[med-svn] [r-cran-vegan] 04/06: Imported Upstream version 2.0-10
Andreas Tille
tille at debian.org
Tue Jan 21 08:45:27 UTC 2014
This is an automated email from the git hooks/post-receive script.
tille pushed a commit to branch master
in repository r-cran-vegan.
commit 50945653dd8c5fd83cb1c314b95a0f11ce8d2ade
Author: Andreas Tille <tille at debian.org>
Date: Tue Jan 21 09:44:15 2014 +0100
Imported Upstream version 2.0-10
---
DESCRIPTION | 10 ++--
MD5 | 93 +++++++++++++++++------------------
NAMESPACE | 3 --
R/boxplot.specaccum.R | 4 +-
R/confint.fisherfit.R | 6 ---
R/decostand.R | 4 +-
R/envfit.default.R | 10 ++--
R/factorfit.R | 8 ++-
R/fisher.alpha.R | 6 +--
R/fisherfit.R | 67 +++++++++++++++++--------
R/nestednodf.R | 6 +--
R/ordiArrowMul.R | 2 +-
R/permutest.betadisper.R | 53 ++++++++++++++------
R/plot.profile.fisherfit.R | 16 ------
R/plot.renyiaccum.R | 13 +++--
R/plot.specaccum.R | 40 +++++++++++++--
R/print.cca.R | 2 +-
R/print.fisherfit.R | 10 ++--
R/print.oecosimu.R | 4 +-
R/profile.fisherfit.R | 43 ----------------
R/renyiaccum.R | 17 +++++--
R/specaccum.R | 36 +++++++++++---
R/tsallisaccum.R | 5 +-
R/vectorfit.R | 3 +-
inst/ChangeLog | 24 ++++++++-
inst/NEWS.Rd | 75 ++++++++++++++++++++++++++++
inst/doc/FAQ-vegan.pdf | Bin 148690 -> 149064 bytes
inst/doc/NEWS.html | 89 +++++++++++++++++++++++++++++++++
inst/doc/decision-vegan.pdf | Bin 319750 -> 324746 bytes
inst/doc/diversity-vegan.R | 42 +++++++---------
inst/doc/diversity-vegan.Rnw | 15 +-----
inst/doc/diversity-vegan.pdf | Bin 358270 -> 357896 bytes
inst/doc/intro-vegan.pdf | Bin 237324 -> 260455 bytes
man/betadisper.Rd | 4 +-
man/diversity.Rd | 8 +--
man/fisherfit.Rd | 46 ++---------------
man/nobs.adonis.Rd | 7 +--
man/permutest.betadisper.Rd | 7 ++-
man/renyi.Rd | 18 +++++--
man/screeplot.cca.Rd | 5 +-
man/simper.Rd | 2 +-
man/specaccum.Rd | 16 ++++--
man/tsallis.Rd | 111 ++++++++++++++++++++++++++++++------------
vignettes/FAQ-vegan.pdf | Bin 148690 -> 149064 bytes
vignettes/NEWS.html | 89 +++++++++++++++++++++++++++++++++
vignettes/decision-vegan.tex | 20 ++++----
vignettes/diversity-vegan.Rnw | 15 +-----
vignettes/diversity-vegan.tex | 111 +++++++++++++++++-------------------------
vignettes/intro-vegan.tex | 60 ++++++++++++-----------
49 files changed, 760 insertions(+), 465 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index af1ee72..a885386 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,18 +1,18 @@
Package: vegan
Title: Community Ecology Package
-Version: 2.0-9
-Date: September 25, 2013
+Version: 2.0-10
+Date: December 12, 2013
Author: Jari Oksanen, F. Guillaume Blanchet, Roeland Kindt, Pierre Legendre,
Peter R. Minchin, R. B. O'Hara, Gavin L. Simpson, Peter Solymos,
M. Henry H. Stevens, Helene Wagner
Maintainer: Jari Oksanen <jari.oksanen at oulu.fi>
-Depends: permute, lattice, R (>= 2.14.0)
+Depends: permute (>= 0.8-0), lattice, R (>= 2.15.0)
Suggests: MASS, mgcv, cluster, scatterplot3d, rgl, tcltk
Description: Ordination methods, diversity analysis and other
functions for community and vegetation ecologists.
License: GPL-2
URL: http://cran.r-project.org, http://vegan.r-forge.r-project.org/
-Packaged: 2013-09-25 07:30:17 UTC; jarioksa
+Packaged: 2013-12-12 10:13:58 UTC; jarioksa
NeedsCompilation: yes
Repository: CRAN
-Date/Publication: 2013-09-25 09:56:27
+Date/Publication: 2013-12-12 11:32:11
diff --git a/MD5 b/MD5
index 30e6e35..a96a821 100644
--- a/MD5
+++ b/MD5
@@ -1,5 +1,5 @@
-2115d26f94d5b10c4a7c8bd4caa31e54 *DESCRIPTION
-717b1fe683786bc702d85368b504bc38 *NAMESPACE
+bf96136dde2851828d6628a29e5df1ab *DESCRIPTION
+917112eb22f9617b3ee0eb2c3432f659 *NAMESPACE
4b8531b446af54510e5fb31f841aed2f *R/AIC.radfit.R
5c5fdbcdc2a38e2cbafdb8f2c5eb2e08 *R/CCorA.R
6592cf7dc692f87b4a147eb625e18624 *R/MDSrotate.R
@@ -45,7 +45,7 @@ fbec6d133dea10372ce082c7035a8ab2 *R/beals.R
875d40515bf55ee65dc7fcdefb9f52d1 *R/biplot.CCorA.R
e83522ded9481ebde69e61419d0033b7 *R/biplot.rda.R
0999bb90f22b72fade2ca6adbd01758f *R/boxplot.betadisper.R
-f5abc1a3e5417e53a78cf2054f46d0a6 *R/boxplot.specaccum.R
+dd03c1ef27bc56d056dc761fd7ecd153 *R/boxplot.specaccum.R
cbf54233db3c2839101f98e02eb538dd *R/bstick.R
14ba8e7ffce8b0b0cc9e8a8f3160acf3 *R/bstick.cca.R
229bb1ed02b171c8ce0a2bdfb3b37ef6 *R/bstick.decorana.R
@@ -69,12 +69,11 @@ c6c6a44746c586dd8b75204efa17b531 *R/clamtest.R
ea10763445cb76b219d18bb274109df5 *R/coef.rda.R
ab87ce0f23c88b6b40207a7597fa9b64 *R/commsimulator.R
722959743928d23213409c778c6acbc2 *R/confint.MOStest.R
-17c08a04de98c78868754f0040e4528d *R/confint.fisherfit.R
490b90477d595160757812bc06d6a70b *R/contribdiv.R
d0f10f160ac99ba936824a49c608868a *R/cophenetic.spantree.R
edee3aaced61290b219985d0ce69155c *R/coverscale.R
1b1a6343072d69c5ccbf9a72ba068cbd *R/decorana.R
-1169ef2aa4cd76c33c6166b1b253a665 *R/decostand.R
+c22bdcfe87e2bf710db3b301d880a54a *R/decostand.R
476dabb4b68409249d28557129ff3d6c *R/density.anosim.R
4a13947927b175862e2266ff9589f2a0 *R/density.oecosimu.R
f15615231f0bcad3a68ab7d718968251 *R/densityplot.oecosimu.R
@@ -89,7 +88,7 @@ cafeabc2133997b3381c9edf6a971abf *R/distconnected.R
79c66a10794aacaf08f1d28192228bea *R/eigengrad.R
be739eb24b369efbdaefa03537a5418c *R/eigenvals.R
17a62527ee103c09bfba0c851ab12560 *R/envfit.R
-6e63b5fff3b7e834694b8adbdb6c6070 *R/envfit.default.R
+0a315b3c3c55494d08ae86fafd3939f2 *R/envfit.default.R
fe12ea2872df48afc72f59efd3c50c4f *R/envfit.formula.R
f443552fe39ec3d6a259f953f4c3af1b *R/estaccumR.R
81098475867f802dea0565fe426c9fc5 *R/estimateR.R
@@ -98,10 +97,10 @@ fde991da12a66144a0cd1aa30150e258 *R/estimateR.default.R
1df3194c88598964282c114cb8db5513 *R/estimateR.matrix.R
8fadb18ee25d5c3689f437a4d3db0360 *R/eventstar.R
5ad3db71edac392b0513ccb96700af0d *R/extractAIC.cca.R
-ddc17af5f1e4d952cdf0c54048fdc7c0 *R/factorfit.R
+cbf14ecd859d43cf37b1754539e9fefe *R/factorfit.R
7e304b1c384c4d8588e5dbedd9459c73 *R/fieller.MOStest.R
-c9a0a434a146ba9ebe25a9e72f36439c *R/fisher.alpha.R
-05bd12db4d832b01b37b15294e6fa15f *R/fisherfit.R
+ee8330855e6a7bc2350047d76b2209a4 *R/fisher.alpha.R
+2776f68ef40e177303c3b73163036969 *R/fisherfit.R
6baa91137f90af022902e047bde687ce *R/fitspecaccum.R
1db8e420fdd54103774d745d356333b8 *R/fitted.capscale.R
8fc0cd4954e2976b71fe4995291d2fab *R/fitted.cca.R
@@ -157,14 +156,14 @@ f5e79cb1c2dc1fcabb6e6b5cb4dc0828 *R/nestedbetasor.R
85d4744650c1e2a0edf16809b77f7ab4 *R/nestedchecker.R
c15884dd28790c7521ecb33175c86e5c *R/nesteddisc.R
e65023174f4ce8874a2f88f332af5a37 *R/nestedn0.R
-917143cda17f75f9219e7263b79b1fab *R/nestednodf.R
+2f8881df74a6d2ffc9c2f324ec9ce90e *R/nestednodf.R
cf4c7acbbf20366f629dee40c9203764 *R/nestedtemp.R
74b2723851155de631716fa479f8ea38 *R/no.shared.R
47973ff187f68836a19d20ea37c60868 *R/nobs.R
2c24d7eeb78c8149275ce5b6b3c3bd88 *R/oecosimu.R
7b3988a207ecfe1ea574c5857ffcd2a3 *R/orderingKM.R
e3d108eed97633040fa22c2b384e19e4 *R/ordiArgAbsorber.R
-d2d5d94504676e6b1edf25b0c024edf3 *R/ordiArrowMul.R
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898781983832b0c3fef8319a53f4979e *R/ordiArrowTextXY.R
da71b576fb9908051a545375e14a80e0 *R/ordiGetData.R
99c1ec285e2afe4fb8beccbd507a123e *R/ordiNAexclude.R
@@ -204,7 +203,7 @@ c53e9402a842833d80a8df39c0adee6f *R/orglpoints.R
e08110689dfeb1098cb4d9194f084c66 *R/permatfull.R
26a9634c5ad6bc16e2e24c283e33b761 *R/permatswap.R
909306255cee4f36d2ba7ba13d376e90 *R/permuted.index.R
-7aedada06df1c5e0dff74f77e4479fbb *R/permutest.betadisper.R
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4764a3d49455270e5217b72aa4d68787 *R/permutest.cca.R
b4e77b98f86c4b567d687b64e3aa8812 *R/persp.renyiaccum.R
011a26868189ef4f3516a1b1931a2ea1 *R/persp.tsallisaccum.R
@@ -234,15 +233,14 @@ fdc1beae72f52a43883861a8b56bf289 *R/plot.prc.R
6cd9c1a91d03a8afb8f9148f0d369cad *R/plot.preston.R
31b95161a7558e111e3c01778b9d17db *R/plot.prestonfit.R
5159170150e3c6d1ed92b5c3ec984b75 *R/plot.procrustes.R
-c7e5c6d58944d75ab6dac163e051769f *R/plot.profile.fisherfit.R
02ff38f3fb337a63534356255b8641a9 *R/plot.rad.R
fc2dc1b63ae6f50067a7a376c736394b *R/plot.radfit.R
af0dac1922ddd4eac1090ba1dd5b1089 *R/plot.radfit.frame.R
360dec911e8d4e772f888d89b8e0f6f7 *R/plot.radline.R
08f6b41506125e27b37a08b3bb730ffb *R/plot.renyi.R
-90046d0d152cdca37f7868aa61fad5c2 *R/plot.renyiaccum.R
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e71b966111f99c7048ebbe26c1aa6a12 *R/plot.spantree.R
-e449c6ef786f8802c9806b51248b66cc *R/plot.specaccum.R
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abc96c8853871035d494dfa9086d4d6e *R/plot.taxondive.R
6104fadf391072e78a8f2825ac41ceb2 *R/plot.varpart.R
00d109fe7fc29440698b9f1a4bbc876f *R/plot.varpart234.R
@@ -275,11 +273,11 @@ eb223fbfded71ae4f0b374c1e92c3f2e *R/predict.specaccum.R
a530724906dc69888c27a538fc388cbf *R/print.betadisper.R
2945b0c68fb04cb2c7dc460a419c5478 *R/print.bioenv.R
528c225f34769670a4a3049a0e29ae59 *R/print.capscale.R
-f757010a0187dc81e9b844df25c58640 *R/print.cca.R
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6d0cd7929afcbe0d192c980dc5196555 *R/print.decorana.R
65e888e34fa8a8e1d5b577fbadb3161a *R/print.envfit.R
ff355b68b19f8d8c29917ca33d4e8b8d *R/print.factorfit.R
-53efa849e48c5c91a51f42282237253c *R/print.fisherfit.R
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6da316510cb840a6a9dd8d31d0e205af *R/print.humpfit.R
b31dbaa6493fdda1f865f95b3e889aab *R/print.isomap.R
6263b03c7eb5ae61f917888597abc4fd *R/print.mantel.R
@@ -293,7 +291,7 @@ eed481e994c01ec4d7b443fb8cafad46 *R/print.nesteddisc.R
91c6a9b43c8b045d11a4b8834d1c9d47 *R/print.nestedn0.R
0f8e3935f95b67b96e066435743bbee0 *R/print.nestednodf.R
2f1732fffc2fb487420a910a1d3f5971 *R/print.nestedtemp.R
-32edebf47fbb4b3a0afd92ab56bd5de5 *R/print.oecosimu.R
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faf2620b1fbaec410af7b6e3159510ce *R/print.permat.R
575da3562c07c6324e84288ac603b011 *R/print.permutest.betadisper.R
f0c12622e4f250aacca5b7fabe54cbd1 *R/print.permutest.cca.R
@@ -323,7 +321,6 @@ c80f3931f05ab3066dfe93b98e737856 *R/print.varpart234.R
8917f5ef5398c984e0e2675c83e74c5c *R/print.wcmdscale.R
083d526f54611d40ce749ffe95f169ae *R/procrustes.R
819af0297e5d0a907f7fa91319c67e96 *R/profile.MOStest.R
-8159854c33821cea4cb77e34d882d79e *R/profile.fisherfit.R
2f6b69115ea549102dad9b1b22c88034 *R/profile.humpfit.R
4e28e2b84d11d8f8b0ad6755bcbe2ef1 *R/protest.R
9169bd797963b5b121684de528651170 *R/rad.lognormal.R
@@ -344,7 +341,7 @@ f9008aa5cf3109a3607aca9ac6bfe8d7 *R/rda.default.R
90b562e8a94febce8430a344392a2943 *R/rda.formula.R
eefe337541bf9dce01852dceeac12e1c *R/read.cep.R
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-80b6ecc82b39e2feeeb9059764a9a2c3 *R/renyiaccum.R
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90a897e14094cc1eba66c5f59a5bb79c *R/residuals.cca.R
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4ee8534c438c824f1cf4ea62337e259d *R/rgl.isomap.R
@@ -374,7 +371,7 @@ b35ee7d9cdc86eecefb5dcf478fc8abf *R/simpleRDA2.R
73367e17a66ffeca6410771f0ca8d1ef *R/simulate.rda.R
9f235c650efc4217a3cc88996b627e1d *R/spandepth.R
3bb1adac8b593f81ebf4c2146ee112b9 *R/spantree.R
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3c94a17c2602903234a65cb244053130 *R/specnumber.R
6e382a42402a7bc206b6eb6b6c278d77 *R/specpool.R
77cc19684e9ceb27500ca7f802923328 *R/specpool2vect.R
@@ -413,12 +410,12 @@ dcfdf0eb68a8acfa6a8b0cfb6fcac0f5 *R/text.cca.R
350a6ba06c34f2efc74c6aa503f8a7ab *R/treedive.R
cf0f2cbf17bbd944d455f71918ab88eb *R/treeheight.R
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722ab25ac95b6c419b29a94347916f23 *R/veganCovEllipse.R
@@ -447,20 +444,20 @@ c51905bd025ccea2737527b6fca4a081 *data/mite.pcnm.rda
ee3c343418d7cf2e435028adf93205f1 *data/sipoo.rda
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33db614085aa448f4241cd79ddc62461 *man/CCorA.Rd
@@ -475,7 +472,7 @@ a2fa01618dd236031de91527f7902ce9 *man/adonis.Rd
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@@ -493,13 +490,13 @@ bda32a146ba37c0193a850b9358e4ef8 *man/density.adonis.Rd
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+5857c2307b1dfd69953a88bd3c384180 *man/fisherfit.Rd
841b3f32510ed2c3f64186d623f858ae *man/goodness.cca.Rd
4d5e44b51132481ab920292b2651041c *man/goodness.metaMDS.Rd
81f199c3ba2c65a7b6f81cbb7cc9886d *man/humpfit.Rd
@@ -518,7 +515,7 @@ c50bd45c9e8c6e892d2dd8f7fe5f0bd9 *man/model.matrix.cca.Rd
382e99ee5b67d89f2f9ad440236929f5 *man/mso.Rd
838a98b67e2015061513c32731732608 *man/multipart.Rd
646fcb9015f0f3dc520ab2be0db5c042 *man/nestedtemp.Rd
-0746338956acbf265148314e40c37587 *man/nobs.adonis.Rd
+c7f768b6f36aec4bc9d5b4c8f72c1141 *man/nobs.adonis.Rd
90239dffda8fb82e8e8f3e6b46b0be7a *man/oecosimu.Rd
3e6f6e4c473e4ea91c18d34bf487ff0c *man/ordiarrows.Rd
03aab4cb7ca71141281d2abd3e810231 *man/ordihull.Rd
@@ -535,7 +532,7 @@ d971701b3c6f89b3a6b358a3966a43d2 *man/ordixyplot.Rd
adc9628edf2079867649bbaa68daee53 *man/pcnm.Rd
864ed25d069da12a2226310240f1f740 *man/permatfull.Rd
807092c467db330149046d1dc9e9ab91 *man/permutations.Rd
-fb24c58ca61caf767ced5bd79dbff57e *man/permutest.betadisper.Rd
+0ca5118e13c43995271c3a9175145ab5 *man/permutest.betadisper.Rd
47898b675bb6d36fce6961e6a70d8d57 *man/plot.cca.Rd
7b4d950fcf9d3f4591a217ae9b5ccf7e *man/prc.Rd
37cad2f61855e0cc430943ac98885069 *man/predict.cca.Rd
@@ -545,20 +542,20 @@ f61f64cc1be643149fd02f08a0cd7f9f *man/radfit.Rd
3e70bfa0a8ae5d4c3c60dba77500b584 *man/rankindex.Rd
64342c9ea7e7b2607d433c3346f9726a *man/raupcrick.Rd
2867f5f71a47da498cbadf9aaa01b2b6 *man/read.cep.Rd
-c1ffd9ca78ad968e1da11fdba007cbe8 *man/renyi.Rd
+dc7d7857e7a01ea099fc97c8a3a13239 *man/renyi.Rd
5c25a88ca55fabce5783509c706faad5 *man/scores.Rd
-9732a76d9f971df9db16b97d5746615e *man/screeplot.cca.Rd
-947c357c856ef350340eb54673c0bc5c *man/simper.Rd
+8104fd642b527f76e159580e3d317fcf *man/screeplot.cca.Rd
+814fe1cad3b64291fd13772a6078ea9d *man/simper.Rd
45cd418b2264b4eb6abc89cc11a7877f *man/simulate.rda.Rd
b34910fa6ed6c9bfbd90a7f7443a135f *man/sipoo.Rd
d7dd63e022633049766cffdaf6cac723 *man/spantree.Rd
-ef48716a5ed02feeb8da58c293596362 *man/specaccum.Rd
+2e0ddc50d04a9b8dae57ee475b3edc5c *man/specaccum.Rd
53818a4edb1d52d425065bea76963021 *man/specpool.Rd
5b9e51c85395f80f8504954e4175f877 *man/stepacross.Rd
0aac5f5c8f58fc8fe1cb6c0ba819b196 *man/taxondive.Rd
85f77fcf89b48586502c00baef8e5561 *man/tolerance.Rd
bfe306a0cb659930e17e46d191f7629f *man/treedive.Rd
-5a1c08a3f35d82027259b3000e94cd2e *man/tsallis.Rd
+fd154a9d281c586683c87fdf0d44ccad *man/tsallis.Rd
033dd7d7917185cea81e4d7afcd59df9 *man/varechem.Rd
e7717c542e5c0372ca2ff71bcc26d8b0 *man/varpart.Rd
699122da39bdbbfbfeb6a1f8f078242c *man/vegan-defunct.Rd
@@ -580,15 +577,15 @@ e19f79f4b3fef915a3ece2db284475f6 *src/monoMDS.f
31bdbe9b08340e1662a62cf6e61ade6a *src/pnpoly.c
b9b647fcf8a3e59e10b9351fae60ec06 *src/stepacross.c
87233fad519f344865adfc74c92c2a1a *src/vegdist.c
-e864afa3351b069bc8898598f7963a14 *vignettes/FAQ-vegan.pdf
+4ffa0736ba10dddfed202a2a0ef51983 *vignettes/FAQ-vegan.pdf
7d9cb640d68ea4c935a7c3e1590c5532 *vignettes/FAQ-vegan.texi
45ce50de9edf3aeacd8d11d1483f764c *vignettes/Makefile
-e4063a155721a54563fbac418581ea77 *vignettes/NEWS.html
+e98d4ad5d4d34bfbdf934da7deff70aa *vignettes/NEWS.html
fce7a85b3e7f348fb12812758dc45d5c *vignettes/decision-vegan.Rnw
-ff9e75f6d8456d17109cb2f5ea2c8496 *vignettes/decision-vegan.tex
-d0e8beedbfe2f77f8a6934e4a459674d *vignettes/diversity-vegan.Rnw
-7e73ba0ba0eb2e798da23421c0b61b2f *vignettes/diversity-vegan.tex
+73a0586b73a2d0634a05b768ca8492d1 *vignettes/decision-vegan.tex
+658b2d71438cb7f1a7315b7a893b993c *vignettes/diversity-vegan.Rnw
+429f9669fe0c242b122a61de0303e714 *vignettes/diversity-vegan.tex
66c024cfa42524d1649f7033286c52b0 *vignettes/intro-vegan.Rnw
-45c079193646c54dec64b19556e623fc *vignettes/intro-vegan.tex
+ee3f2c8366052dea6b145c30f304430e *vignettes/intro-vegan.tex
0c229cd8dbde571130ff2f4b516414e5 *vignettes/vegan.bib
fd58fa43e5e36d0ddcddd26dac1c7e31 *vignettes/vegan.sty
diff --git a/NAMESPACE b/NAMESPACE
index 78ceafd..f786902 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -131,7 +131,6 @@ S3method(coef, rda)
# confint: stats -- also uses MASS:::confint.glm & MASS:::profile.glm
# does this work with namespaces??
S3method(confint, MOStest)
-S3method(confint, fisherfit)
# cophenetic: stats
S3method(cophenetic, spantree)
# density: stats
@@ -264,7 +263,6 @@ S3method(plot, prc)
S3method(plot, preston)
S3method(plot, prestonfit)
S3method(plot, procrustes)
-S3method(plot, profile.fisherfit)
S3method(plot, rad)
S3method(plot, radfit)
S3method(plot, radfit.frame)
@@ -360,7 +358,6 @@ S3method(print, wcmdscale)
# profile: stats
# see note on 'confint'
S3method(profile, MOStest)
-S3method(profile, fisherfit)
S3method(profile, humpfit)
# radfit: vegan
S3method(radfit, data.frame)
diff --git a/R/boxplot.specaccum.R b/R/boxplot.specaccum.R
index e93e6a8..25dc68b 100644
--- a/R/boxplot.specaccum.R
+++ b/R/boxplot.specaccum.R
@@ -1,11 +1,11 @@
-"boxplot.specaccum" <-
+`boxplot.specaccum` <-
function(x, add=FALSE, ...)
{
if (x$method != "random")
stop("boxplot available only for method=\"random\"")
if (!add) {
plot(x$sites, x$richness, type="n", xlab="Sites", ylab="Species",
- ylim=c(1, max(x$richness)), ...)
+ ylim=c(1, max(x$richness, na.rm = TRUE)), ...)
}
tmp <- boxplot(data.frame(t(x$perm)), add=TRUE, at=x$sites, axes=FALSE, ...)
invisible(tmp)
diff --git a/R/confint.fisherfit.R b/R/confint.fisherfit.R
deleted file mode 100644
index b9dc570..0000000
--- a/R/confint.fisherfit.R
+++ /dev/null
@@ -1,6 +0,0 @@
-"confint.fisherfit" <-
- function (object, parm, level=0.95, ...)
-{
- if (!require(MASS)) stop("Needs packages MASS .. not found")
- confint(profile(object), level=level, ...)
-}
diff --git a/R/decostand.R b/R/decostand.R
index 5c0cc2c..2272aee 100644
--- a/R/decostand.R
+++ b/R/decostand.R
@@ -1,4 +1,4 @@
-"decostand" <-
+`decostand` <-
function (x, method, MARGIN, range.global, logbase = 2, na.rm = FALSE, ...)
{
wasDataFrame <- is.data.frame(x)
@@ -34,7 +34,7 @@
if (missing(MARGIN))
MARGIN <- 1
tmp <- apply(x^2, MARGIN, sum, na.rm = na.rm)
- tmp <- pmax(k, sqrt(tmp))
+ tmp <- pmax(.Machine$double.eps, sqrt(tmp))
x <- sweep(x, MARGIN, tmp, "/")
}, range = {
if (missing(MARGIN))
diff --git a/R/envfit.default.R b/R/envfit.default.R
index ea3d5bf..cae6342 100644
--- a/R/envfit.default.R
+++ b/R/envfit.default.R
@@ -1,4 +1,4 @@
-"envfit.default" <-
+`envfit.default` <-
function (ord, env, permutations = 999, strata, choices = c(1, 2),
display = "sites", w = weights(ord), na.rm = FALSE, ...)
{
@@ -17,10 +17,10 @@
na.action <- structure(seq_along(keep)[!keep], class="omit")
}
if (is.data.frame(env)) {
- facts <- unlist(lapply(env, is.factor))
- if (sum(facts)) {
- Pfac <- env[, facts, drop = FALSE]
- P <- env[, !facts, drop = FALSE]
+ vects <- sapply(env, is.numeric)
+ if (any(!vects)) { # have factors
+ Pfac <- env[, !vects, drop = FALSE]
+ P <- env[, vects, drop = FALSE]
if (length(P)) {
if (permutations) {
if (!exists(".Random.seed", envir = .GlobalEnv,
diff --git a/R/factorfit.R b/R/factorfit.R
index 22733a3..cca6a76 100644
--- a/R/factorfit.R
+++ b/R/factorfit.R
@@ -2,9 +2,13 @@
function (X, P, permutations = 0, strata, w, ...)
{
P <- as.data.frame(P)
+ ## Check that all variables are factors, and coerce if necessary
+ if(any(!sapply(P, is.factor)))
+ P <- data.frame(lapply(P, function(x)
+ if (is.factor(x)) x else factor(x)))
P <- droplevels(P) ## make sure only the used levels are present
if (any(!sapply(P, is.factor)))
- stop("All fitted variables must be factors")
+ stop("All non-numeric variables must be factors")
NR <- nrow(X)
NC <- ncol(X)
NF <- ncol(P)
@@ -41,7 +45,7 @@
var = double(1), PACKAGE = "vegan")$var
tmp[i] <- 1 - invar/totvar
}
- pval.this <- (sum(tmp > r.this) + 1)/(permutations + 1)
+ pval.this <- (sum(tmp >= r.this) + 1)/(permutations + 1)
pval <- c(pval, pval.this)
}
}
diff --git a/R/fisher.alpha.R b/R/fisher.alpha.R
index 137ee46..de4eeb5 100644
--- a/R/fisher.alpha.R
+++ b/R/fisher.alpha.R
@@ -1,12 +1,12 @@
-"fisher.alpha" <-
- function (x, MARGIN = 1, se = FALSE, ...)
+`fisher.alpha` <-
+ function (x, MARGIN = 1, ...)
{
x <- as.matrix(x)
if(ncol(x) == 1)
x <- t(x)
sol <- apply(x, MARGIN, fisherfit)
out <- unlist(lapply(sol, function(x) x$estimate))
- if (se) {
+ if (FALSE) {
out <- list(alpha = out)
out$se <- unlist(lapply(sol, function(x) sqrt(diag(solve(x$hessian)))[1]))
out$df.residual <- unlist(lapply(sol, df.residual))
diff --git a/R/fisherfit.R b/R/fisherfit.R
index 105503a..7b2cbe2 100644
--- a/R/fisherfit.R
+++ b/R/fisherfit.R
@@ -1,24 +1,49 @@
-"fisherfit" <-
- function (x, ...)
+## Fisher alpha is actually based only on the number of species S and
+## number of individuals.
+
+`fisherfit` <-
+ function(x, ...)
{
- Dev.logseries <- function(n.r, p, N) {
- r <- as.numeric(names(n.r))
- x <- N/(N + p)
- logmu <- log(p) + log(x) * r - log(r)
- lhood <- -sum(n.r * (logmu - log(n.r)) + 1) - p * log(1 -
- x)
- lhood
- }
- tmp <- as.rad(x)
+ nr <- as.fisher(x)
+ S <- sum(nr)
N <- sum(x)
- tmp <- tmp/N
- p <- 1/sum(tmp^2)
- n.r <- as.fisher(x)
- LSeries <- nlm(Dev.logseries, n.r = n.r, p = p, N = N,
- hessian = TRUE, ...)
- LSeries$df.residual <- sum(x > 0) - 1
- LSeries$nuisance <- N/(N + LSeries$estimate)
- LSeries$fisher <- n.r
- class(LSeries) <- "fisherfit"
- LSeries
+ ## Solve 'x' (Fisher alpha).
+ d1fun <- function(x, S, N) x * log(1 + N/x) - S
+ ## We may need to bracket the interval
+ hi <- 50
+ lo <- 1
+ tries <- 0
+ repeat {
+ sol <- try(uniroot(d1fun, c(lo, hi), S = S, N = N, ...), silent = TRUE)
+ if (inherits(sol, "try-error")) {
+ if(d1fun(hi, S, N) < 0)
+ hi <- 2*hi
+ if(d1fun(lo, S, N) > 0)
+ lo <- lo/2
+ tries <- tries + 1
+ }
+ else break
+ ## alpha can tend to +Inf: set root = NA etc.
+ if (tries > 200) {
+ sol <- list(root = NA, f.root = NA, iter = NA, init.it = NA,
+ estim.prec = NA)
+ break
+ }
+ }
+ ## 'extendInt' arg was added in R r63162 | maechler | 2013-07-03
+ ## 11:47:22 +0300 (Wed, 03 Jul 2013). Latest release is R 3.0.2 of
+ ## 2013-09-25, but it still does not have the argument. In the
+ ## future we may switch to the following:
+
+ ##sol <- uniroot(d1fun, c(1,50), extendInt = "yes", S = S, N = N, ...)
+
+ nuisance <- N/(N + sol$root)
+ ## we used nlm() earlier, and the following output is compatible
+ out <- list(estimate = sol$root, hessian = NA,
+ iterations = sol$iter, df.residual = NA,
+ nuisance = nuisance, fisher = nr,
+ estim.prec = sol$estim.prec,
+ code = 2*is.na(sol$estim.prec) + 1)
+ class(out) <- "fisherfit"
+ out
}
diff --git a/R/nestednodf.R b/R/nestednodf.R
index 6dad9a1..574f58e 100644
--- a/R/nestednodf.R
+++ b/R/nestednodf.R
@@ -22,7 +22,7 @@
}
nr <- NROW(comm)
nc <- NCOL(comm)
- fill <- sum(rfill)/length(comm)
+ fill <- sum(rfill)/prod(dim(comm))
N.paired.rows <- numeric(nr * (nr - 1)/2)
N.paired.cols <- numeric(nc * (nc - 1)/2)
counter <- 0
@@ -35,7 +35,7 @@
if (weighted) {
second <- comm[j, ]
N.paired.rows[counter] <-
- sum(first - second > 0 & second > 0)/sum(second > 0)
+ sum(first - second >= 0 & second > 0)/sum(second > 0)
}
else {
N.paired.rows[counter] <-
@@ -53,7 +53,7 @@
if (weighted) {
second <- comm[, j]
N.paired.cols[counter] <-
- sum(first - second > 0 & second > 0)/sum(second > 0)
+ sum(first - second >= 0 & second > 0)/sum(second > 0)
}
else {
N.paired.cols[counter] <-
diff --git a/R/ordiArrowMul.R b/R/ordiArrowMul.R
index 782826b..af9abcb 100644
--- a/R/ordiArrowMul.R
+++ b/R/ordiArrowMul.R
@@ -5,7 +5,7 @@
{
u <- par("usr")
u <- u - rep(at, each = 2)
- r <- c(range(x[,1]), range(x[,2]))
+ r <- c(range(x[,1], na.rm = TRUE), range(x[,2], na.rm = TRUE))
## 'rev' takes care of reversed axes like xlim(1,-1)
rev <- sign(diff(u))[-2]
if (rev[1] < 0)
diff --git a/R/permutest.betadisper.R b/R/permutest.betadisper.R
index cd3a1ff..6f668b9 100644
--- a/R/permutest.betadisper.R
+++ b/R/permutest.betadisper.R
@@ -1,5 +1,5 @@
`permutest.betadisper` <- function(x, pairwise = FALSE,
- control = permControl(nperm = 999), ...)
+ control = how(nperm = 999), ...)
{
t.statistic <- function(x, y) {
m <- length(x)
@@ -11,6 +11,7 @@
pooled <- sqrt(((m-1)*xvar + (n-1)*yvar) / (m+n-2))
(xbar - ybar) / (pooled * sqrt(1/m + 1/n))
}
+
if(!inherits(x, "betadisper"))
stop("Only for class \"betadisper\"")
## will issue error if only a single group
@@ -20,42 +21,61 @@
mod.Q <- mod$qr
p <- mod.Q$rank
resids <- qr.resid(mod.Q, x$distances)
- res <- numeric(length = control$nperm + 1)
+
+ ## extract groups
+ group <- x$group
+
+ ## get set of permutations - shuffleSet checks design
+ perms <- shuffleSet(length(group), control = control)
+
+ ## number of permutations being performed, possibly adjusted after
+ ## checking in shuffleSet
+ nperm <- nrow(perms)
+
+ ## set-up objects to hold permuted results
+ res <- numeric(length = nperm + 1)
res[1] <- summary(mod)$fstatistic[1]
+
## pairwise comparisons
if(pairwise) {
## unique pairings
combin <- combn(levels(x$group), 2)
n.pairs <- ncol(combin)
- t.stats <- matrix(0, ncol = n.pairs, nrow = control$nperm + 1)
- t.stats[1,] <- apply(combn(levels(x$group), 2), 2, function(z) {
- t.statistic(x$distances[x$group == z[1]],
- x$distances[x$group == z[2]])})
+ t.stats <- matrix(0, ncol = n.pairs, nrow = nperm + 1)
+ t.stats[1,] <- apply(combn(levels(group), 2), 2, function(z) {
+ t.statistic(x$distances[group == z[1]],
+ x$distances[group == z[2]])})
}
- for(i in seq(along = res[-1])) {
- perm <- shuffle(nobs, control = control)
- perm.resid <- resids[perm]
- f <- qr.fitted(mod.Q, perm.resid)
+
+ ## begin loop over shuffleSet perms
+ for(i in seq_len(nperm)) {
+ perm <- perms[i,] ## take current permutation from set
+ perm.resid <- resids[perm] ## permute residuals
+ f <- qr.fitted(mod.Q, perm.resid) ## create new data
mss <- sum((f - mean(f))^2)
r <- qr.resid(mod.Q, perm.resid)
rss <- sum(r^2)
rdf <- nobs - p
resvar <- rss / rdf
res[i+1] <- (mss / (p - 1)) / resvar
+
## pairwise comparisons
if(pairwise) {
for(j in seq_len(n.pairs)) {
- grp1 <- x$distance[perm][x$group == combin[1, j]]
- grp2 <- x$distance[perm][x$group == combin[2, j]]
+ grp1 <- x$distance[perm][group == combin[1, j]]
+ grp2 <- x$distance[perm][group == combin[2, j]]
t.stats[i+1, j] <- t.statistic(grp1, grp2)
}
}
}
+
+ ## compute permutation p-value
pval <- sum(res >= res[1]) / length(res)
+
if(pairwise) {
df <- apply(combin, 2, function(z) {
- length(x$distances[x$group == z[1]]) +
- length(x$distance[x$group == z[2]]) - 2})
+ length(x$distances[group == z[1]]) +
+ length(x$distance[group == z[2]]) - 2})
pairwise <- list(observed = 2 * pt(-abs(t.stats[1,]), df),
permuted = apply(t.stats, 2,
function(z) sum(abs(z) >= abs(z[1]))/length(z)))
@@ -64,12 +84,13 @@
} else {
pairwise <- NULL
}
- retval <- cbind(mod.aov[, 1:4], c(control$nperm, NA), c(pval, NA))
+
+ retval <- cbind(mod.aov[, 1:4], c(nperm, NA), c(pval, NA))
dimnames(retval) <- list(c("Groups", "Residuals"),
c("Df", "Sum Sq", "Mean Sq", "F", "N.Perm",
"Pr(>F)"))
retval <- list(tab = retval, pairwise = pairwise,
- groups = levels(x$group), control = control)
+ groups = levels(group), control = control)
class(retval) <- "permutest.betadisper"
retval
}
diff --git a/R/plot.profile.fisherfit.R b/R/plot.profile.fisherfit.R
deleted file mode 100644
index e5bbc8b..0000000
--- a/R/plot.profile.fisherfit.R
+++ /dev/null
@@ -1,16 +0,0 @@
-`plot.profile.fisherfit` <-
- function (x, type = "l", ...)
-{
- tmp <- attr(x, "original.fit")
- est <- tmp$coefficients
- se <- tmp$std.err
- alpha <- x$alpha[, 1]
- tau <- x$alpha[, 2]
- sp <- spline(tau, alpha)
- plot(sp$x, sp$y, type = type, xlab = "alpha", ylab = "tau",
- ...)
- abline(-est/se, 1/se, lty = 2)
- abline(v = est, lty = 3)
- abline(h = 0, lty = 3)
- invisible()
-}
diff --git a/R/plot.renyiaccum.R b/R/plot.renyiaccum.R
index ae5744c..9b414cb 100644
--- a/R/plot.renyiaccum.R
+++ b/R/plot.renyiaccum.R
@@ -1,12 +1,15 @@
`plot.renyiaccum` <-
-function (x, what=c("mean", "Qnt 0.025", "Qnt 0.975"), type = "l", ...)
+function (x, what=c("Collector", "mean", "Qnt 0.025", "Qnt 0.975"),
+ type = "l", ...)
{
- if (any(what %in% colnames(x[,1,])))
- x <- x[,,what]
+ what <- what[what %in% dimnames(x)[[3]]]
+ if (any(what %in% dimnames(x)[[3]]))
+ x <- x[,,what, drop = FALSE]
dm <- dim(x)
- lin <- rep(colnames(x[,1,]), each=dm[1]*dm[2])
+ dnam <- dimnames(x)
+ lin <- rep(dnam[[3]], each=dm[1]*dm[2])
Sites <- rep(1:dm[1], len=prod(dm))
- alp <- factor(rownames(x[1,,]), levels=rownames(x[1,,]))
+ alp <- factor(dnam[[2]], levels=dnam[[2]])
alpha <- rep(rep(alp, each=dm[1]), len=prod(dm))
Diversity <- as.vector(x)
xyplot(Diversity ~ Sites | alpha, groups=lin, type=type, ...)
diff --git a/R/plot.specaccum.R b/R/plot.specaccum.R
index 6e7bbb0..5c19246 100644
--- a/R/plot.specaccum.R
+++ b/R/plot.specaccum.R
@@ -1,17 +1,32 @@
`plot.specaccum` <-
- function(x, add = FALSE, ci = 2, ci.type = c("bar","line","polygon"),
- col = par("fg"), ci.col = col, ci.lty = 1, xlab,
- ylab = x$method, ylim, xvar = c("sites", "individuals"), ...)
+ function(x, add = FALSE, random = FALSE, ci = 2,
+ ci.type = c("bar","line","polygon"), col = par("fg"), ci.col = col,
+ ci.lty = 1, xlab, ylab = x$method, ylim,
+ xvar = c("sites", "individuals", "effort"), ...)
{
+ if(random && x$method != "random")
+ stop("random = TRUE can be used only with method='random'")
xvar <- match.arg(xvar)
+ ## adjust weights to number of sites
+ if (random && !is.null(x$weights) && xvar == "sites") {
+ n <- length(x$effort)
+ adj <- n/x$effort[n]
+ } else {
+ adj <- 1
+ }
xaxvar <- x[[xvar]]
if (missing(xlab))
xlab <- paste(toupper(substring(xvar, 1, 1)),
substring(xvar, 2), sep="")
+ if (random)
+ ci <- FALSE
ci.type <- match.arg(ci.type)
if (!add) {
if (missing(ylim))
- ylim <- c(1, max(x$richness, x$richness + ci*x$sd))
+ if (random)
+ ylim <- c(1, max(x$perm, na.rm = TRUE))
+ else
+ ylim <- c(1, max(x$richness, x$richness + ci*x$sd, na.rm = TRUE))
plot(xaxvar, x$richness, xlab=xlab, ylab=ylab, ylim=ylim,
type="n", ...)
}
@@ -25,6 +40,21 @@
c(x$richness - ci*x$sd, rev(x$richness + ci*x$sd)), col=ci.col,
lty=ci.lty, ...)
)
- lines(xaxvar, x$richness,col=col, ...)
+ if (random) {
+ if (is.null(x$weights)) {
+ for(i in seq_len(NCOL(x$perm)))
+ lines(xaxvar, x$perm[,i], col=col, ...)
+ } else {
+ for(i in seq_len(NCOL(x$perm)))
+ lines(x$weights[,i]*adj, x$perm[,i], col=col, ...)
+ }
+ } else
+ lines(xaxvar, x$richness,col=col, ...)
invisible()
}
+
+`lines.specaccum` <-
+ function(x, ...)
+{
+ plot(x, add = TRUE, ...)
+}
diff --git a/R/print.cca.R b/R/print.cca.R
index 3a3f667..bcfd764 100644
--- a/R/print.cca.R
+++ b/R/print.cca.R
@@ -27,7 +27,7 @@
## Remove "Proportion" if only one component
if (is.null(x$CCA) && is.null(x$pCCA))
tbl <- tbl[,-2]
- printCoefmat(tbl, digits = digits, na.print = "")
+ printCoefmat(tbl, digits = digits, na.print = "", zap.ind = 1:2)
cat("Inertia is", x$inertia, "\n")
if (!is.null(x$CCA$alias))
cat("Some constraints were aliased because they were collinear (redundant)\n")
diff --git a/R/print.fisherfit.R b/R/print.fisherfit.R
index a7368cf..d1c4618 100644
--- a/R/print.fisherfit.R
+++ b/R/print.fisherfit.R
@@ -1,12 +1,8 @@
-"print.fisherfit" <-
+`print.fisherfit` <-
function (x, ...)
{
cat("\nFisher log series model\n")
- cat("No. of species:", sum(x$fisher), "\n\n")
- out <- cbind(x$estimate, sqrt(diag(solve(x$hessian))))
- colnames(out) <- c("Estimate", "Std. Error")
- rownames(out) <- "alpha"
- printCoefmat(out)
- cat("\n")
+ cat("No. of species:", sum(x$fisher), "\n")
+ cat("Fisher alpha: ", x$estimate, "\n\n")
invisible(x)
}
diff --git a/R/print.oecosimu.R b/R/print.oecosimu.R
index e0ff79a..23d413c 100644
--- a/R/print.oecosimu.R
+++ b/R/print.oecosimu.R
@@ -28,8 +28,8 @@
}
probs <- switch(x$oecosimu$alternative,
two.sided = c(0.025, 0.5, 0.975),
- less = c(0, 0.5, 0.95),
- greater = c(0.05, 0.5, 1))
+ greater = c(0, 0.5, 0.95),
+ less = c(0.05, 0.5, 1))
qu <- apply(x$oecosimu$simulated, 1, quantile, probs=probs, na.rm = TRUE)
m <- cbind("statistic" = x$oecosimu$statistic,
"z" = x$oecosimu$z, "mean" = x$oecosimu$means, t(qu),
diff --git a/R/profile.fisherfit.R b/R/profile.fisherfit.R
deleted file mode 100644
index 035eb5a..0000000
--- a/R/profile.fisherfit.R
+++ /dev/null
@@ -1,43 +0,0 @@
-"profile.fisherfit" <-
- function (fitted, alpha = 0.01, maxsteps = 20, del = zmax/5, ...)
-{
- Dev.logseries <- function(n.r, p, N) {
- r <- as.numeric(names(n.r))
- x <- N/(N + p)
- logmu <- log(p) + log(x) * r - log(r)
- lhood <- -sum(n.r * (logmu - log(n.r)) + 1) - p * log(1 -
- x)
- lhood
- }
- par <- fitted$estimate
- names(par) <- "alpha"
- std.err <- sqrt(diag(solve(fitted$hessian)))
- minll <- fitted$minimum
- nr <- fitted$fisher
- N <- sum(as.numeric(names(nr)) * nr)
- zmax <- sqrt(qchisq(1 - alpha/2, 1))
- zi <- 0
- bi <- par
- for (sgn in c(-1, 1)) {
- step <- 0
- z <- 0
- b <- 0
- while ((step <- step + 1) < maxsteps && abs(z) < zmax) {
- b <- par + sgn * step * del * std.err
- fm <- Dev.logseries(nr, b, N)
- zz <- 2 * (fm - minll)
- if (zz > -0.001)
- zz <- max(zz, 0)
- else stop("profiling has found a better solution, so original fit had not converged")
- z <- sgn * sqrt(zz)
- bi <- c(bi, b)
- zi <- c(zi, z)
- }
- }
- si <- order(bi)
- out <- list()
- out$alpha <- data.frame(tau = zi[si], par.vals = bi[si])
- attr(out, "original.fit") <- list(coefficients = par, std.err = std.err)
- class(out) <- c("profile.fisherfit", "profile.glm", "profile")
- out
-}
diff --git a/R/renyiaccum.R b/R/renyiaccum.R
index 75e0b8c..add178d 100644
--- a/R/renyiaccum.R
+++ b/R/renyiaccum.R
@@ -1,6 +1,9 @@
`renyiaccum` <-
-function(x, scales=c(0, 0.5, 1, 2, 4, Inf), permutations = 100, raw = FALSE, ...)
-{
+function(x, scales=c(0, 0.5, 1, 2, 4, Inf), permutations = 100,
+ raw = FALSE, collector = FALSE, subset, ...)
+{
+ if (!missing(subset))
+ x <- subset(x, subset)
x <- as.matrix(x)
n <- nrow(x)
p <- ncol(x)
@@ -17,12 +20,16 @@ function(x, scales=c(0, 0.5, 1, 2, 4, Inf), permutations = 100, raw = FALSE, ...
result[,,k] <- as.matrix(renyi((apply(x[sample(n),],2,cumsum)),
scales=scales, ...))
}
+ if (raw)
+ collector <- FALSE
+ if (collector)
+ ref <- as.matrix(renyi(apply(x, 2, cumsum), scales = scales, ...))
if (raw) {
if (m==1) {
result <- result[,1,]
}
}else{
- tmp <- array(dim=c(n,m,6))
+ tmp <- array(dim=c(n,m,6 + as.numeric(collector)))
for (i in 1:n) {
for (j in 1:m) {
tmp[i,j,1] <- mean(result[i,j,1:permutations])
@@ -31,12 +38,14 @@ function(x, scales=c(0, 0.5, 1, 2, 4, Inf), permutations = 100, raw = FALSE, ...
tmp[i,j,4] <- max(result[i,j,1:permutations])
tmp[i,j,5] <- quantile(result[i,j,1:permutations],0.025)
tmp[i,j,6] <- quantile(result[i,j,1:permutations],0.975)
+ if (collector)
+ tmp[i,j,7] <- ref[i,j]
}
}
result <- tmp
dimnames(result) <- list(pooled.sites=c(1:n),
scale=scales,
- c("mean", "stdev", "min", "max", "Qnt 0.025", "Qnt 0.975"))
+ c("mean", "stdev", "min", "max", "Qnt 0.025", "Qnt 0.975", if (collector) "Collector"))
}
class(result) <- c("renyiaccum", class(result))
result
diff --git a/R/specaccum.R b/R/specaccum.R
index 0747dea..a80ad70 100644
--- a/R/specaccum.R
+++ b/R/specaccum.R
@@ -1,7 +1,15 @@
`specaccum` <-
function (comm, method = "exact", permutations = 100, conditioned=TRUE,
- gamma="jack1", ...)
+ gamma="jack1", w = NULL, subset, ...)
{
+ METHODS <- c("collector", "random", "exact", "rarefaction", "coleman")
+ method <- match.arg(method, METHODS)
+ if (!is.null(w) && !(method %in% c("random", "collector")))
+ stop(gettextf("weights 'w' can be only used with methods 'random' and 'collector'"))
+ if (!missing(subset)) {
+ comm <- subset(comm, subset)
+ w <- subset(w, subset)
+ }
x <- comm
x <- as.matrix(x)
x <- x[, colSums(x) > 0, drop=FALSE]
@@ -15,22 +23,34 @@
accumulator <- function(x, ind) {
rowSums(apply(x[ind, ], 2, cumsum) > 0)
}
- METHODS <- c("collector", "random", "exact", "rarefaction", "coleman")
- method <- match.arg(method, METHODS)
specaccum <- sdaccum <- sites <- perm <- NULL
if (n == 1 && method != "rarefaction")
message("No actual accumulation since only 1 site provided")
switch(method, collector = {
sites <- 1:n
+ xout <- weights <- cumsum(w)
specaccum <- accumulator(x, sites)
}, random = {
perm <- array(dim = c(n, permutations))
+ if (!is.null(w))
+ weights <- array(dim = c(n, permutations))
for (i in 1:permutations) {
- perm[, i] <- accumulator(x, sample(n))
+ perm[, i] <- accumulator(x, ord <- sample(n))
+ if(!is.null(w))
+ weights[,i] <- cumsum(w[ord])
}
sites <- 1:n
- specaccum <- apply(perm, 1, mean)
- sdaccum <- apply(perm, 1, sd)
+ if (is.null(w)) {
+ specaccum <- apply(perm, 1, mean)
+ sdaccum <- apply(perm, 1, sd)
+ } else {
+ sumw <- sum(w)
+ xout <- seq(sumw/n, sumw, length.out = n)
+ intx <- sapply(seq_len(n), function(i)
+ approx(weights[,i], perm[,i], xout = xout)$y)
+ specaccum <- apply(intx, 1, mean)
+ sdaccum <- apply(intx, 1, sd)
+ }
}, exact = {
freq <- colSums(x > 0)
freq <- freq[freq > 0]
@@ -87,6 +107,10 @@
})
out <- list(call = match.call(), method = method, sites = sites,
richness = specaccum, sd = sdaccum, perm = perm)
+ if (!is.null(w)) {
+ out$weights <- weights
+ out$effort <- xout
+ }
if (method == "rarefaction")
out$individuals <- ind
class(out) <- "specaccum"
diff --git a/R/tsallisaccum.R b/R/tsallisaccum.R
index 571e637..44af3eb 100644
--- a/R/tsallisaccum.R
+++ b/R/tsallisaccum.R
@@ -1,6 +1,9 @@
tsallisaccum <-
-function (x, scales = seq(0, 2, 0.2), permutations = 100, raw = FALSE, ...)
+function (x, scales = seq(0, 2, 0.2), permutations = 100, raw = FALSE,
+ subset, ...)
{
+ if (!missing(subset))
+ x <- subset(x, subset)
x <- as.matrix(x)
n <- nrow(x)
p <- ncol(x)
diff --git a/R/vectorfit.R b/R/vectorfit.R
index cb16623..f54d66e 100644
--- a/R/vectorfit.R
+++ b/R/vectorfit.R
@@ -18,6 +18,7 @@
H <- qr.fitted(Q, Pw)
heads <- qr.coef(Q, Pw)
r <- diag(cor(H, Pw)^2)
+ r[is.na(r)] <- 0
heads <- decostand(heads, "norm", 2)
heads <- t(heads)
if (is.null(colnames(X)))
@@ -36,7 +37,7 @@
Hperm <- qr.fitted(Q, take)
permstore[i, ] <- diag(cor(Hperm, take))^2
}
- permstore <- sweep(permstore, 2, r, ">")
+ permstore <- sweep(permstore, 2, r, ">=")
pvals <- (apply(permstore, 2, sum) + 1)/(permutations + 1)
}
else pvals <- NULL
diff --git a/inst/ChangeLog b/inst/ChangeLog
index 21a62bf..4c030db 100644
--- a/inst/ChangeLog
+++ b/inst/ChangeLog
@@ -1,7 +1,29 @@
-$Date: 2013-09-25 09:53:42 +0300 (Wed, 25 Sep 2013) $
+$Date: 2013-12-12 12:06:44 +0200 (Thu, 12 Dec 2013) $
VEGAN RELEASE VERSIONS at http://cran.r-project.org/
+Version 2.0-10 (released December 12, 2013)
+
+ * r2815: update email in simper.Rd.
+ * merge 2809,2810: treat all non-numeric variables as factors
+ inenvfit.
+ * merge 2713 man/: remove references to very old R versions in man
+ files (R/ part of this rev not applied)
+ * merge 2708: adapt quantilesto test direction.
+ * merge 2679: add collector curve to renyiaccum.
+ * merge 2678: renyiaccum can plot one scale.
+ * merge 2641: subset in renyi/spec/tsallisaccum.
+ * merge 2630,1,2: fisherfit new algo and delete profile & confint.
+ * merge 2628,9: plot vectorfit *should* work with constant
+ (non-variable) vectors: partial conflict, needs checking.
+ * merge 2627: zap zeros in print.cca.
+ * merge 2626: nestednodf fill and consinstency in quantitative
+ data.
+ * merge 2527: adapt permutest.betadisper to the CRAN release of
+ permute 0.8-0.
+ * merge 2451, 2454, 2455, 2465: weighted specaccum.
+ * conflicts (not applied): r2625 (oecosimu), 2638 (oecosimu.Rd)
+
Version 2.0-9 (released September 25, 2013)
* merge 2618: a typo.
diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd
index 97df479..f8124dc 100644
--- a/inst/NEWS.Rd
+++ b/inst/NEWS.Rd
@@ -2,6 +2,81 @@
\title{vegan News}
\encoding{UTF-8}
+\section{Changes in version 2.0-10}{
+
+ \subsection{GENERAL}{
+ \itemize{
+
+ \item This version is adapted to the changes in \pkg{permute}
+ package version 0.8-0 and no more triggers NOTEs in package
+ checks. This release may be the last of the 2.0 series, and the
+ next \pkg{vegan} release is scheduled to be a major release with
+ newly designed \code{oecosimu} and community pattern simulation,
+ support for parallel processing, and full support of the
+ \pkg{permute} package. If you are interested in these
+ developments, you may try the development versions of
+ \pkg{vegan} in
+ \href{http://r-forge.r-project.org/projects/vegan/}{R-Forge} or
+ \href{https://github.com/jarioksa/vegan}{GitHub} and report the
+ problems and user experience to us. } } % end general
+
+ \subsection{BUG FIXES}{
+
+ \itemize{
+
+ \item \code{envfit} function assumed that all external variables
+ were either numeric or factors, and failed if they were, say,
+ character strings. Now only numeric variables are taken as
+ continuous vectors, and all other variables (character strings,
+ logical) are coerced to factors if possible. The function also
+ should work with degenerate data, like only one level of a
+ factor or a constant value of a continuous environmental
+ variable. The ties were wrongly in assessing permutation
+ \eqn{P}-values in \code{vectorfit}.
+
+ \item \code{nestednodf} with quantitative data was not
+ consistent with binary models, and the fill was wrongly
+ calculated with quantitative data.
+
+ \item \code{oecosimu} now correctly adapts displayed quantiles
+ of simulated values to the \code{alternative} test direction.
+
+ \item \code{renyiaccum} plotting failed if only one level of
+ diversity \code{scale} was used.
+
+ }
+ } % bug fixes
+
+ \subsection{NEW FEATURES}{
+ \itemize{
+
+ \item The Kempton and Taylor algorithm was found unreliable in
+ \code{fisherfit} and \code{fisher.alpha}, and now the estimation
+ of Fisher \eqn{\alpha}{alpha} is only based on the number of
+ species and the number of individuals. The estimation of
+ standard errors and profile confidence intervals also had to be
+ scrapped.
+
+ \item \code{renyiaccum}, \code{specaccum} and
+ \code{tsallisaccum} functions gained \code{subset} argument.
+
+ \item \code{renyiaccum} can now add a \code{collector} curve to
+ to the analysis. The collector curve is the diversity
+ accumulation in the order of the sampling units. With an
+ interesting ordering or sampling units this allows comparing
+ actual species accumulations with the expected randomized
+ accumulation.
+
+ \item \code{specaccum} can now perform weighted accumulation
+ using the sampling effort as weights.
+
+ }
+
+ } % new features
+
+
+} % end 2.0-10
+
\section{Changes in version 2.0-9}{
\itemize{
diff --git a/inst/doc/FAQ-vegan.pdf b/inst/doc/FAQ-vegan.pdf
index 538c746..34554d2 100644
Binary files a/inst/doc/FAQ-vegan.pdf and b/inst/doc/FAQ-vegan.pdf differ
diff --git a/inst/doc/NEWS.html b/inst/doc/NEWS.html
index f565471..4694118 100644
--- a/inst/doc/NEWS.html
+++ b/inst/doc/NEWS.html
@@ -8,6 +8,95 @@
<h2>vegan News</h2>
+<h3>Changes in version 2.0-10</h3>
+
+
+
+<h4>GENERAL</h4>
+
+
+<ul>
+<li><p> This version is adapted to the changes in <span class="pkg">permute</span>
+package version 0.8-0 and no more triggers NOTEs in package
+checks. This release may be the last of the 2.0 series, and the
+next <span class="pkg">vegan</span> release is scheduled to be a major release with
+newly designed <code>oecosimu</code> and community pattern simulation,
+support for parallel processing, and full support of the
+<span class="pkg">permute</span> package. If you are interested in these
+developments, you may try the development versions of
+<span class="pkg">vegan</span> in
+<a href="http://r-forge.r-project.org/projects/vegan/">R-Forge</a> or
+<a href="https://github.com/jarioksa/vegan">GitHub</a> and report the
+problems and user experience to us. </p>
+</li></ul>
+
+
+
+<h4>BUG FIXES</h4>
+
+
+<ul>
+<li> <p><code>envfit</code> function assumed that all external variables
+were either numeric or factors, and failed if they were, say,
+character strings. Now only numeric variables are taken as
+continuous vectors, and all other variables (character strings,
+logical) are coerced to factors if possible. The function also
+should work with degenerate data, like only one level of a
+factor or a constant value of a continuous environmental
+variable. The ties were wrongly in assessing permutation
+<i>P</i>-values in <code>vectorfit</code>.
+</p>
+</li>
+<li> <p><code>nestednodf</code> with quantitative data was not
+consistent with binary models, and the fill was wrongly
+calculated with quantitative data.
+</p>
+</li>
+<li> <p><code>oecosimu</code> now correctly adapts displayed quantiles
+of simulated values to the <code>alternative</code> test direction.
+</p>
+</li>
+<li> <p><code>renyiaccum</code> plotting failed if only one level of
+diversity <code>scale</code> was used.
+</p>
+</li></ul>
+
+
+
+
+<h4>NEW FEATURES</h4>
+
+
+
+<ul>
+<li><p> The Kempton and Taylor algorithm was found unreliable in
+<code>fisherfit</code> and <code>fisher.alpha</code>, and now the estimation
+of Fisher <i>alpha</i> is only based on the number of
+species and the number of individuals. The estimation of
+standard errors and profile confidence intervals also had to be
+scrapped.
+</p>
+</li>
+<li> <p><code>renyiaccum</code>, <code>specaccum</code> and
+<code>tsallisaccum</code> functions gained <code>subset</code> argument.
+</p>
+</li>
+<li> <p><code>renyiaccum</code> can now add a <code>collector</code> curve to
+to the analysis. The collector curve is the diversity
+accumulation in the order of the sampling units. With an
+interesting ordering or sampling units this allows comparing
+actual species accumulations with the expected randomized
+accumulation.
+</p>
+</li>
+<li> <p><code>specaccum</code> can now perform weighted accumulation
+using the sampling effort as weights.
+</p>
+</li></ul>
+
+
+
+
<h3>Changes in version 2.0-9</h3>
diff --git a/inst/doc/decision-vegan.pdf b/inst/doc/decision-vegan.pdf
index f93ed46..d6c1bd2 100644
Binary files a/inst/doc/decision-vegan.pdf and b/inst/doc/decision-vegan.pdf differ
diff --git a/inst/doc/diversity-vegan.R b/inst/doc/diversity-vegan.R
index 5965def..5225d29 100644
--- a/inst/doc/diversity-vegan.R
+++ b/inst/doc/diversity-vegan.R
@@ -118,40 +118,34 @@ plot(fish)
###################################################
-### code chunk number 18: diversity-vegan.Rnw:338-339
-###################################################
-confint(fish)
-
-
-###################################################
-### code chunk number 19: diversity-vegan.Rnw:362-363
+### code chunk number 18: diversity-vegan.Rnw:351-352
###################################################
prestondistr(BCI[k,])
###################################################
-### code chunk number 20: diversity-vegan.Rnw:394-396
+### code chunk number 19: diversity-vegan.Rnw:383-385
###################################################
rad <- radfit(BCI[k,])
rad
###################################################
-### code chunk number 21: diversity-vegan.Rnw:399-400
+### code chunk number 20: diversity-vegan.Rnw:388-389
###################################################
getOption("SweaveHooks")[["fig"]]()
print(radlattice(rad))
###################################################
-### code chunk number 22: a
+### code chunk number 21: a
###################################################
sac <- specaccum(BCI)
plot(sac, ci.type="polygon", ci.col="yellow")
###################################################
-### code chunk number 23: diversity-vegan.Rnw:469-470
+### code chunk number 22: diversity-vegan.Rnw:458-459
###################################################
getOption("SweaveHooks")[["fig"]]()
sac <- specaccum(BCI)
@@ -159,33 +153,33 @@ plot(sac, ci.type="polygon", ci.col="yellow")
###################################################
-### code chunk number 24: diversity-vegan.Rnw:498-499
+### code chunk number 23: diversity-vegan.Rnw:487-488
###################################################
ncol(BCI)/mean(specnumber(BCI)) - 1
###################################################
-### code chunk number 25: diversity-vegan.Rnw:516-518
+### code chunk number 24: diversity-vegan.Rnw:505-507
###################################################
beta <- vegdist(BCI, binary=TRUE)
mean(beta)
###################################################
-### code chunk number 26: diversity-vegan.Rnw:525-526
+### code chunk number 25: diversity-vegan.Rnw:514-515
###################################################
betadiver(help=TRUE)
###################################################
-### code chunk number 27: diversity-vegan.Rnw:544-546
+### code chunk number 26: diversity-vegan.Rnw:533-535
###################################################
z <- betadiver(BCI, "z")
quantile(z)
###################################################
-### code chunk number 28: diversity-vegan.Rnw:556-561
+### code chunk number 27: diversity-vegan.Rnw:545-550
###################################################
data(dune)
data(dune.env)
@@ -195,46 +189,46 @@ mod
###################################################
-### code chunk number 29: diversity-vegan.Rnw:564-565
+### code chunk number 28: diversity-vegan.Rnw:553-554
###################################################
getOption("SweaveHooks")[["fig"]]()
boxplot(mod)
###################################################
-### code chunk number 30: diversity-vegan.Rnw:622-623
+### code chunk number 29: diversity-vegan.Rnw:611-612
###################################################
specpool(BCI)
###################################################
-### code chunk number 31: diversity-vegan.Rnw:628-630
+### code chunk number 30: diversity-vegan.Rnw:617-619
###################################################
s <- sample(nrow(BCI), 25)
specpool(BCI[s,])
###################################################
-### code chunk number 32: diversity-vegan.Rnw:641-642
+### code chunk number 31: diversity-vegan.Rnw:630-631
###################################################
estimateR(BCI[k,])
###################################################
-### code chunk number 33: diversity-vegan.Rnw:678-680
+### code chunk number 32: diversity-vegan.Rnw:667-669
###################################################
veiledspec(prestondistr(BCI[k,]))
veiledspec(BCI[k,])
###################################################
-### code chunk number 34: diversity-vegan.Rnw:694-695
+### code chunk number 33: diversity-vegan.Rnw:683-684
###################################################
smo <- beals(BCI)
###################################################
-### code chunk number 35: a
+### code chunk number 34: a
###################################################
j <- which(colnames(BCI) == "Ceiba.pentandra")
plot(beals(BCI, species=j, include=FALSE), BCI[,j],
@@ -243,7 +237,7 @@ plot(beals(BCI, species=j, include=FALSE), BCI[,j],
###################################################
-### code chunk number 36: diversity-vegan.Rnw:708-709
+### code chunk number 35: diversity-vegan.Rnw:697-698
###################################################
getOption("SweaveHooks")[["fig"]]()
j <- which(colnames(BCI) == "Ceiba.pentandra")
diff --git a/inst/doc/diversity-vegan.Rnw b/inst/doc/diversity-vegan.Rnw
index 70178fe..ff84ad6 100644
--- a/inst/doc/diversity-vegan.Rnw
+++ b/inst/doc/diversity-vegan.Rnw
@@ -9,7 +9,7 @@
\title{Vegan: ecological diversity} \author{Jari Oksanen}
-\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2597 2013-08-28 08:56:55Z jarioksa $ $
+\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2807 2013-12-05 11:50:52Z jarioksa $ $
processed with vegan \Sexpr{packageDescription("vegan", field="Version")}
in \Sexpr{R.version.string} on \today}}
@@ -326,18 +326,7 @@ plot(fish)
(\Sexpr{k}).}
\label{fig:fisher}
\end{figure}
-We already saw $\alpha$ as a diversity index. Now we also obtained
-estimate of standard error of $\alpha$ (these also are optionally
-available in \code{fisher.alpha}). The standard errors are based on
-the second derivatives (curvature) of log-likelihood at the solution
-of $\alpha$. The distribution of $\alpha$ is often non-normal
-and skewed, and standard errors are of not much use. However,
-\code{fisherfit} has a \code{profile} method that can be used to
-inspect the validity of normal assumptions, and will be used in
-calculations of confidence intervals from profile deviance:
-<<>>=
-confint(fish)
-@
+We already saw $\alpha$ as a diversity index.
Preston's log-normal model is the main challenger to Fisher's
log-series \citep{Preston48}. Instead of plotting species by
diff --git a/inst/doc/diversity-vegan.pdf b/inst/doc/diversity-vegan.pdf
index 375d17f..ccb277a 100644
Binary files a/inst/doc/diversity-vegan.pdf and b/inst/doc/diversity-vegan.pdf differ
diff --git a/inst/doc/intro-vegan.pdf b/inst/doc/intro-vegan.pdf
index b3ccbbb..254576b 100644
Binary files a/inst/doc/intro-vegan.pdf and b/inst/doc/intro-vegan.pdf differ
diff --git a/man/betadisper.Rd b/man/betadisper.Rd
index 2156299..d25a5bf 100644
--- a/man/betadisper.Rd
+++ b/man/betadisper.Rd
@@ -279,7 +279,7 @@ groups[c(2,20)] <- NA
dis[c(2, 20)] <- NA
mod2 <- betadisper(dis, groups) ## warnings
mod2
-permutest(mod2, control = permControl(nperm = 100))
+permutest(mod2, control = how(nperm = 100))
anova(mod2)
plot(mod2)
boxplot(mod2)
@@ -288,7 +288,7 @@ plot(TukeyHSD(mod2))
## Using group centroids
mod3 <- betadisper(dis, groups, type = "centroid")
mod3
-permutest(mod3, control = permControl(nperm = 100))
+permutest(mod3, control = how(nperm = 100))
anova(mod3)
plot(mod3)
boxplot(mod3)
diff --git a/man/diversity.Rd b/man/diversity.Rd
index 465bc79..324f33d 100644
--- a/man/diversity.Rd
+++ b/man/diversity.Rd
@@ -20,7 +20,7 @@ rrarefy(x, sample)
drarefy(x, sample)
rarecurve(x, step = 1, sample, xlab = "Sample Size", ylab = "Species",
label = TRUE, ...)
-fisher.alpha(x, MARGIN = 1, se = FALSE, ...)
+fisher.alpha(x, MARGIN = 1, ...)
specnumber(x, groups, MARGIN = 1)
}
@@ -86,11 +86,7 @@ specnumber(x, groups, MARGIN = 1)
\code{fisher.alpha} estimates the \eqn{\alpha} parameter of
Fisher's logarithmic series (see \code{\link{fisherfit}}).
The estimation is possible only for genuine
- counts of individuals. The function can optionally return standard
- errors of \eqn{\alpha}. These should be regarded only as rough
- indicators of the accuracy: the confidence limits of \eqn{\alpha} are
- strongly non-symmetric and the standard errors cannot be used in
- Normal inference.
+ counts of individuals.
Function \code{specnumber} finds the number of species. With
\code{MARGIN = 2}, it finds frequencies of species. If \code{groups}
diff --git a/man/fisherfit.Rd b/man/fisherfit.Rd
index ba462f4..3ab5a78 100644
--- a/man/fisherfit.Rd
+++ b/man/fisherfit.Rd
@@ -2,9 +2,6 @@
\alias{fisherfit}
\alias{as.fisher}
\alias{plot.fisherfit}
-\alias{profile.fisherfit}
-\alias{confint.fisherfit}
-\alias{plot.profile.fisherfit}
\alias{prestonfit}
\alias{prestondistr}
\alias{as.preston}
@@ -25,9 +22,6 @@
}
\usage{
fisherfit(x, ...)
-\method{confint}{fisherfit}(object, parm, level = 0.95, ...)
-\method{profile}{fisherfit}(fitted, alpha = 0.01, maxsteps = 20, del = zmax/5,
- ...)
prestonfit(x, tiesplit = TRUE, ...)
prestondistr(x, truncate = -1, ...)
\method{plot}{prestonfit}(x, xlab = "Frequency", ylab = "Species", bar.col = "skyblue",
@@ -45,12 +39,6 @@ as.preston(x, tiesplit = TRUE, ...)
\arguments{
\item{x}{Community data vector for fitting functions or their result
object for \code{plot} functions.}
- \item{object, fitted}{Fitted model.}
- \item{parm}{Not used.}
- \item{level}{The confidence level required.}
- \item{alpha}{The extend of profiling as significance.}
- \item{maxsteps}{Maximum number of steps in profiling.}
- \item{del}{Step length.}
\item{tiesplit}{Split frequencies \eqn{1, 2, 4, 8} etc between adjacent
octaves.}
\item{truncate}{Truncation point for log-Normal model, in log2
@@ -73,9 +61,8 @@ as.preston(x, tiesplit = TRUE, ...)
\details{
In Fisher's logarithmic series the expected
number of species \eqn{f} with \eqn{n} observed individuals is
- \eqn{f_n = \alpha x^n / n} (Fisher et al. 1943). The estimation
- follows Kempton & Taylor (1974) and uses function
- \code{\link{nlm}}. The estimation is possible only for genuine
+ \eqn{f_n = \alpha x^n / n} (Fisher et al. 1943).
+ The estimation is possible only for genuine
counts of individuals. The parameter \eqn{\alpha} is used as a
diversity index, and \eqn{\alpha} and its standard error can be
estimated with a separate function \code{\link{fisher.alpha}}. The
@@ -84,20 +71,6 @@ as.preston(x, tiesplit = TRUE, ...)
function \code{as.fisher} transforms abundance data into Fisher
frequency table.
- Function \code{fisherfit} estimates the standard error of
- \eqn{\alpha}{alpha}. However, the confidence limits cannot be directly
- estimated from the standard errors, but you should use function
- \code{confint} based on profile likelihood. Function \code{confint}
- uses function \code{\link[MASS]{confint.glm}} of the \pkg{MASS}
- package, using \code{profile.fisherfit} for the profile
- likelihood. Function \code{profile.fisherfit} follows
- \code{\link[MASS]{profile.glm}} and finds the \eqn{\tau}{tau} parameter or
- signed square root of two times log-Likelihood profile. The profile can
- be inspected with a \code{plot} function which shows the \eqn{\tau}{tau}
- and a dotted line corresponding to the Normal assumption: if standard
- errors can be directly used in Normal inference these two lines
- are similar.
-
Preston (1948) was not satisfied with Fisher's model which seemed to
imply infinite species richness, and postulated that rare species is
a diminishing class and most species are in the middle of frequency
@@ -162,11 +135,8 @@ as.preston(x, tiesplit = TRUE, ...)
\code{method}. Function \code{prestondistr} omits the entry
\code{fitted}. The function \code{fisherfit} returns the result of
\code{\link{nlm}}, where item \code{estimate} is \eqn{\alpha}. The
- result object is amended with the following items:
- \item{df.residuals}{Residual degrees of freedom.}
- \item{nuisance}{Parameter \eqn{x}.} \item{fisher}{Observed data
- from \code{as.fisher}.}
-
+ result object is amended with the \code{nuisance} parameter and item
+ \code{fisher} for the observed data from \code{as.fisher}
}
\references{
Fisher, R.A., Corbet, A.S. & Williams, C.B. (1943). The relation
@@ -174,10 +144,6 @@ as.preston(x, tiesplit = TRUE, ...)
random sample of animal population. \emph{Journal of Animal Ecology}
12: 42--58.
- Kempton, R.A. & Taylor, L.R. (1974). Log-series and log-normal
- parameters as diversity discriminators for
- Lepidoptera. \emph{Journal of Animal Ecology} 43: 381--399.
-
Preston, F.W. (1948) The commonness and rarity of
species. \emph{Ecology} 29, 254--283.
@@ -186,7 +152,7 @@ as.preston(x, tiesplit = TRUE, ...)
distribution. \emph{Journal of Animal Ecology} 74, 409--422.
}
-\author{Bob O'Hara (\code{fisherfit}) and Jari Oksanen. }
+\author{Bob O'Hara and Jari Oksanen. }
\seealso{\code{\link{diversity}}, \code{\link{fisher.alpha}},
\code{\link{radfit}}, \code{\link{specpool}}. Function
@@ -200,8 +166,6 @@ as.preston(x, tiesplit = TRUE, ...)
data(BCI)
mod <- fisherfit(BCI[5,])
mod
-plot(profile(mod))
-confint(mod)
# prestonfit seems to need large samples
mod.oct <- prestonfit(colSums(BCI))
mod.ll <- prestondistr(colSums(BCI))
diff --git a/man/nobs.adonis.Rd b/man/nobs.adonis.Rd
index e7eca9c..69d8565 100644
--- a/man/nobs.adonis.Rd
+++ b/man/nobs.adonis.Rd
@@ -31,14 +31,15 @@
}
}
-\details{ Function \code{nobs} is generic in \R version 2.13.0, and
+\details{ Function \code{nobs} is generic in \R, and
\pkg{vegan} provides methods for objects from \code{\link{adonis}},
\code{\link{betadisper}}, \code{\link{cca}} and other related
methods, \code{\link{CCorA}}, \code{\link{decorana}},
\code{\link{isomap}}, \code{\link{metaMDS}}, \code{\link{pcnm}},
\code{\link{procrustes}}, \code{\link{radfit}},
- \code{\link{varpart}} and \code{\link{wcmdscale}}. } \value{ A
- single number, normally an integer, giving the number of
+ \code{\link{varpart}} and \code{\link{wcmdscale}}. }
+
+\value{ A single number, normally an integer, giving the number of
observations. }
\author{
diff --git a/man/permutest.betadisper.Rd b/man/permutest.betadisper.Rd
index 4fba1fd..3e33c6b 100644
--- a/man/permutest.betadisper.Rd
+++ b/man/permutest.betadisper.Rd
@@ -10,7 +10,7 @@
}
\usage{
\method{permutest}{betadisper}(x, pairwise = FALSE,
- control = permControl(nperm = 999), \dots)
+ control = how(nperm = 999), \dots)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
@@ -18,8 +18,7 @@
call to \code{betadisper}.}
\item{pairwise}{logical; perform pairwise comparisons of group means?}
\item{control}{a list of control values for the permutations
- to replace the default values returned by the function
- \code{\link{permControl}}}
+ as returned by the function \code{\link[permute]{how}}}
\item{\dots}{Arguments passed to other methods.}
}
\details{
@@ -49,7 +48,7 @@
pairwise comparisons of group mean distances (dispersions or variances).}
\item{groups}{character; the levels of the grouping factor.}
\item{control}{a list, the result of a call to
- \code{\link{permControl}}.}
+ \code{\link{how}}.}
}
\references{
Anderson, M.J. (2006) Distance-based tests for homogeneity of
diff --git a/man/renyi.Rd b/man/renyi.Rd
index ef38b00..0bd0ad3 100644
--- a/man/renyi.Rd
+++ b/man/renyi.Rd
@@ -14,16 +14,18 @@
\code{renyiaccum} finds these statistics with accumulating sites.
}
\usage{
-renyi(x, scales = c(0, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, Inf), hill = FALSE)
+renyi(x, scales = c(0, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, Inf),
+ hill = FALSE)
\method{plot}{renyi}(x, ...)
renyiaccum(x, scales = c(0, 0.5, 1, 2, 4, Inf), permutations = 100,
- raw = FALSE, ...)
-\method{plot}{renyiaccum} (x, what = c("mean", "Qnt 0.025", "Qnt 0.975"), type = "l",
+ raw = FALSE, collector = FALSE, subset, ...)
+\method{plot}{renyiaccum}(x, what = c("Collector", "mean", "Qnt 0.025", "Qnt 0.975"),
+ type = "l",
...)
-\method{persp}{renyiaccum} (x, theta = 220, col = heat.colors(100), zlim, ...)
+\method{persp}{renyiaccum}(x, theta = 220, col = heat.colors(100), zlim, ...)
rgl.renyiaccum(x, rgl.height = 0.2, ...)
}
-%- maybe also 'usage' for other objects documented here.
+
\arguments{
\item{x}{Community data matrix or plotting object. }
\item{scales}{Scales of \enc{Rényi}{Renyi} diversity.}
@@ -33,6 +35,12 @@ rgl.renyiaccum(x, rgl.height = 0.2, ...)
\item{raw}{if \code{FALSE} then return summary statistics of
permutations, and if \code{TRUE} then returns the individual
permutations.}
+ \item{collector}{Accumulate the diversities in the order the sites are
+ in the data set, and the collector curve can be plotted against
+ summary of permutations. The argument is ignored if \code{raw = TRUE}.
+ }
+ \item{subset}{logical expression indicating sites (rows) to keep: missing
+ values are taken as \code{FALSE}.}
\item{what}{Items to be plotted.}
\item{type}{Type of plot, where \code{type = "l"} means lines.}
\item{theta}{Angle defining the viewing direction (azimuthal) in
diff --git a/man/screeplot.cca.Rd b/man/screeplot.cca.Rd
index ee83d07..1252a72 100644
--- a/man/screeplot.cca.Rd
+++ b/man/screeplot.cca.Rd
@@ -116,10 +116,7 @@ bstick(n, \dots)
Legendre, P. and Legendre, L. (2012) \emph{Numerical Ecology}. 3rd English
ed. Elsevier.
}
-\note{Function \code{screeplot} is generic from \code{R} version
- 2.5.0. In these versions you can use plain \code{screeplot} command
- without suffices \code{cca}, \code{prcomp} etc.
- }
+
\author{Gavin L. Simpson}
\seealso{
\code{\link{cca}}, \code{\link{decorana}}, \code{\link{princomp}} and
diff --git a/man/simper.Rd b/man/simper.Rd
index fb49641..22d6375 100644
--- a/man/simper.Rd
+++ b/man/simper.Rd
@@ -83,7 +83,7 @@ data(dune.env)
summary(sim)
}
\author{
- Eduard Szöcs \email{szoe8822 at uni-landau.de}
+ Eduard Szöcs \email{eduardszoecs at gmail.com}
}
\references{
diff --git a/man/specaccum.Rd b/man/specaccum.Rd
index 896ef71..7c28e42 100644
--- a/man/specaccum.Rd
+++ b/man/specaccum.Rd
@@ -17,10 +17,11 @@
}
\usage{
specaccum(comm, method = "exact", permutations = 100,
- conditioned =TRUE, gamma = "jack1", ...)
-\method{plot}{specaccum}(x, add = FALSE, ci = 2, ci.type = c("bar", "line", "polygon"),
- col = par("fg"), ci.col = col, ci.lty = 1, xlab,
- ylab = x$method, ylim, xvar = c("sites", "individuals"), ...)
+ conditioned =TRUE, gamma = "jack1", w = NULL, subset, ...)
+\method{plot}{specaccum}(x, add = FALSE, random = FALSE, ci = 2,
+ ci.type = c("bar", "line", "polygon"), col = par("fg"), ci.col = col,
+ ci.lty = 1, xlab, ylab = x$method, ylim,
+ xvar = c("sites", "individuals", "effort"), ...)
\method{boxplot}{specaccum}(x, add = FALSE, ...)
fitspecaccum(object, model, method = "random", ...)
\method{plot}{fitspecaccum}(x, col = par("fg"), lty = 1, xlab = "Sites",
@@ -43,9 +44,14 @@ fitspecaccum(object, model, method = "random", ...)
\item{conditioned}{ Estimation of standard deviation is conditional on
the empirical dataset for the exact SAC}
\item{gamma}{Method for estimating the total extrapolated number of species in the
- survey area by function \code{\link{specpool}}}
+ survey area by function \code{\link{specpool}}}
+ \item{w}{Weights giving the sampling effort (an experimental feature
+ that may be removed).}
+ \item{subset}{logical expression indicating sites (rows) to keep: missing
+ values are taken as \code{FALSE}.}
\item{x}{A \code{specaccum} result object}
\item{add}{Add to an existing graph.}
+ \item{random}{\dots}
\item{ci}{Multiplier used to get confidence intervals from standard
deviation (standard error of the estimate). Value \code{ci = 0}
suppresses drawing confidence intervals.}
diff --git a/man/tsallis.Rd b/man/tsallis.Rd
index edca3e5..1eb4255 100644
--- a/man/tsallis.Rd
+++ b/man/tsallis.Rd
@@ -9,65 +9,112 @@ Function \code{tsallis} find Tsallis diversities with any scale or the correspon
}
\usage{
tsallis(x, scales = seq(0, 2, 0.2), norm = FALSE, hill = FALSE)
-tsallisaccum(x, scales = seq(0, 2, 0.2), permutations = 100, raw = FALSE, ...)
+tsallisaccum(x, scales = seq(0, 2, 0.2), permutations = 100,
+ raw = FALSE, subset, ...)
\method{persp}{tsallisaccum}(x, theta = 220, phi = 15, col = heat.colors(100), zlim, ...)
}
-%- maybe also 'usage' for other objects documented here.
+
\arguments{
\item{x}{Community data matrix or plotting object. }
\item{scales}{Scales of Tsallis diversity.}
- \item{norm}{Logical, if \code{TRUE} diversity values are normalized by their maximum (diversity value at equiprobability conditions).}
+
+ \item{norm}{Logical, if \code{TRUE} diversity values are normalized
+ by their maximum (diversity value at equiprobability conditions).}
+
\item{hill}{Calculate Hill numbers.}
- \item{permutations}{Number of random permutations in accumulating sites.}
- \item{raw}{If \code{FALSE} then return summary statistics of permutations, and if TRUE then returns the individual permutations.}
- \item{theta, phi}{angles defining the viewing direction. \code{theta} gives the azimuthal direction and \code{phi} the colatitude.}
- \item{col}{Colours used for surface.}
- \item{zlim}{Limits of vertical axis.}
- \item{\dots}{Other arguments which are passed to \code{tsallis} and to graphical functions.}
+
+ \item{permutations}{Number of random permutations in accumulating
+ sites.}
-}
-\details{
-The Tsallis diversity (also equivalent to Patil and Taillie diversity) is a one-parametric generalised entropy function, defined as:
+ \item{raw}{If \code{FALSE} then return summary statistics of
+ permutations, and if TRUE then returns the individual
+ permutations.}
+
+ \item{subset}{logical expression indicating sites (rows) to keep:
+ missing values are taken as \code{FALSE}.}
+
+ \item{theta, phi}{angles defining the viewing
+ direction. \code{theta} gives the azimuthal direction and
+ \code{phi} the colatitude.}
+
+ \item{col}{Colours used for surface.} \item{zlim}{Limits of
+ vertical axis.}
+
+ \item{\dots}{Other arguments which are passed to \code{tsallis} and
+ to graphical functions.}
+
+}
+
+\details{ The Tsallis diversity (also equivalent to Patil and Taillie
+diversity) is a one-parametric generalised entropy function, defined
+as:
\deqn{H_q = \frac{1}{q-1} (1-\sum_{i=1}^S p_i^q)}{H.q = 1/(q-1)(1-sum(p^q))}
-where \eqn{q} is a scale parameter, \eqn{S} the number of species in the sample (Tsallis 1988, Tothmeresz 1995). This diversity is concave for all \eqn{q>0}, but non-additive (Keylock 2005). For \eqn{q=0} it gives the number of species minus one, as \eqn{q} tends to 1 this gives Shannon diversity, for \eqn{q=2} this gives the Simpson index (see function \code{\link{diversity}}).
+where \eqn{q} is a scale parameter, \eqn{S} the number of species in
+the sample (Tsallis 1988, Tothmeresz 1995). This diversity is concave
+for all \eqn{q>0}, but non-additive (Keylock 2005). For \eqn{q=0} it
+gives the number of species minus one, as \eqn{q} tends to 1 this
+gives Shannon diversity, for \eqn{q=2} this gives the Simpson index
+(see function \code{\link{diversity}}).
-If \code{norm = TRUE}, \code{tsallis} gives values normalized by the maximum:
+If \code{norm = TRUE}, \code{tsallis} gives values normalized by the
+maximum:
\deqn{H_q(max) = \frac{S^{1-q}-1}{1-q}}{H.q(max) = (S^(1-q)-1)/(1-q)}
-where \eqn{S} is the number of species. As \eqn{q} tends to 1, maximum is defined as \eqn{ln(S)}.
+where \eqn{S} is the number of species. As \eqn{q} tends to 1, maximum
+is defined as \eqn{ln(S)}.
-If \code{hill = TRUE}, \code{tsallis} gives Hill numbers (numbers equivalents, see Jost 2007):
+If \code{hill = TRUE}, \code{tsallis} gives Hill numbers (numbers
+equivalents, see Jost 2007):
\deqn{D_q = (1-(q-1) H)^{1/(1-q)}}{D.q = (1-(q-1)*H)^(1/(1-q))}
-Details on plotting methods and accumulating values can be found on the help pages of the functions \code{\link{renyi}} and \code{\link{renyiaccum}}.
-}
-\value{
-Function \code{tsallis} returns a data frame of selected indices. Function \code{tsallisaccum} with argument \code{raw = FALSE} returns a three-dimensional array, where the first dimension are the accumulated sites, second dimension are the diversity scales, and third dimension are the summary statistics \code{mean}, \code{stdev}, \code{min}, \code{max}, \code{Qnt 0.025} and \code{Qnt 0.975}. With argument \code{raw = TRUE} the statistics on the third dimension are replaced with individu [...]
+Details on plotting methods and accumulating values can be found on
+the help pages of the functions \code{\link{renyi}} and
+\code{\link{renyiaccum}}.
}
+
+\value{
+Function \code{tsallis} returns a data frame of selected
+indices. Function \code{tsallisaccum} with argument \code{raw = FALSE}
+returns a three-dimensional array, where the first dimension are the
+accumulated sites, second dimension are the diversity scales, and
+third dimension are the summary statistics \code{mean}, \code{stdev},
+\code{min}, \code{max}, \code{Qnt 0.025} and \code{Qnt 0.975}. With
+argument \code{raw = TRUE} the statistics on the third dimension are
+replaced with individual permutation results. }
+
\references{
-Tsallis, C. (1988) Possible generalization of Boltzmann-Gibbs statistics.
- \emph{J. Stat. Phis.} 52, 479--487.
+
+Tsallis, C. (1988) Possible generalization of Boltzmann-Gibbs
+ statistics. \emph{J. Stat. Phis.} 52, 479--487.
Tothmeresz, B. (1995) Comparison of different methods for diversity
ordering. \emph{Journal of Vegetation Science} \bold{6}, 283--290.
-Patil, G. P. and Taillie, C. (1982) Diversity as a concept and its measurement.
- \emph{J. Am. Stat. Ass.} \bold{77}, 548--567.
+Patil, G. P. and Taillie, C. (1982) Diversity as a concept and its
+ measurement. \emph{J. Am. Stat. Ass.} \bold{77}, 548--567.
-Keylock, C. J. (2005) Simpson diversity and the Shannon-Wiener index as special cases of a generalized entropy.
- \emph{Oikos} \bold{109}, 203--207.
+Keylock, C. J. (2005) Simpson diversity and the Shannon-Wiener index
+ as special cases of a generalized entropy. \emph{Oikos} \bold{109},
+ 203--207.
-Jost, L (2007) Partitioning diversity into independent alpha and beta components.
- \emph{Ecology} \bold{88}, 2427--2439.
-}
-\author{\enc{Péter Sólymos}{Peter Solymos}, \email{solymos at ualberta.ca}, based on the code of Roeland Kindt and Jari Oksanen written for \code{renyi}}
-\seealso{
-Plotting methods and accumulation routines are based on functions \code{\link{renyi}} and \code{\link{renyiaccum}}. An object of class 'tsallisaccum' can be used with function \code{\link{rgl.renyiaccum}} as well. See also settings for \code{\link{persp}}.
+Jost, L (2007) Partitioning diversity into independent alpha and beta
+ components. \emph{Ecology} \bold{88}, 2427--2439.
}
+
+\author{\enc{Péter Sólymos}{Peter Solymos},
+\email{solymos at ualberta.ca}, based on the code of Roeland Kindt and
+Jari Oksanen written for \code{renyi}}
+
+\seealso{ Plotting methods and accumulation routines are based on
+functions \code{\link{renyi}} and \code{\link{renyiaccum}}. An object
+of class 'tsallisaccum' can be used with function
+\code{\link{rgl.renyiaccum}} as well. See also settings for
+\code{\link{persp}}. }
+
\examples{
data(BCI)
i <- sample(nrow(BCI), 12)
diff --git a/vignettes/FAQ-vegan.pdf b/vignettes/FAQ-vegan.pdf
index 538c746..34554d2 100644
Binary files a/vignettes/FAQ-vegan.pdf and b/vignettes/FAQ-vegan.pdf differ
diff --git a/vignettes/NEWS.html b/vignettes/NEWS.html
index f565471..4694118 100644
--- a/vignettes/NEWS.html
+++ b/vignettes/NEWS.html
@@ -8,6 +8,95 @@
<h2>vegan News</h2>
+<h3>Changes in version 2.0-10</h3>
+
+
+
+<h4>GENERAL</h4>
+
+
+<ul>
+<li><p> This version is adapted to the changes in <span class="pkg">permute</span>
+package version 0.8-0 and no more triggers NOTEs in package
+checks. This release may be the last of the 2.0 series, and the
+next <span class="pkg">vegan</span> release is scheduled to be a major release with
+newly designed <code>oecosimu</code> and community pattern simulation,
+support for parallel processing, and full support of the
+<span class="pkg">permute</span> package. If you are interested in these
+developments, you may try the development versions of
+<span class="pkg">vegan</span> in
+<a href="http://r-forge.r-project.org/projects/vegan/">R-Forge</a> or
+<a href="https://github.com/jarioksa/vegan">GitHub</a> and report the
+problems and user experience to us. </p>
+</li></ul>
+
+
+
+<h4>BUG FIXES</h4>
+
+
+<ul>
+<li> <p><code>envfit</code> function assumed that all external variables
+were either numeric or factors, and failed if they were, say,
+character strings. Now only numeric variables are taken as
+continuous vectors, and all other variables (character strings,
+logical) are coerced to factors if possible. The function also
+should work with degenerate data, like only one level of a
+factor or a constant value of a continuous environmental
+variable. The ties were wrongly in assessing permutation
+<i>P</i>-values in <code>vectorfit</code>.
+</p>
+</li>
+<li> <p><code>nestednodf</code> with quantitative data was not
+consistent with binary models, and the fill was wrongly
+calculated with quantitative data.
+</p>
+</li>
+<li> <p><code>oecosimu</code> now correctly adapts displayed quantiles
+of simulated values to the <code>alternative</code> test direction.
+</p>
+</li>
+<li> <p><code>renyiaccum</code> plotting failed if only one level of
+diversity <code>scale</code> was used.
+</p>
+</li></ul>
+
+
+
+
+<h4>NEW FEATURES</h4>
+
+
+
+<ul>
+<li><p> The Kempton and Taylor algorithm was found unreliable in
+<code>fisherfit</code> and <code>fisher.alpha</code>, and now the estimation
+of Fisher <i>alpha</i> is only based on the number of
+species and the number of individuals. The estimation of
+standard errors and profile confidence intervals also had to be
+scrapped.
+</p>
+</li>
+<li> <p><code>renyiaccum</code>, <code>specaccum</code> and
+<code>tsallisaccum</code> functions gained <code>subset</code> argument.
+</p>
+</li>
+<li> <p><code>renyiaccum</code> can now add a <code>collector</code> curve to
+to the analysis. The collector curve is the diversity
+accumulation in the order of the sampling units. With an
+interesting ordering or sampling units this allows comparing
+actual species accumulations with the expected randomized
+accumulation.
+</p>
+</li>
+<li> <p><code>specaccum</code> can now perform weighted accumulation
+using the sampling effort as weights.
+</p>
+</li></ul>
+
+
+
+
<h3>Changes in version 2.0-9</h3>
diff --git a/vignettes/decision-vegan.tex b/vignettes/decision-vegan.tex
index 8c8be0a..e860de2 100644
--- a/vignettes/decision-vegan.tex
+++ b/vignettes/decision-vegan.tex
@@ -9,8 +9,8 @@
\date{\footnotesize{$ $Id: decision-vegan.Rnw 2616 2013-09-11 08:34:17Z jarioksa $ $
processed with vegan
-2.0-9
-in R Under development (unstable) (2013-09-25 r63985) on \today}}
+2.0-10
+in R Under development (unstable) (2013-12-11 r64449) on \today}}
%% need no \usepackage{Sweave}
\begin{document}
@@ -507,19 +507,19 @@ Call: cca(formula = varespec[i, ] ~ Al + K, data
Inertia Proportion Rank
Total 2.0832 1.0000
-Constrained 0.2794 0.1341 2
-Unconstrained 1.8038 0.8659 21
+Constrained 0.1932 0.0927 2
+Unconstrained 1.8900 0.9073 21
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
- CCA1 CCA2
-0.21548 0.06392
+ CCA1 CCA2
+0.1298 0.0634
Eigenvalues for unconstrained axes:
CA1 CA2 CA3 CA4 CA5 CA6
-0.40322 0.31886 0.22005 0.18714 0.17563 0.11703
+0.52408 0.31643 0.21958 0.17766 0.17696 0.11951
CA7 CA8
-0.10042 0.08205
+0.08447 0.07063
(Showed only 8 of all 21 unconstrained eigenvalues)
\end{Soutput}
\end{Schunk}
@@ -547,11 +547,11 @@ remain within numerical accuracy:
> max(residuals(proc))
\end{Sinput}
\begin{Soutput}
-[1] 3.435509e-14
+[1] 2.67932e-14
\end{Soutput}
\end{Schunk}
In \code{cca} the difference would be somewhat larger than now
-observed 3.4355e-14 because site
+observed 2.6793e-14 because site
weights used for environmental variables are shuffled with the species
data.
diff --git a/vignettes/diversity-vegan.Rnw b/vignettes/diversity-vegan.Rnw
index 70178fe..ff84ad6 100644
--- a/vignettes/diversity-vegan.Rnw
+++ b/vignettes/diversity-vegan.Rnw
@@ -9,7 +9,7 @@
\title{Vegan: ecological diversity} \author{Jari Oksanen}
-\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2597 2013-08-28 08:56:55Z jarioksa $ $
+\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2807 2013-12-05 11:50:52Z jarioksa $ $
processed with vegan \Sexpr{packageDescription("vegan", field="Version")}
in \Sexpr{R.version.string} on \today}}
@@ -326,18 +326,7 @@ plot(fish)
(\Sexpr{k}).}
\label{fig:fisher}
\end{figure}
-We already saw $\alpha$ as a diversity index. Now we also obtained
-estimate of standard error of $\alpha$ (these also are optionally
-available in \code{fisher.alpha}). The standard errors are based on
-the second derivatives (curvature) of log-likelihood at the solution
-of $\alpha$. The distribution of $\alpha$ is often non-normal
-and skewed, and standard errors are of not much use. However,
-\code{fisherfit} has a \code{profile} method that can be used to
-inspect the validity of normal assumptions, and will be used in
-calculations of confidence intervals from profile deviance:
-<<>>=
-confint(fish)
-@
+We already saw $\alpha$ as a diversity index.
Preston's log-normal model is the main challenger to Fisher's
log-series \citep{Preston48}. Instead of plotting species by
diff --git a/vignettes/diversity-vegan.tex b/vignettes/diversity-vegan.tex
index e2d5220..cb3a38a 100644
--- a/vignettes/diversity-vegan.tex
+++ b/vignettes/diversity-vegan.tex
@@ -9,9 +9,9 @@
\title{Vegan: ecological diversity} \author{Jari Oksanen}
-\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2597 2013-08-28 08:56:55Z jarioksa $ $
- processed with vegan 2.0-9
- in R Under development (unstable) (2013-09-25 r63985) on \today}}
+\date{\footnotesize{$ $Id: diversity-vegan.Rnw 2807 2013-12-05 11:50:52Z jarioksa $ $
+ processed with vegan 2.0-10
+ in R Under development (unstable) (2013-12-11 r64449) on \today}}
%% need no \usepackage{Sweave}
\begin{document}
@@ -344,36 +344,17 @@ log-series for a randomly selected plot is (Fig. \ref{fig:fisher}):
\end{Sinput}
\begin{Soutput}
Fisher log series model
-No. of species: 82
-
- Estimate Std. Error
-alpha 30.584 4.1814
+No. of species: 92
+Fisher alpha: 35.12348
\end{Soutput}
\end{Schunk}
\begin{figure}
\includegraphics{diversity-vegan-017}
\caption{Fisher's log-series fitted to one randomly selected site
- (7).}
+ (34).}
\label{fig:fisher}
\end{figure}
-We already saw $\alpha$ as a diversity index. Now we also obtained
-estimate of standard error of $\alpha$ (these also are optionally
-available in \code{fisher.alpha}). The standard errors are based on
-the second derivatives (curvature) of log-likelihood at the solution
-of $\alpha$. The distribution of $\alpha$ is often non-normal
-and skewed, and standard errors are of not much use. However,
-\code{fisherfit} has a \code{profile} method that can be used to
-inspect the validity of normal assumptions, and will be used in
-calculations of confidence intervals from profile deviance:
-\begin{Schunk}
-\begin{Sinput}
-> confint(fish)
-\end{Sinput}
-\begin{Soutput}
- 2.5 % 97.5 %
-23.24833 39.75274
-\end{Soutput}
-\end{Schunk}
+We already saw $\alpha$ as a diversity index.
Preston's log-normal model is the main challenger to Fisher's
log-series \citep{Preston48}. Instead of plotting species by
@@ -394,7 +375,7 @@ octave, and the same for all species at the octave limits occurring 2,
the lower octave. Function \code{prestondistr} directly maximizes
truncated log-normal likelihood without binning data, and it is the
recommended alternative. Log-normal models usually fit poorly to the
-BCI data, but here our random plot (number 7):
+BCI data, but here our random plot (number 34):
\begin{Schunk}
\begin{Sinput}
> prestondistr(BCI[k,])
@@ -402,18 +383,18 @@ BCI data, but here our random plot (number 7):
\begin{Soutput}
Preston lognormal model
Method: maximized likelihood to log2 abundances
-No. of species: 82
+No. of species: 92
- mode width S0
- 1.184636 1.724112 21.142298
+ mode width S0
+ 0.9808822 1.7328484 24.2476646
Frequencies by Octave
- 0 1 2 3 4
-Observed 14.0000 21.50000 16.00000 17.00000 6.500000
-Fitted 16.6969 21.02141 18.90544 12.14537 5.573573
+ 0 1 2 3 4
+Observed 17.00000 24.50000 22.50000 16.00000 6.500000
+Fitted 20.65821 24.24619 20.39683 12.29845 5.315036
5 6
-Observed 6.000000 1.0000000
-Fitted 1.827072 0.4278351
+Observed 3.500000 2.0000000
+Fitted 1.646382 0.3655304
\end{Soutput}
\end{Schunk}
@@ -452,26 +433,26 @@ set gives (Fig. \ref{fig:rad}):
\end{Sinput}
\begin{Soutput}
RAD models, family poisson
-No. of species 82, total abundance 416
-
- par1 par2 par3 Deviance
-Null 44.1321
-Preemption 0.050871 35.5813
-Lognormal 1.0473 1.0934 15.0446
-Zipf 0.1343 -0.81154 37.1297
-Mandelbrot 4.4331 -1.6963 9.5855 6.8295
- AIC BIC
-Null 288.5829 288.5829
-Preemption 282.0320 284.4388
-Lognormal 263.4954 268.3088
-Zipf 285.5805 290.3940
-Mandelbrot 257.2803 264.5004
+No. of species 92, total abundance 447
+
+ par1 par2 par3 Deviance AIC
+Null 96.957 363.040
+Preemption 0.049501 94.601 362.684
+Lognormal 0.87031 1.2147 23.247 293.330
+Zipf 0.15445 -0.88735 20.796 290.879
+Mandelbrot 0.52179 -1.2176 2.4672 6.227 278.310
+ BIC
+Null 363.040
+Preemption 365.205
+Lognormal 298.373
+Zipf 295.922
+Mandelbrot 285.875
\end{Soutput}
\end{Schunk}
\begin{figure}
-\includegraphics{diversity-vegan-021}
+\includegraphics{diversity-vegan-020}
\caption{Ranked abundance distribution models for a random plot
- (no. 7). The best model has the lowest \textsc{aic}.}
+ (no. 34). The best model has the lowest \textsc{aic}.}
\label{fig:rad}
\end{figure}
@@ -539,7 +520,7 @@ The recommended is Kindt's exact method (Fig. \ref{fig:sac}):
\end{Sinput}
\end{Schunk}
\begin{figure}
-\includegraphics{diversity-vegan-023}
+\includegraphics{diversity-vegan-022}
\caption{Species accumulation curve for the BCI data; exact method.}
\label{fig:sac}
\end{figure}
@@ -699,7 +680,7 @@ Eigenvalues for PCoA axes:
\end{Soutput}
\end{Schunk}
\begin{figure}
-\includegraphics{diversity-vegan-029}
+\includegraphics{diversity-vegan-028}
\caption{Box plots of beta diversity measured as the average steepness
($z$) of the species area curve in the Arrhenius model $S = cX^z$ in
Management classes of dune meadows.}
@@ -775,10 +756,10 @@ the plots (but this is rarely true):
> specpool(BCI[s,])
\end{Sinput}
\begin{Soutput}
- Species chao chao.se jack1 jack1.se jack2
-All 206 228.1538 11.90403 229.04 7.528506 239.6583
+ Species chao chao.se jack1 jack1.se jack2
+All 207 229.3214 11.73157 231 6.499231 241.6567
boot boot.se n
-All 216.8594 4.088276 25
+All 218.3863 3.760674 25
\end{Soutput}
\end{Schunk}
@@ -795,12 +776,12 @@ two of these methods:
> estimateR(BCI[k,])
\end{Sinput}
\begin{Soutput}
- 7
-S.obs 82.000000
-S.chao1 105.625000
-se.chao1 13.008761
-S.ACE 108.315823
-se.ACE 4.913317
+ 34
+S.obs 92.000000
+S.chao1 127.062500
+se.chao1 17.669342
+S.ACE 124.460040
+se.ACE 5.531529
\end{Soutput}
\end{Schunk}
Chao's method is similar as above, but uses another, ``unbiased''
@@ -843,14 +824,14 @@ can try:
\end{Sinput}
\begin{Soutput}
Extrapolated Observed Veiled
- 91.370848 82.000000 9.370848
+ 105.32232 92.00000 13.32232
\end{Soutput}
\begin{Sinput}
> veiledspec(BCI[k,])
\end{Sinput}
\begin{Soutput}
Extrapolated Observed Veiled
- 97.93917 82.00000 15.93917
+ 111.38235 92.00000 19.38235
\end{Soutput}
\end{Schunk}
@@ -883,7 +864,7 @@ the target species in the smoothing (Fig. \ref{fig:beals}):
\end{Sinput}
\end{Schunk}
\begin{figure}
-\includegraphics{diversity-vegan-036}
+\includegraphics{diversity-vegan-035}
\caption{Beals smoothing for \emph{Ceiba pentandra}.}
\label{fig:beals}
\end{figure}
diff --git a/vignettes/intro-vegan.tex b/vignettes/intro-vegan.tex
index a28cc3a..ad70f6d 100644
--- a/vignettes/intro-vegan.tex
+++ b/vignettes/intro-vegan.tex
@@ -8,8 +8,8 @@
\date{\footnotesize{$ $Id: intro-vegan.Rnw 2597 2013-08-28 08:56:55Z jarioksa $ $
processed with vegan
-2.0-9
-in R Under development (unstable) (2013-09-25 r63985) on \today}}
+2.0-10
+in R Under development (unstable) (2013-12-11 r64449) on \today}}
%% need no \usepackage{Sweave}
\begin{document}
@@ -113,8 +113,12 @@ species scores to the configuration as weighted averages (function
\end{Sinput}
\begin{Soutput}
Run 0 stress 0.1192678
-Run 1 stress 0.1192683
-... procrustes: rmse 0.0003967661 max resid 0.001215737
+Run 1 stress 0.1183186
+... New best solution
+... procrustes: rmse 0.02026951 max resid 0.06495418
+Run 2 stress 0.1886532
+Run 3 stress 0.1183186
+... procrustes: rmse 1.293513e-05 max resid 4.605534e-05
*** Solution reached
\end{Soutput}
\begin{Sinput}
@@ -130,9 +134,9 @@ Data: dune
Distance: bray
Dimensions: 2
-Stress: 0.1192678
+Stress: 0.1183186
Stress type 1, weak ties
-Two convergent solutions found after 1 tries
+Two convergent solutions found after 3 tries
Scaling: centring, PC rotation, halfchange scaling
Species: expanded scores based on ‘dune’
\end{Soutput}
@@ -291,7 +295,7 @@ variables using permutation tests:
***VECTORS
NMDS1 NMDS2 r2 Pr(>r)
-A1 0.99008 0.14052 0.3798 0.02697 *
+A1 0.96474 0.26320 0.3649 0.02298 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
P values based on 1000 permutations.
@@ -300,10 +304,10 @@ P values based on 1000 permutations.
Centroids:
NMDS1 NMDS2
-ManagementBF -0.4474 -0.0193
-ManagementHF -0.2689 -0.1256
-ManagementNM 0.2976 0.5798
-ManagementSF 0.1502 -0.4654
+ManagementBF -0.4534 -0.0102
+ManagementHF -0.2636 -0.1282
+ManagementNM 0.2958 0.5790
+ManagementSF 0.1506 -0.4670
Goodness of fit:
r2 Pr(>r)
@@ -335,12 +339,12 @@ Link function: identity
Formula:
y ~ s(x1, x2, k = 10, bs = "tp", fx = FALSE)
-<environment: 0x36047f0>
+<environment: 0x3c181e8>
Estimated degrees of freedom:
-1.62 total = 2.62
+1.59 total = 2.59
-REML score: 41.42642
+REML score: 41.58727
\end{Soutput}
\end{Schunk}
\begin{figure}
@@ -481,8 +485,8 @@ Terms added sequentially (first to last)
Model: cca(formula = dune ~ A1 + Management, data = dune.env)
Df Chisq F N.Perm Pr(>F)
-A1 1 0.2248 2.5245 199 0.01 **
-Management 3 0.5550 2.0780 199 0.01 **
+A1 1 0.2248 2.5245 199 0.015 *
+Management 3 0.5550 2.0780 199 0.005 **
Residual 15 1.3355
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
@@ -502,7 +506,7 @@ Marginal effects of terms
Model: cca(formula = dune ~ A1 + Management, data = dune.env)
Df Chisq F N.Perm Pr(>F)
-A1 1 0.1759 1.9761 899 0.03222 *
+A1 1 0.1759 1.9761 699 0.02857 *
Management 3 0.5550 2.0780 199 0.00500 **
Residual 15 1.3355
---
@@ -517,12 +521,12 @@ Moreover, it is possible to analyse significance of each axis:
\end{Sinput}
\begin{Soutput}
Model: cca(formula = dune ~ A1 + Management, data = dune.env)
- Df Chisq F N.Perm Pr(>F)
-CCA1 1 0.3187 3.5801 199 0.005 **
-CCA2 1 0.2372 2.6640 199 0.010 **
-CCA3 1 0.1322 1.4845 299 0.110
-CCA4 1 0.0917 1.0297 99 0.390
-Residual 15 1.3355
+ Df Chisq F N.Perm Pr(>F)
+CCA1 1 0.3187 3.5801 199 0.00500 **
+CCA2 1 0.2372 2.6640 299 0.01667 *
+CCA3 1 0.1322 1.4845 199 0.11500
+CCA4 1 0.0917 1.0297 99 0.33000
+Residual 15 1.3355
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
\end{Soutput}
@@ -576,10 +580,10 @@ Permutation test for cca under reduced model
Terms added sequentially (first to last)
Model: cca(formula = dune ~ A1 + Management + Condition(Moisture), data = dune.env)
- Df Chisq F N.Perm Pr(>F)
-A1 1 0.1154 1.4190 99 0.08 .
-Management 3 0.3954 1.6205 99 0.02 *
-Residual 12 0.9761
+ Df Chisq F N.Perm Pr(>F)
+A1 1 0.1154 1.4190 99 0.15
+Management 3 0.3954 1.6205 99 0.01 **
+Residual 12 0.9761
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
\end{Soutput}
@@ -598,7 +602,7 @@ Permutations stratified within 'Moisture'
Model: cca(formula = dune ~ A1 + Management + Condition(Moisture), data = dune.env)
Df Chisq F N.Perm Pr(>F)
-A1 1 0.1154 1.4190 99 0.23
+A1 1 0.1154 1.4190 99 0.30
Management 3 0.3954 1.6205 99 0.01 **
Residual 12 0.9761
---
--
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