[Vegan-commits] r2145 - pkg/vegan/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Apr 24 12:06:18 CEST 2012
Author: jarioksa
Date: 2012-04-24 12:06:17 +0200 (Tue, 24 Apr 2012)
New Revision: 2145
Modified:
pkg/vegan/tests/Examples/vegan-Ex.Rout.save
Log:
update tests for adipart/hierpat/multipart changes
Modified: pkg/vegan/tests/Examples/vegan-Ex.Rout.save
===================================================================
--- pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-04-23 19:12:10 UTC (rev 2144)
+++ pkg/vegan/tests/Examples/vegan-Ex.Rout.save 2012-04-24 10:06:17 UTC (rev 2145)
@@ -1,5 +1,5 @@
-R Under development (unstable) (2012-04-16 r59057) -- "Unsuffered Consequences"
+R Under development (unstable) (2012-04-24 r59157) -- "Unsuffered Consequences"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-unknown-linux-gnu (64-bit)
@@ -161,7 +161,7 @@
Formula:
y ~ poly(x1, 1) + poly(x2, 1)
-<environment: 0x336a260>
+<environment: 0x2fe66b8>
Total model degrees of freedom 3
GCV score: 0.0427924
@@ -491,7 +491,8 @@
> ### Name: adipart
> ### Title: Additive Diversity Partitioning and Hierarchical Null Model
> ### Testing
-> ### Aliases: adipart hiersimu print.hiersimu
+> ### Aliases: adipart adipart.default adipart.formula print.adipart hiersimu
+> ### hiersimu.default hiersimu.formula print.hiersimu
> ### Keywords: multivariate
>
> ### ** Examples
@@ -521,29 +522,47 @@
> 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=19)
+> adipart(mite, index="richness", nsimul=19)
adipart with 19 simulations using method “r2dtable”
with index richness, weights unif
- statistic z 2.5% 50% 97.5% Pr(sim.)
-alpha.1 15.1143 -38.4304 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 9.1184 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 *
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 15.114 -38.430 22.032 22.300 22.608 0.05 *
+gamma 35.000 0.000 35.000 35.000 35.000 1.00
+beta.1 19.886 38.430 12.392 12.700 12.968 0.05 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> adipart(mite ~ ., levsm, index="richness", nsimul=19)
+adipart with 19 simulations using method “r2dtable”
+with index richness, weights unif
+
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 15.1143 -46.2370 22.1257 22.4429 22.6236 0.05 *
+alpha.2 29.7500 -21.7076 34.3625 35.0000 35.0000 0.05 *
+alpha.3 33.0000 0.0000 35.0000 35.0000 35.0000 0.05 *
+gamma 35.0000 0.0000 35.0000 35.0000 35.0000 1.00
+beta.1 14.6357 9.0407 12.0075 12.4286 12.8743 0.05 *
+beta.2 3.2500 13.1373 0.0000 0.0000 0.6375 0.05 *
+beta.3 2.0000 0.0000 0.0000 0.0000 0.0000 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=19)
+> hiersimu(mite, FUN=diversity, relative=TRUE, nsimul=19)
hiersimu with 19 simulations
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+level_1 0.76064 -71.19509 0.93487 0.93856 0.9444 0.05 *
+leve_2 1.00000 0.00000 1.00000 1.00000 1.0000 1.00
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> hiersimu(mite ~., levsm, FUN=diversity, relative=TRUE, nsimul=19)
+hiersimu with 19 simulations
+
statistic z 2.5% 50% 97.5% Pr(sim.)
-l1 0.76064 -72.78152 0.93511 0.93959 0.9424 0.05 *
-l2 0.89736 -117.34300 0.99619 0.99815 0.9989 0.05 *
-l3 0.92791 -462.37200 0.99914 0.99939 0.9997 0.05 *
+l1 0.76064 -75.13855 0.93389 0.93819 0.9427 0.05 *
+l2 0.89736 -110.96786 0.99699 0.99814 0.9999 0.05 *
+l3 0.92791 -417.33833 0.99904 0.99943 0.9996 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
@@ -553,9 +572,9 @@
hiersimu with 19 simulations
statistic z 2.5% 50% 97.5% Pr(sim.)
-l1 0.52070 5.05179 0.31808 0.36836 0.4227 0.05 *
-l2 0.60234 11.31794 0.10507 0.17096 0.2283 0.05 *
-l3 0.67509 16.06868 -0.27784 -0.19160 -0.0895 0.05 *
+l1 0.52070 8.52164 0.32262 0.35107 0.3848 0.05 *
+l2 0.60234 14.38537 0.09670 0.15043 0.1969 0.05 *
+l3 0.67509 20.31622 -0.23479 -0.19594 -0.0988 0.05 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
@@ -2087,7 +2106,7 @@
>
> ### Name: decostand
> ### Title: Standardization Methods for Community Ecology
-> ### Aliases: decostand wisconsin
+> ### Aliases: decostand wisconsin scoverage
> ### Keywords: multivariate manip
>
> ### ** Examples
@@ -2112,8 +2131,11 @@
> sptrans <- decostand(varespec, "chi.square")
> plot(procrustes(rda(sptrans), cca(varespec)))
>
+> data(mite)
+> sptrans <- scoverage(mite)
>
>
+>
> cleanEx()
> nameEx("designdist")
> ### * designdist
@@ -3891,7 +3913,7 @@
>
> ### Name: multipart
> ### Title: Multiplicative Diversity Partitioning
-> ### Aliases: multipart
+> ### Aliases: multipart multipart.default multipart.formula print.multipart
> ### Keywords: multivariate
>
> ### ** Examples
@@ -3915,7 +3937,7 @@
+ 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=19)
+> multipart(mite, levsm, index="renyi", scales=1, nsimul=19)
multipart with 19 simulations
with index renyi, scales 1, global TRUE
@@ -3929,32 +3951,46 @@
beta.3 1.17939 460.54976 1.00083 1.00148 1.0021 0.05 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+> 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 8.0555 -66.6425 12.1143 12.2120 12.2910 0.05 *
+alpha.2 11.2353 -94.6265 14.0246 14.0917 14.1193 0.05 *
+alpha.3 12.0064 -355.9621 14.1283 14.1374 14.1485 0.05 *
+gamma 14.1603 0.0000 14.1603 14.1603 14.1603 1.00
+beta.1 1.3568 35.0277 1.1478 1.1565 1.1660 0.05 *
+beta.2 1.0710 33.4227 1.0015 1.0035 1.0078 0.05 *
+beta.3 1.1794 419.1662 1.0008 1.0016 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 -66.642517 12.114272 12.212001 12.2910 0.05 *
-alpha.2 11.235261 -94.626473 14.024555 14.091674 14.1193 0.05 *
-alpha.3 12.006443 -355.962100 14.128270 14.137438 14.1485 0.05 *
+alpha.1 8.055481 -53.543897 12.071168 12.198307 12.3689 0.05 *
+alpha.2 11.235261 -99.506116 14.032767 14.076147 14.1247 0.05 *
+alpha.3 12.006443 -343.945577 14.123604 14.136297 14.1438 0.05 *
gamma 14.160271 0.000000 14.160271 14.160271 14.1603 1.00
-beta.1 0.078594 27.638115 0.067625 0.068160 0.0688 0.05 *
-beta.2 0.535514 33.422699 0.500756 0.501738 0.5039 0.05 *
-beta.3 0.589695 419.166208 0.500417 0.500808 0.5011 0.05 *
+beta.1 0.078594 19.567843 0.067236 0.068392 0.0691 0.05 *
+beta.2 0.535514 35.965825 0.500294 0.502062 0.5035 0.05 *
+beta.3 0.589695 404.814230 0.500583 0.500848 0.5013 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 -53.5439 12.0712 12.1983 12.3689 0.05 *
-alpha.2 11.2353 -99.5061 14.0328 14.0761 14.1247 0.05 *
-alpha.3 12.0064 -343.9456 14.1236 14.1363 14.1438 0.05 *
-gamma 14.1603 0.0000 14.1603 14.1603 14.1603 1.00
-beta.1 1.7578 81.2944 1.1448 1.1608 1.1731 0.05 *
-beta.2 1.2603 124.6871 1.0025 1.0060 1.0091 0.05 *
-beta.3 1.1794 404.8142 1.0012 1.0017 1.0026 0.05 *
+ statistic z 2.5% 50% 97.5% Pr(sim.)
+alpha.1 8.05548 -69.63045 12.09434 12.18504 12.3160 0.05 *
+alpha.2 11.23526 -81.67569 14.05248 14.09208 14.1625 0.05 *
+alpha.3 12.00644 -321.41937 14.12429 14.13991 14.1456 0.05 *
+gamma 14.16027 0.00000 14.16027 14.16027 14.1603 1.00
+beta.1 1.75784 105.55202 1.14975 1.16210 1.1708 0.05 *
+beta.2 1.26034 102.69533 0.99985 1.00484 1.0077 0.05 *
+beta.3 1.17939 378.33472 1.00104 1.00144 1.0025 0.05 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
@@ -4730,7 +4766,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9905320>
+<environment: 0x99098a8>
Estimated degrees of freedom:
6.4351 total = 7.43507
@@ -4746,7 +4782,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x8caa2e0>
+<environment: 0x8ff01d8>
Estimated degrees of freedom:
6.1039 total = 7.103853
@@ -4902,7 +4938,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x9b50e30>
+<environment: 0x9b5efd0>
Estimated degrees of freedom:
8.9275 total = 9.927492
@@ -4915,7 +4951,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xa64ecc8>
+<environment: 0x8e85b98>
Estimated degrees of freedom:
7.7529 total = 8.75294
@@ -4928,7 +4964,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0x961bac8>
+<environment: 0xa37abe0>
Estimated degrees of freedom:
8.8962 total = 9.89616
@@ -7467,7 +7503,7 @@
Formula:
y ~ s(x1, x2, k = knots)
-<environment: 0xa026f68>
+<environment: 0x971c8e8>
Estimated degrees of freedom:
2 total = 3
@@ -7947,7 +7983,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 24.949 0.148 25.187 0 0
+Time elapsed: 25.241 0.208 25.54 0 0
> grDevices::dev.off()
null device
1
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