 0.1
-gamma 14.16027 0.00000 14.16027 14.16027 14.1603 1.0
-beta.1 1.35678 24.85873 1.14268 1.15598 1.1643 0.1
-beta.2 1.07103 33.37286 0.99935 1.00319 1.0056 0.1
-beta.3 1.17939 413.49699 1.00090 1.00148 1.0021 0.1
-> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=9, relative=TRUE)
-multipart with 9 simulations
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 8.05548 -50.70213 12.06155 12.17739 12.3400 0.05 *
+alpha.2 11.23526 -81.96187 14.03610 14.08098 14.1485 0.05 *
+alpha.3 12.00644 -352.92816 14.12822 14.13863 14.1485 0.05 *
+gamma 14.16027 0.00000 14.16027 14.16027 14.1603 1.00
+beta.1 1.35678 27.78084 1.14474 1.15859 1.1683 0.05 *
+beta.2 1.07103 28.03171 0.99912 1.00368 1.0067 0.05 *
+beta.3 1.17939 415.61573 1.00083 1.00153 1.0023 0.05 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, relative=TRUE)
+multipart with 19 simulations
with index renyi, scales 1, global TRUE
- statistic z 2.5% 50% 97.5% Pr(sim.)
-alpha.1 8.055481 -52.029990 12.058266 12.166664 12.2796 0.1
-alpha.2 11.235261 -74.976817 14.029198 14.060144 14.1372 0.1
-alpha.3 12.006443 -455.862328 14.128554 14.136459 14.1420 0.1
-gamma 14.160271 0.000000 14.160271 14.160271 14.1603 1.0
-beta.1 0.078594 27.718478 0.067828 0.068339 0.0689 0.1
-beta.2 0.535514 25.325379 0.499875 0.502749 0.5036 0.1
-beta.3 0.589695 536.670846 0.500646 0.500842 0.5011 0.1
-> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=9, global=TRUE)
-multipart with 9 simulations
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 8.055481 -58.367006 12.114272 12.212001 12.3367 0.05 *
+alpha.2 11.235261 -101.405343 14.030529 14.092411 14.1193 0.05 *
+alpha.3 12.006443 -397.121850 14.130733 14.140903 14.1485 0.05 *
+gamma 14.160271 0.000000 14.160271 14.160271 14.1603 1.00
+beta.1 0.078594 24.964962 0.067424 0.068173 0.0688 0.05 *
+beta.2 0.535514 34.653906 0.500756 0.501720 0.5039 0.05 *
+beta.3 0.589695 467.673224 0.500417 0.500685 0.5010 0.05 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> multipart(mite ~ ., levsm, index="renyi", scales=1, nsimul=19, global=TRUE)
+multipart with 19 simulations
with index renyi, scales 1, global TRUE
- statistic z 2.5% 50% 97.5% Pr(sim.)
-alpha.1 8.0555 -60.1230 12.1089 12.1625 12.2818 0.1
-alpha.2 11.2353 -85.0716 14.0297 14.0945 14.1205 0.1
-alpha.3 12.0064 -449.3895 14.1332 14.1371 14.1461 0.1
-gamma 14.1603 0.0000 14.1603 14.1603 14.1603 1.0
-beta.1 1.7578 91.0706 1.1529 1.1643 1.1694 0.1
-beta.2 1.2603 106.4842 1.0028 1.0047 1.0093 0.1
-beta.3 1.1794 529.2934 1.0010 1.0016 1.0019 0.1
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 8.0555 -51.9567 12.0474 12.1983 12.3527 0.05 *
+alpha.2 11.2353 -97.0303 14.0328 14.0761 14.1247 0.05 *
+alpha.3 12.0064 -369.9941 14.1236 14.1345 14.1428 0.05 *
+gamma 14.1603 0.0000 14.1603 14.1603 14.1603 1.00
+beta.1 1.7578 78.6700 1.1463 1.1608 1.1754 0.05 *
+beta.2 1.2603 121.5731 1.0025 1.0060 1.0091 0.05 *
+beta.3 1.1794 435.4695 1.0012 1.0018 1.0026 0.05 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
>
>
@@ -4253,65 +4265,6 @@
PC12 PC13
0.3107 0.2273
-> ordistep(rda(dune ~ 1, dune.env), scope = formula(mod1), perm.max = 200)
-
-Start: dune ~ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Management 3 87.082 2.8400 199 0.005 **
-+ Moisture 3 87.707 2.5883 199 0.005 **
-+ Manure 4 89.232 1.9539 199 0.010 **
-+ A1 1 89.591 1.9217 199 0.055 .
-+ Use 2 91.032 1.1741 99 0.290
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Management
-
- Df AIC F N.Perm Pr(>F)
-- Management 3 89.62 2.84 99 0.01 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Moisture 3 85.567 1.9764 199 0.02 *
-+ Manure 3 87.517 1.3902 99 0.16
-+ A1 1 87.424 1.2965 99 0.27
-+ Use 2 88.284 1.0510 99 0.37
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
-Step: dune ~ Management + Moisture
-
- Df AIC F N.Perm Pr(>F)
-- Moisture 3 87.082 1.9764 99 0.01 **
-- Management 3 87.707 2.1769 99 0.01 **
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-
- Df AIC F N.Perm Pr(>F)
-+ Manure 3 85.762 1.1225 99 0.35
-+ A1 1 86.220 0.8359 99 0.62
-+ Use 2 86.842 0.8027 99 0.76
-
-Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
-
- Inertia Proportion Rank
-Total 84.1237 1.0000
-Constrained 46.4249 0.5519 6
-Unconstrained 37.6988 0.4481 13
-Inertia is variance
-
-Eigenvalues for constrained axes:
- RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
-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
-
>
> ## Example without scope. Default direction is "backward"
> ordistep(mod1, perm.max = 200)
@@ -4319,54 +4272,54 @@
Start: dune ~ A1 + Moisture + Management + Use + Manure
Df AIC F N.Perm Pr(>F)
-- Use 2 86.056 0.8330 99 0.73
-- A1 1 85.933 0.7933 99 0.60
-- Manure 3 87.357 1.0737 99 0.51
-- Management 2 87.672 1.1976 99 0.27
-- Moisture 3 88.818 1.3320 99 0.20
+- Use 2 86.056 0.8330 99 0.69
+- A1 1 85.933 0.7933 99 0.64
+- Manure 3 87.357 1.0737 99 0.41
+- Management 2 87.672 1.1976 99 0.29
+- Moisture 3 88.818 1.3320 99 0.22
Step: dune ~ A1 + Moisture + Management + Manure
Df AIC F N.Perm Pr(>F)
-- A1 1 85.762 0.8015 99 0.670
-- Manure 3 86.220 1.0829 99 0.330
-- Management 2 86.688 1.1728 99 0.240
+- A1 1 85.762 0.8015 99 0.650
+- Manure 3 86.220 1.0829 99 0.360
+- Management 2 86.688 1.1728 99 0.270
- Moisture 3 87.779 1.4140 199 0.135
Step: dune ~ Moisture + Management + Manure
Df AIC F N.Perm Pr(>F)
-- Manure 3 85.567 1.1225 99 0.32
-- Management 2 86.060 1.1986 99 0.21
-- Moisture 3 87.517 1.5788 199 0.05 *
+- Management 2 86.060 1.1986 99 0.33
+- Manure 3 85.567 1.1225 99 0.30
+- Moisture 3 87.517 1.5788 99 0.03 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-Step: dune ~ Moisture + Management
+Step: dune ~ Moisture + Manure
- Df AIC F N.Perm Pr(>F)
-- Moisture 3 87.082 1.9764 99 0.01 **
-- Management 3 87.707 2.1769 99 0.01 **
+ Df AIC F N.Perm Pr(>F)
+- Manure 4 87.707 1.8598 99 0.02 *
+- Moisture 3 89.232 2.3275 99 0.01 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-Call: rda(formula = dune ~ Moisture + Management, data = dune.env)
+Call: rda(formula = dune ~ Moisture + Manure, data = dune.env)
Inertia Proportion Rank
Total 84.1237 1.0000
-Constrained 46.4249 0.5519 6
-Unconstrained 37.6988 0.4481 13
+Constrained 49.1609 0.5844 7
+Unconstrained 34.9628 0.4156 12
Inertia is variance
Eigenvalues for constrained axes:
- RDA1 RDA2 RDA3 RDA4 RDA5 RDA6
-21.588 14.075 4.123 3.163 2.369 1.107
+ RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7
+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
-8.2409 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
+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
>
> ## Example of ordistep, forward
@@ -4377,9 +4330,9 @@
Df AIC F N.Perm Pr(>F)
+ Management 3 87.082 2.8400 199 0.005 **
+ Moisture 3 87.707 2.5883 199 0.005 **
-+ Manure 4 89.232 1.9539 199 0.015 *
-+ A1 1 89.591 1.9217 199 0.050 *
-+ Use 2 91.032 1.1741 99 0.250
++ Manure 4 89.232 1.9539 199 0.010 **
++ A1 1 89.591 1.9217 199 0.045 *
++ Use 2 91.032 1.1741 99 0.350
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -4387,18 +4340,18 @@
Df AIC F N.Perm Pr(>F)
+ Moisture 3 85.567 1.9764 199 0.005 **
-+ Manure 3 87.517 1.3902 199 0.100 .
-+ A1 1 87.424 1.2965 99 0.280
-+ Use 2 88.284 1.0510 99 0.360
++ Manure 3 87.517 1.3902 199 0.095 .
++ A1 1 87.424 1.2965 99 0.180
++ Use 2 88.284 1.0510 99 0.380
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Step: dune ~ Management + Moisture
Df AIC F N.Perm Pr(>F)
-+ Manure 3 85.762 1.1225 99 0.35
-+ A1 1 86.220 0.8359 99 0.56
-+ Use 2 86.842 0.8027 99 0.69
++ Manure 3 85.762 1.1225 99 0.32
++ A1 1 86.220 0.8359 99 0.61
++ Use 2 86.842 0.8027 99 0.66
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
@@ -4583,7 +4536,7 @@
<none> 0.3177536
Df AIC F N.Perm Pr(>F)
-+ Substrate 6 -87.768 1.8251 199 0.005 **
++ Substrate 6 -87.768 1.8251 199 0.01 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -4617,12 +4570,11 @@
Step: R2.adj= 0.4367038
Call: mite.hel ~ WatrCont + Shrub + Substrate + Topo + SubsDens
-> step.res <- ordiR2step(mod0, scope = formula(mod1), direction="forward", trace=0)
> step.res$anova # Summary table
R2.adj Df AIC F N.Perm Pr(>F)
+ WatrCont 0.26085 1 -84.336 25.3499 199 0.005 **
+ Shrub 0.31775 2 -88.034 3.8360 199 0.005 **
-+ Substrate 0.36536 6 -87.768 1.8251 199 0.005 **
++ Substrate 0.36536 6 -87.768 1.8251 199 0.010 **
+ Topo 0.40042 1 -90.924 4.5095 199 0.005 **
+ SubsDens 0.43670 1 -94.489 4.7999 199 0.005 **
<All variables> 0.43670
@@ -4663,7 +4615,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x1036f60a0>
+<environment: 0x1034ff030>
Estimated degrees of freedom:
6.2955 total = 7.295494
@@ -4679,7 +4631,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x1060bec30>
+<environment: 0x10486ac98>
Estimated degrees of freedom:
4.9207 total = 5.920718
@@ -4835,7 +4787,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x106c72e68>
+<environment: 0x105dda708>
Estimated degrees of freedom:
8.9275 total = 9.927492
@@ -4848,7 +4800,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10707ca38>
+<environment: 0x1070190e8>
Estimated degrees of freedom:
7.7529 total = 8.75294
@@ -4861,7 +4813,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x10704a200>
+<environment: 0x10695daa0>
Estimated degrees of freedom:
8.8962 total = 9.89616
@@ -7457,31 +7409,6 @@
6.6269 4.3091 3.5491 2.5465 2.3403 0.9335 0.6121
> plot(mod1)
-> ## Overall permutation test for all variables
-> anova(mod1)
-Permutation test for rda under reduced model
-
-Model: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure, data = dune.env)
- Df Var F N.Perm Pr(>F)
-Model 12 63.206 1.7627 199 0.005 **
-Residual 7 20.917
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-> ## Permutation test for terms added sequentially
-> anova(mod1, by = "term")
-Permutation test for rda under reduced model
-Terms added sequentially (first to last)
-
-Model: rda(formula = dune ~ A1 + Moisture + Management + Use + Manure, data = dune.env)
- Df Var F N.Perm Pr(>F)
-A1 1 8.1148 2.7156 99 0.01 **
-Moisture 3 21.6497 2.4150 99 0.01 **
-Management 3 19.1153 2.1323 99 0.02 *
-Use 2 4.7007 0.7865 99 0.70
-Manure 3 9.6257 1.0737 99 0.40
-Residual 7 20.9175
----
-Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Automatic selection of variables by permutation P-values
> mod <- ordistep(mod0, scope=formula(mod1))
@@ -7491,8 +7418,8 @@
+ Management 3 87.082 2.8400 199 0.005 **
+ Moisture 3 87.707 2.5883 199 0.005 **
+ Manure 4 89.232 1.9539 199 0.005 **
-+ A1 1 89.591 1.9217 999 0.040 *
-+ Use 2 91.032 1.1741 99 0.250
++ A1 1 89.591 1.9217 999 0.045 *
++ Use 2 91.032 1.1741 99 0.210
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -7503,11 +7430,11 @@
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
- Df AIC F N.Perm Pr(>F)
-+ Moisture 3 85.567 1.9764 199 0.01 **
-+ Manure 3 87.517 1.3902 99 0.18
-+ A1 1 87.424 1.2965 99 0.24
-+ Use 2 88.284 1.0510 99 0.37
+ Df AIC F N.Perm Pr(>F)
++ Moisture 3 85.567 1.9764 299 0.0200 *
++ Manure 3 87.517 1.3902 299 0.1133
++ A1 1 87.424 1.2965 99 0.1800
++ Use 2 88.284 1.0510 99 0.4000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -7522,7 +7449,7 @@
Df AIC F N.Perm Pr(>F)
+ Manure 3 85.762 1.1225 99 0.27
+ A1 1 86.220 0.8359 99 0.70
-+ Use 2 86.842 0.8027 99 0.77
++ Use 2 86.842 0.8027 99 0.76
> mod
Call: rda(formula = dune ~ Management + Moisture, data = dune.env)
@@ -7544,6 +7471,16 @@
0.3107 0.2273
> plot(mod)
+> ## Permutation test for all variables
+> anova(mod)
+Permutation test for rda under reduced model
+
+Model: rda(formula = dune ~ Management + Moisture, data = dune.env)
+ Df Var F N.Perm Pr(>F)
+Model 6 46.425 2.6682 199 0.005 **
+Residual 13 37.699
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> ## Permutation test of "type III" effects, or significance when a term
> ## is added to the model after all other terms
> anova(mod, by = "margin")
@@ -7574,7 +7511,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x1080d9440>
+<environment: 0x1080db7c0>
Estimated degrees of freedom:
2 total = 3
@@ -7587,14 +7524,14 @@
Call:
adonis(formula = dune ~ ., data = dune.env)
- Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
-A1 1 0.7230 0.72295 5.2038 0.16817 0.001 ***
-Moisture 3 1.1871 0.39569 2.8482 0.27613 0.004 **
-Management 3 0.9036 0.30121 2.1681 0.21019 0.028 *
-Use 2 0.0921 0.04606 0.3315 0.02143 0.973
-Manure 3 0.4208 0.14026 1.0096 0.09787 0.489
-Residuals 7 0.9725 0.13893 0.22621
-Total 19 4.2990 1.00000
+ Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
+A1 1 0.7230 0.72295 5.2038 0.16817 0.002 **
+Moisture 3 1.1871 0.39569 2.8482 0.27613 0.003 **
+Management 3 0.9036 0.30121 2.1681 0.21019 0.025 *
+Use 2 0.0921 0.04606 0.3315 0.02143 0.976
+Manure 3 0.4208 0.14026 1.0096 0.09787 0.490
+Residuals 7 0.9725 0.13893 0.22621
+Total 19 4.2990 1.00000
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> adonis(dune ~ Management + Moisture, dune.env)
@@ -8051,7 +7988,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 108.803 1.165 111.255 0 0
+Time elapsed: 106.217 1.196 108.951 0 0
> grDevices::dev.off()
null device
1
More information about the Vegan-commits
mailing list