[Analogue-commits] r221 - in pkg: . tests tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri May 27 11:42:57 CEST 2011
Author: gsimpson
Date: 2011-05-27 11:42:57 +0200 (Fri, 27 May 2011)
New Revision: 221
Added:
pkg/tests/
pkg/tests/Examples/
pkg/tests/Examples/analogue-Ex.Rout.save
Log:
add a tests directory with a saved copy of output from running the examples
Added: pkg/tests/Examples/analogue-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/analogue-Ex.Rout.save (rev 0)
+++ pkg/tests/Examples/analogue-Ex.Rout.save 2011-05-27 09:42:57 UTC (rev 221)
@@ -0,0 +1,7741 @@
+
+R version 2.13.0 Patched (2011-05-20 r55969)
+Copyright (C) 2011 The R Foundation for Statistical Computing
+ISBN 3-900051-07-0
+Platform: x86_64-unknown-linux-gnu (64-bit)
+
+R is free software and comes with ABSOLUTELY NO WARRANTY.
+You are welcome to redistribute it under certain conditions.
+Type 'license()' or 'licence()' for distribution details.
+
+ Natural language support but running in an English locale
+
+R is a collaborative project with many contributors.
+Type 'contributors()' for more information and
+'citation()' on how to cite R or R packages in publications.
+
+Type 'demo()' for some demos, 'help()' for on-line help, or
+'help.start()' for an HTML browser interface to help.
+Type 'q()' to quit R.
+
+> pkgname <- "analogue"
+> source(file.path(R.home("share"), "R", "examples-header.R"))
+> options(warn = 1)
+> library('analogue')
+Loading required package: vegan
+This is vegan 1.17-8
+Loading required package: lattice
+Loading required package: grid
+Loading required package: MASS
+Loading required package: princurve
+This is analogue 0.7-0
+>
+> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> cleanEx()
+> nameEx("ImbrieKipp")
+> ### * ImbrieKipp
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: ImbrieKipp
+> ### Title: Imbrie and Kipp foraminifera training set
+> ### Aliases: ImbrieKipp SumSST WinSST Salinity V12.122
+> ### Keywords: datasets
+>
+> ### ** Examples
+>
+> data(ImbrieKipp)
+> head(ImbrieKipp)
+ O.univ G.cglob G.ruber G.tenel G.saccu G.rubes G.pacL G.pacR G.bullo
+V14.61 0 0 0.00 0 0.00 0.00 98.97 0.90 0.00
+V17.196 0 0 0.00 0 0.00 0.00 98.13 0.94 0.47
+V18.110 0 0 0.00 0 0.00 0.00 96.29 1.71 1.00
+V16.227 0 0 0.00 0 0.00 0.00 94.33 4.82 0.85
+V14.47 0 0 0.11 0 0.11 0.11 68.50 2.71 10.95
+V23.22 0 0 0.00 0 0.00 0.00 55.69 16.62 19.90
+ G.falco G.calid G.aequi G.gluti G.duter G.infla G.trnL G.trnR G.crasf
+V14.61 0 0 0 0.00 0 0.00 0.00 0 0.00
+V17.196 0 0 0 0.47 0 0.00 0.00 0 0.00
+V18.110 0 0 0 0.00 0 0.57 0.00 0 0.00
+V16.227 0 0 0 0.00 0 0.00 0.00 0 0.00
+V14.47 0 0 0 1.95 0 14.20 0.11 0 0.43
+V23.22 0 0 0 2.29 0 1.57 0.00 0 0.00
+ G.scitu G.mentu P.obliq C.nitid S.dehis G.digit Other G.quin G.hirsu
+V14.61 0.00 0.00 0.00 0 0 0 0.13 0.00 0.00
+V17.196 0.00 0.00 0.00 0 0 0 0.00 0.00 0.00
+V18.110 0.00 0.00 0.00 0 0 0 0.43 0.00 0.00
+V16.227 0.00 0.00 0.00 0 0 0 0.00 0.00 0.00
+V14.47 0.11 0.11 0.11 0 0 0 0.18 0.32 0.00
+V23.22 0.00 0.00 0.00 0 0 0 0.65 3.14 0.13
+>
+> data(SumSST)
+> data(WinSST)
+> data(Salinity)
+>
+> plot(cbind(SumSST, WinSST, Salinity))
+>
+> data(V12.122)
+> head(V12.122)
+ O.univ G.cglob G.ruber G.tenel G.saccu G.rubes G.pacL G.pacR G.bullo
+0 0.01792 0.00489 0.43485 0.00814 0.25570 0.00651 0 0.00163 0.00000
+10 0.03203 0.00712 0.37722 0.00356 0.30961 0.00712 0 0.00356 0.00000
+20 0.02564 0.01709 0.47009 0.00855 0.20513 0.01709 0 0.01282 0.00427
+30 0.01124 0.00562 0.47190 0.01124 0.12360 0.02247 0 0.03933 0.00562
+40 0.00671 0.01007 0.43623 0.03020 0.15436 0.01007 0 0.00336 0.00671
+50 0.01149 0.00766 0.52873 0.00766 0.12261 0.00000 0 0.00383 0.02299
+ G.falco G.calid G.aequi G.gluti G.duter G.infla G.trnL G.trnR G.crasf
+0 0.00163 0.00326 0.03257 0.08958 0.04560 0.00163 0.00163 0.00000 0.00000
+10 0.00000 0.00000 0.02491 0.08185 0.05694 0.00000 0.00712 0.00356 0.00000
+20 0.00000 0.00855 0.00855 0.09402 0.05556 0.00000 0.00427 0.00855 0.00000
+30 0.00562 0.02247 0.05056 0.07865 0.06742 0.01124 0.00000 0.01685 0.00562
+40 0.00336 0.01678 0.08054 0.09396 0.03691 0.04698 0.00336 0.02349 0.01342
+50 0.00000 0.01916 0.06897 0.07663 0.04981 0.04215 0.00000 0.02682 0.00766
+ G.scitu G.mentu P.obliq C.nitid S.dehis G.digit Other G.quin G.hirsu
+0 0.00163 0.07492 0.00977 0.00651 0.00163 0 0.00000 0 0
+10 0.00000 0.05694 0.01423 0.00000 0.00356 0 0.01068 0 0
+20 0.00855 0.02991 0.00855 0.00855 0.00000 0 0.00427 0 0
+30 0.00562 0.01124 0.02247 0.00000 0.00000 0 0.01124 0 0
+40 0.00336 0.01007 0.00671 0.00000 0.00000 0 0.00336 0 0
+50 0.00000 0.00000 0.00000 0.00000 0.00000 0 0.00383 0 0
+ G.hexag G.cglom cfH.pel
+0 0 0 0
+10 0 0 0
+20 0 0 0
+30 0 0 0
+40 0 0 0
+50 0 0 0
+>
+>
+>
+> cleanEx()
+> nameEx("Pollen")
+> ### * Pollen
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: Pollen
+> ### Title: North American Modern Pollen Database
+> ### Aliases: Pollen Biome Climate Location
+> ### Keywords: datasets
+>
+> ### ** Examples
+>
+> data(Pollen)
+>
+> data(Climate)
+>
+> data(Biome)
+>
+> data(Location)
+>
+>
+>
+> cleanEx()
+> nameEx("RMSEP")
+> ### * RMSEP
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: RMSEP
+> ### Title: Root mean square error of prediction
+> ### Aliases: RMSEP RMSEP.default RMSEP.mat RMSEP.bootstrap.mat
+> ### RMSEP.bootstrap.wa
+> ### Keywords: methods utilities
+>
+> ### ** Examples
+>
+> ## Imbrie and Kipp example
+> ## load the example data
+> data(ImbrieKipp)
+> data(SumSST)
+> data(V12.122)
+>
+> ## merge training and test set on columns
+> dat <- join(ImbrieKipp, V12.122, verbose = TRUE)
+
+Summary:
+
+ Rows Cols
+Data set 1: 61 27
+Data set 2: 110 30
+Merged: 171 30
+
+>
+> ## extract the merged data sets and convert to proportions
+> ImbrieKipp <- dat[[1]] / 100
+> V12.122 <- dat[[2]] / 100
+>
+> ## fit the MAT model using the squared chord distance measure
+> (ik.mat <- mat(ImbrieKipp, SumSST, method = "chord"))
+
+ Modern Analogue Technique
+
+Call:
+mat(x = ImbrieKipp, y = SumSST, method = "chord")
+
+Percentiles of the dissimilarities for the training set:
+
+ 1% 2% 5% 10% 20%
+0.220 0.280 0.341 0.414 0.501
+
+Inferences based on the mean of k-closest analogues:
+
+ k RMSEP R2 Avg Bias Max Bias
+ 1 2.501 0.880 0.321 9.000
+ 2 1.875 0.931 0.284 6.000
+ 3 1.713 0.941 0.133 5.167
+ 4 1.796 0.935 0.177 5.125
+ 5 1.748 0.939 0.209 5.100
+ 6 1.716 0.943 0.284 5.667
+ 7 1.763 0.943 0.381 6.429
+ 8 1.831 0.941 0.390 6.625
+ 9 1.913 0.940 0.449 7.222
+ 10 2.040 0.935 0.577 7.500
+
+Inferences based on the weighted mean of k-closest analogues:
+
+ k RMSEP R2 Avg Bias Max Bias
+ 1 2.501 0.880 0.321 9.000
+ 2 1.894 0.929 0.263 6.183
+ 3 1.733 0.940 0.138 5.470
+ 4 1.773 0.937 0.173 5.384
+ 5 1.750 0.939 0.187 5.366
+ 6 1.709 0.942 0.218 5.493
+ 7 1.712 0.942 0.254 5.635
+ 8 1.758 0.940 0.253 5.693
+ 9 1.777 0.939 0.274 5.838
+ 10 1.857 0.935 0.362 5.927
+
+>
+> ## Leave-one-out RMSEP for the MAT model
+> RMSEP(ik.mat)
+[1] 1.733353
+>
+> ## bootstrap training set
+> (ik.boot <- bootstrap(ik.mat, n.boot = 100))
+
+ Bootstrap results for palaeoecological models
+
+Model type: MAT
+Weighted mean: FALSE
+Number of bootstrap cycles: 100
+
+Leave-one-out and bootstrap-derived error estimates:
+
+ k RMSEP S1 S2 r.squared avg.bias max.bias
+LOO 3 1.713 - - 0.9409 0.1328 5.167
+Bootstrap 5 1.953 0.9133 1.726 0.9707 0.3017 5.484
+
+>
+> ## extract the Birks et al (1990) RMSEP
+> RMSEP(ik.boot)
+[1] 1.952979
+>
+> ## Calculate the alternative formulation
+> RMSEP(ik.boot, type = "standard")
+[1] 1.726244
+>
+>
+>
+> cleanEx()
+> nameEx("Stratiplot")
+> ### * Stratiplot
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: Stratiplot
+> ### Title: Palaeoecological stratigraphic diagrams
+> ### Aliases: Stratiplot Stratiplot.default Stratiplot.formula
+> ### Keywords: hplot
+>
+> ### ** Examples
+>
+> data(V12.122)
+> Depths <- as.numeric(rownames(V12.122))
+>
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("h","l","g","smooth")))
+>
+> ## Order taxa by WA in depth --- ephasises change over time
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("h"), sort = "wa"))
+>
+> ## Using the default interface
+> spp.want <- c("O.univ","G.ruber","G.tenel","G.pacR")
+> (plt <- Stratiplot(V12.122[, spp.want], y = Depths,
++ type = c("poly", "g")))
+>
+> ## Adding zones to a Stratigraphic plot
+> ## Default labelling and draw zone legend
+> ## Here we choose 4 arbitrary Depths as the zone boundaries
+> set.seed(123)
+> Zones <-sample(Depths, 4)
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("poly","g"),
++ zones = Zones))
+>
+> ## As before, but supplying your own zone labels
+> zone.labs <- c("A","B","C","D","E")
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("poly","g"),
++ zones = Zones, zoneNames = zone.labs))
+>
+> ## Suppress the drawing of the zone legend
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("poly","g"),
++ zones = Zones, drawLegend = FALSE))
+>
+> ## Add zones and draw a legend, but do not label the zones
+> (plt <- Stratiplot(Depths ~ O.univ + G.ruber + G.tenel + G.pacR,
++ data = V12.122, type = c("poly","g"),
++ zones = Zones, zoneNames = ""))
+>
+>
+>
+> cleanEx()
+> nameEx("abernethy")
+> ### * abernethy
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: abernethy
+> ### Title: Abernethy Forest Pollen Sequence
+> ### Aliases: abernethy
+> ### Keywords: datasets
+>
+> ### ** Examples
+>
+> data(abernethy)
+> head(abernethy)
+ Betula Pinus sylvestris Ulmus Quercus Alnus glutinosa Corylus-Myrica Salix
+1 15.11 51.82 2.68 3.44 5.54 6.88 0.00
+2 21.00 59.48 0.93 1.86 0.93 7.81 0.93
+3 9.26 76.23 0.54 1.09 0.73 7.08 0.00
+4 20.70 74.84 0.80 0.48 0.00 0.96 0.16
+5 6.07 88.06 0.39 1.37 0.20 1.17 0.20
+6 11.32 81.57 1.15 1.34 0.19 2.30 0.58
+ Juniperus communis Calluna vulgaris Empetrum Gramineae Cyperaceae
+1 0 7.07 0.76 1.34 4.78
+2 0 3.35 0.74 0.56 2.04
+3 0 2.36 0.36 0.18 2.18
+4 0 0.64 0.00 0.32 0.64
+5 0 0.78 0.59 0.39 0.59
+6 0 0.38 0.00 0.38 0.58
+ Solidago-type Compositae Artemisia Caryophyllaceae Sagina Silene
+1 0 0 0 0 0 0
+2 0 0 0 0 0 0
+3 0 0 0 0 0 0
+4 0 0 0 0 0 0
+5 0 0 0 0 0 0
+6 0 0 0 0 0 0
+ Chenopodiaceae Epilobium-type Papilionaceae Anthyllis vulneraria
+1 0 0 0 0
+2 0 0 0 0
+3 0 0 0 0
+4 0 0 0 0
+5 0 0 0 0
+6 0 0 0 0
+ Astragalus alpinus Ononis-type Rosaceae Rubiaceae Ranunculaceae Thalictrum
+1 0 0 0.00 0 0 0
+2 0 0 0.19 0 0 0
+3 0 0 0.00 0 0 0
+4 0 0 0.00 0 0 0
+5 0 0 0.00 0 0 0
+6 0 0 0.19 0 0 0
+ Rumex acetosa-type Oxyria-type Parnassia palustris Saxifraga spp1
+1 0 0 0 0
+2 0 0 0 0
+3 0 0 0 0
+4 0 0 0 0
+5 0 0 0 0
+6 0 0 0 0
+ Saxifraga spp2 Sedum Urtica Veronica Depth Age
+1 0 0 0 0 300 5515
+2 0 0 0 0 305 5632
+3 0 0 0 0 310 5749
+4 0 0 0 0 315 5866
+5 0 0 0 0 320 5983
+6 0 0 0 0 325 6100
+>
+> (plt <- Stratiplot(Age ~ . - Depth,
++ data = chooseTaxa(abernethy, n.occ = 5,
++ max.abun = 10),
++ type = "poly"))
+>
+>
+>
+> cleanEx()
+> nameEx("analog")
+> ### * analog
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: analog
+> ### Title: Analogue matching
+> ### Aliases: analog analog.default print.analog
+> ### Keywords: multivariate
+>
+> ### ** Examples
+>
+> ## Imbrie and Kipp example
+> ## load the example data
+> data(ImbrieKipp)
+> data(SumSST)
+> data(V12.122)
+>
+> ## merge training and test set on columns
+> dat <- join(ImbrieKipp, V12.122, verbose = TRUE)
+
+Summary:
+
+ Rows Cols
+Data set 1: 61 27
+Data set 2: 110 30
+Merged: 171 30
+
+>
+> ## extract the merged data sets and convert to proportions
+> ImbrieKipp <- dat[[1]] / 100
+> V12.122 <- dat[[2]] / 100
+>
+> ## Imbrie and Kipp foraminfera sea-surface temperature
+>
+> ## analog matching between SWAP and RLGH core
+> ik.analog <- analog(ImbrieKipp, V12.122, method = "chord")
+> ik.analog
+
+ Analogue matching for fossil samples
+
+Call: analog(x = ImbrieKipp, y = V12.122, method = "chord")
+Dissimilarity: chord
+
+Percentiles of the dissimilarities for the training set:
+
+ 1% 2% 5% 10% 20%
+0.220 0.280 0.341 0.414 0.501
+
+ Minimum dissimilarity per sample
+
+Dissimilarity: chord
+
+ 0 10 20 30 40 50 60 70 80 90 100 110 120
+0.900 0.902 0.903 0.904 0.904 0.907 0.907 0.908 0.908 0.908 0.910 0.908 0.910
+ 130 140 150 160 170 180 190 200 210 220 230 240 250
+0.909 0.911 0.908 0.908 0.906 0.908 0.909 0.906 0.907 0.906 0.906 0.906 0.906
+ 260 270 280 290 300 310 320 330 340 350 360 370 380
+0.906 0.909 0.910 0.907 0.907 0.912 0.910 0.911 0.907 0.908 0.908 0.910 0.908
+ 390 400 410 420 430 440 450 460 470 480 490 500 510
+0.910 0.907 0.911 0.906 0.909 0.905 0.906 0.907 0.906 0.909 0.907 0.906 0.911
+ 520 530 540 550 560 570 580 590 600 610 620 630 640
+0.907 0.910 0.910 0.910 0.907 0.907 0.906 0.904 0.906 0.910 0.913 0.906 0.907
+ 650 660 670 680 690 700 710 720 730 740 750 760 770
+0.906 0.910 0.911 0.913 0.907 0.909 0.909 0.912 0.914 0.908 0.907 0.909 0.911
+ 780 790 800 810 820 830 840 850 860 870 880 890 900
+0.914 0.907 0.907 0.908 0.908 0.906 0.907 0.910 0.910 0.910 0.909 0.913 0.912
+ 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030
+0.908 0.909 0.909 0.907 0.910 0.909 0.908 0.907 0.911 0.908 0.913 0.910 0.910
+ 1040 1050 1060 1070 1080 1090
+0.912 0.906 0.910 0.910 0.908 0.907
+
+> summary(ik.analog)
+
+ Analogue matching for fossil samples
+
+Call: analog(x = ImbrieKipp, y = V12.122, method = "chord")
+Dissimilarity: chord
+k-closest: 10
+
+Percentiles of the dissimilarities for the training set:
+
+ 1% 2% 5% 10% 20%
+0.220 0.280 0.341 0.414 0.501
+
+k-closest analogues
+
+ k 0 10 20 30 40 50 60
+ 1 V12.122 V12.122 V12.122 V20.230 V22.172 V22.172 V22.172
+ 2 V14.5 V20.234 V20.234 V14.90 V20.167 V20.167 A153.154
+ 3 V20.234 V14.5 V16.21 V22.172 V19.216 V19.216 V10.89
+ 4 V18.21 V18.21 A180.76 V9.31 V10.89 V10.89 A179.13
+ 5 A180.76 A180.76 V14.5 V12.79 V22.204 V16.21 V16.21
+ 6 A180.72 A180.72 V15.164 V19.216 V16.21 V15.164 V20.167
+ 7 V15.164 V20.230 V20.230 A180.72 V20.234 V20.234 V22.219
+ 8 V16.21 V20.167 V14.90 V22.204 V20.230 A153.154 V19.308
+ 9 V20.167 V16.21 A180.72 A180.76 V14.90 V20.230 V12.18
+ 10 V20.230 V20.7 V20.167 V20.167 V15.164 A179.13 V19.216
+ 70 80 90 100 110 120 130
+ V10.89 V22.172 V19.216 V19.216 V9.31 V9.31 V9.31
+ V22.172 V19.216 V22.172 V22.204 V14.90 V19.216 V14.90
+ V16.21 V20.167 V22.204 V9.31 V20.230 V22.172 V19.216
+ V14.90 V16.21 V16.190 V22.172 V19.216 V14.90 V22.172
+ V19.216 V10.89 A153.154 V20.234 V22.204 V20.230 V20.230
+ A153.154 V15.164 V14.90 V14.90 V12.79 V20.167 V12.79
+ V20.167 A179.13 V9.31 V16.21 V22.172 A153.154 V22.204
+ V20.234 V20.230 V20.167 V10.89 A180.72 V22.204 A153.154
+ V22.204 V20.234 V12.18 A153.154 A180.76 V12.79 V16.190
+ V9.31 V14.90 V10.89 V15.164 V20.234 V3.128 A180.72
+ 140 150 160 170 180 190 200
+ V9.31 V10.89 V20.167 V20.167 A179.13 V19.216 V20.230
+ V22.172 V22.172 V20.230 V10.89 V20.167 V20.230 V14.90
+ V20.230 V20.167 V10.89 V20.230 V14.90 V14.90 V22.172
+ V20.234 V19.216 V19.216 V16.21 V20.230 V9.31 V9.31
+ V14.90 V16.21 V9.31 V22.172 V10.89 V22.172 V20.167
+ V20.167 A153.154 V14.90 A153.154 V16.21 V20.167 V20.234
+ V12.122 V14.90 A153.154 V14.90 V22.172 V12.79 V12.122
+ V19.216 V20.234 V16.190 A179.13 V9.31 V12.122 V12.79
+ A180.72 V15.164 V16.33 V9.31 V15.164 V20.234 V16.21
+ V16.21 V20.230 V22.172 V22.219 V19.216 V3.128 A180.72
+ 210 220 230 240 250 260 270
+ V14.90 V12.122 V12.122 V3.128 V14.90 V14.90 V14.90
+ V22.172 V14.90 V18.21 V20.167 V22.204 V20.167 V22.172
+ V20.167 V20.230 V14.90 V12.122 V22.172 V22.172 A180.72
+ V3.128 V18.21 V14.5 V14.90 A180.72 V3.128 V12.122
+ V12.122 V20.167 A180.76 V18.21 V12.79 V12.122 V12.79
+ V20.230 V20.234 V20.234 V22.172 V12.122 A180.72 V22.204
+ V20.234 V14.5 V3.128 A180.76 A180.76 V22.204 V3.128
+ A180.72 V22.172 A180.72 V15.164 V20.167 V12.79 A180.78
+ V15.164 A180.72 V20.167 A180.72 V10.89 A180.76 V18.21
+ A180.76 A180.76 V15.164 V14.5 V15.164 V20.230 V20.167
+ 280 290 300 310 320 330 340
+ V22.172 V12.122 V22.172 V19.216 V19.216 V22.172 V19.216
+ V14.90 V14.90 V20.167 V16.33 V9.31 V19.216 V10.89
+ V12.122 A180.72 V19.216 V14.90 V22.172 V14.90 V22.172
+ V22.204 V14.5 V15.164 V22.172 V20.230 V10.89 A153.154
+ V19.216 V20.167 V16.21 V9.31 V20.234 A153.154 V20.167
+ V15.164 V18.21 V10.89 V10.89 V20.167 V22.204 A179.13
+ V18.21 A180.76 V14.90 V20.167 V22.204 V9.31 V22.204
+ V12.79 V20.234 V20.234 V20.230 V14.90 A180.39 V19.308
+ V14.5 V3.128 V20.230 V16.190 V16.21 V16.190 V16.21
+ V20.234 V22.172 A179.13 V16.21 V10.89 V22.219 V22.219
+ 350 360 370 380 390 400 410
+ V20.230 V20.167 V19.216 V9.31 V19.216 V9.31 V3.128
+ V20.167 V22.172 V22.172 V12.122 V22.172 V14.90 V12.122
+ V9.31 V16.21 V20.167 V20.234 V3.128 V22.204 V20.167
+ V19.216 V10.89 V3.128 V14.90 V12.122 A180.76 V20.230
+ V14.90 V14.90 V22.204 V20.230 V14.90 V20.230 V14.90
+ V16.21 V20.234 V12.122 A180.76 V9.31 A180.72 V9.31
+ V22.172 V15.164 V20.234 V3.128 V20.234 V22.172 V22.172
+ V16.33 V12.122 V20.230 A180.72 V16.33 V12.79 V20.7
+ V10.89 A179.13 V15.164 V22.204 V20.167 V19.216 V20.234
+ V15.164 V20.230 V16.33 V20.167 V22.204 V20.234 V19.216
+ 420 430 440 450 460 470 480
+ V9.31 V12.122 V9.31 A180.76 V20.167 V20.167 V14.90
+ V20.234 V9.31 V14.90 V18.21 V3.128 V22.172 A180.76
+ V14.90 V14.90 V20.234 V20.234 V14.5 V20.234 V22.204
+ V20.230 V20.234 V20.230 A180.72 V18.21 V3.128 V22.172
+ V22.172 A180.76 V12.122 V12.122 A180.76 V14.90 A180.72
+ V12.122 V20.230 A180.72 V14.5 V20.234 V20.230 V12.79
+ V20.167 V3.128 V22.204 V20.230 V12.122 A180.76 V20.230
+ V15.164 A180.72 A180.76 V15.164 V20.230 A180.72 V20.234
+ V3.128 V20.167 V12.79 V3.128 A180.72 V22.204 V3.128
+ A180.76 V22.172 V19.216 V14.90 V15.164 V19.216 A180.78
+ 490 500 510 520 530 540 550
+ V20.234 V12.122 V9.31 V14.90 V20.167 V10.89 V22.204
+ V3.128 V20.234 V22.204 V22.204 V19.216 A153.154 V16.190
+ A180.76 A180.76 V14.90 V9.31 V22.172 V22.219 V19.216
+ A180.72 V20.230 V20.234 V3.128 V10.89 V19.216 V22.172
+ V12.122 V18.21 V20.230 V19.216 V16.21 V12.18 V14.90
+ V22.204 A180.72 V12.122 A180.76 V22.204 V16.190 V20.230
+ V18.21 V3.128 A180.72 A180.72 V12.122 V22.172 V12.79
+ V14.90 V14.5 V19.216 A180.78 V15.164 V16.21 V9.31
+ V14.5 V20.7 A180.76 V12.79 V20.234 V20.167 A180.72
+ V20.230 V20.167 V22.172 V22.172 V20.230 V22.204 V10.89
+ 560 570 580 590 600 610 620
+ V14.90 V20.230 V22.204 V20.167 V14.90 V20.167 V22.204
+ V20.230 V9.31 V12.79 V22.172 A180.72 V14.90 V20.167
+ A180.76 A180.78 V14.90 V15.164 A180.76 V22.204 V19.216
+ V9.31 V14.90 A180.72 V16.21 V22.204 V12.122 V12.122
+ A180.72 A180.72 V22.172 V10.89 V12.122 V22.172 V22.172
+ A180.78 V22.204 V20.230 V19.216 V9.31 V20.230 V3.128
+ V3.128 A180.76 A180.76 V20.230 V20.234 V19.216 V20.234
+ V12.79 V3.128 V9.31 V20.234 V22.172 V3.128 V14.90
+ V22.172 V12.79 V19.216 V3.128 V12.79 V12.79 V20.230
+ V22.204 V20.234 V20.234 V14.90 V20.230 A180.72 V16.33
+ 630 640 650 660 670 680 690
+ V14.90 V12.122 V22.204 V14.90 V9.31 V3.128 A179.13
+ V9.31 V14.90 V12.122 V19.216 V20.234 V14.90 V16.21
+ A180.76 V20.234 V20.234 V22.204 V22.204 V22.172 V10.89
+ A180.72 V20.167 V14.90 V20.167 V14.90 V20.167 V15.164
+ V12.122 V14.5 V22.172 V22.172 A180.72 V19.216 V20.167
+ V22.204 V22.204 A180.72 V9.31 V22.172 V9.31 V22.172
+ V20.230 A180.72 V20.167 V3.128 V20.230 V22.204 V22.204
+ V20.234 V3.128 V14.5 V16.33 V12.122 V16.190 V22.219
+ V22.172 V18.21 A180.76 A180.72 V20.167 V20.230 V12.18
+ V20.167 V20.230 V15.164 V10.89 V15.164 A180.72 V19.308
+ 700 710 720 730 740 750 760
+ V12.122 V20.167 V22.204 V14.90 V14.90 V22.204 V20.234
+ V22.204 V3.128 V22.172 V3.128 V22.204 V20.234 V22.204
+ V20.167 V16.21 V9.31 V9.31 A180.72 V14.90 V14.90
+ V20.234 V15.164 V20.230 V22.172 V12.122 A180.72 A180.76
+ V22.172 V10.89 V14.90 V22.204 V22.172 V12.79 A180.72
+ V19.216 V20.234 V3.128 V20.230 V20.234 V22.172 V3.128
+ V16.21 V12.122 V10.89 V20.167 V20.230 V20.230 V20.167
+ V10.89 V20.230 V19.216 V12.122 V12.79 V12.122 V12.122
+ V15.164 V22.172 V20.167 V19.216 V20.167 V9.31 V20.230
+ A180.72 V22.204 A180.72 V20.234 V3.128 A180.76 V9.31
+ 770 780 790 800 810 820 830
+ V3.128 V3.128 V18.21 V12.122 V12.122 V14.90 V19.216
+ V20.167 V18.21 V12.122 V18.21 V14.90 V22.204 V22.172
+ V15.164 A180.78 V14.5 V14.5 V22.172 V19.216 V20.167
+ V20.230 V14.90 V20.167 V20.234 V22.204 V12.79 V16.33
+ V14.90 V12.122 V14.90 V20.167 V15.164 V22.172 V22.204
+ V16.21 V22.172 A180.72 V15.164 V20.234 V20.230 V14.90
+ V22.204 V20.167 V3.128 A180.72 V12.79 A180.72 V12.122
+ A180.76 A180.72 A180.76 V14.90 V14.5 V20.167 V16.190
+ V9.31 V20.230 V20.234 V12.79 V18.21 V10.89 V12.79
+ V12.122 V14.5 V15.164 V16.21 A180.72 A180.76 V10.89
+ 840 850 860 870 880 890 900
+ V19.216 V22.204 V22.204 V19.216 V19.216 V19.216 V19.216
+ V16.33 V20.234 V14.90 V22.172 V9.31 V19.222 V9.31
+ V22.204 V19.216 V22.172 V20.167 V22.172 V16.190 V12.79
+ V20.167 V9.31 V20.167 V22.204 V20.230 V10.98 V22.204
+ V20.234 V14.90 V19.216 V15.164 V12.122 V22.204 V14.90
+ V10.89 V12.122 V10.89 V14.90 V14.90 A180.39 V22.172
+ V22.172 V20.167 A179.13 V3.128 V20.234 V14.90 V20.230
+ A180.72 V20.230 V15.164 V10.89 V12.79 V22.172 V16.190
+ V9.31 A180.76 V12.18 V20.230 V20.167 V12.79 A180.39
+ V14.90 A180.72 V9.31 V16.21 A180.72 V9.31 V19.222
+ 910 920 930 940 950 960 970
+ V19.216 V19.216 V9.31 V19.216 V20.167 V20.230 V9.31
+ V22.204 V9.31 V20.230 V20.230 V14.90 V12.122 V20.234
+ V16.190 V16.190 V19.216 V9.31 V16.21 V20.234 V20.230
+ V22.172 V14.90 V22.204 V20.167 V20.230 V12.79 V22.172
+ V19.222 V22.204 V22.172 V14.90 V12.122 V22.172 A180.76
+ V12.79 V22.172 V14.90 V22.172 V9.31 V16.21 V19.216
+ A180.39 V20.230 V20.234 V12.79 V20.234 V19.216 V16.21
+ V14.90 A180.39 V20.167 V16.21 V15.164 V9.31 V20.167
+ A153.154 V10.98 V12.79 A180.72 V22.172 V14.90 A180.72
+ V9.31 V12.79 A180.72 V20.234 A179.13 A180.72 V14.90
+ 980 990 1000 1010 1020 1030 1040
+ V9.31 V9.31 V19.216 V14.90 V19.216 V14.90 V9.31
+ V20.230 V20.234 V14.90 V9.31 V16.190 V19.216 V14.90
+ V14.90 V20.230 V22.172 V20.230 V22.204 V22.172 V22.204
+ V20.234 V14.90 V9.31 V22.172 V22.172 V22.204 V19.216
+ A180.76 V22.172 V20.230 V12.79 V19.222 V9.31 V20.234
+ V22.172 V22.204 V12.79 A180.72 V14.90 V12.79 V12.79
+ A180.72 V12.79 V20.167 V19.216 A180.39 V16.190 V22.172
+ V12.79 V12.122 A180.72 V22.204 V12.66 A180.72 A180.72
+ V19.216 V19.216 V22.204 A180.39 V10.98 V20.234 A180.78
+ V12.122 A180.72 V16.21 V12.122 V12.79 V20.230 A180.76
+ 1050 1060 1070 1080 1090
+ V19.216 V22.204 V16.190 V19.216 V22.172
+ V22.204 V19.216 V19.216 V12.122 V22.204
+ V14.90 V14.90 V22.172 V22.172 V14.90
+ V22.172 V12.122 V22.204 V14.90 V19.216
+ V12.79 V22.172 V14.90 V22.204 V10.89
+ V9.31 V12.79 V12.18 V20.167 V20.167
+ V12.122 A180.72 V10.89 V12.79 V12.79
+ A180.72 V9.31 V20.167 A180.72 V20.234
+ V16.33 V20.234 V22.219 V20.230 V9.31
+ V16.190 A180.78 V9.31 V20.234 A180.72
+
+Dissimilarities for k-closest analogues
+
+ k 0 10 20 30 40 50 60 70 80 90
+ 1 0.900 0.902 0.903 0.904 0.904 0.907 0.907 0.908 0.908 0.908
+ 2 0.903 0.904 0.904 0.904 0.905 0.908 0.908 0.909 0.908 0.910
+ 3 0.903 0.904 0.906 0.904 0.906 0.908 0.909 0.910 0.909 0.913
+ 4 0.903 0.906 0.906 0.905 0.906 0.909 0.910 0.910 0.909 0.913
+ 5 0.905 0.906 0.906 0.905 0.908 0.909 0.910 0.910 0.909 0.914
+ 6 0.905 0.907 0.906 0.905 0.908 0.909 0.910 0.911 0.910 0.914
+ 7 0.907 0.907 0.907 0.906 0.908 0.911 0.910 0.911 0.910 0.914
+ 8 0.907 0.908 0.907 0.906 0.908 0.911 0.911 0.911 0.910 0.914
+ 9 0.907 0.909 0.907 0.906 0.908 0.911 0.911 0.911 0.911 0.915
+ 10 0.908 0.910 0.907 0.906 0.908 0.912 0.911 0.911 0.911 0.915
+ 100 110 120 130 140 150 160 170 180 190 200
+ 0.910 0.908 0.910 0.909 0.911 0.908 0.908 0.906 0.908 0.909 0.906
+ 0.911 0.910 0.910 0.909 0.911 0.909 0.910 0.907 0.910 0.910 0.908
+ 0.911 0.912 0.911 0.910 0.912 0.909 0.910 0.907 0.910 0.910 0.908
+ 0.912 0.913 0.911 0.911 0.912 0.910 0.910 0.907 0.911 0.910 0.908
+ 0.913 0.914 0.912 0.911 0.914 0.910 0.910 0.907 0.911 0.912 0.908
+ 0.913 0.914 0.914 0.913 0.914 0.910 0.910 0.908 0.911 0.912 0.909
+ 0.913 0.914 0.914 0.913 0.915 0.911 0.910 0.908 0.911 0.912 0.909
+ 0.913 0.915 0.914 0.914 0.915 0.911 0.910 0.908 0.912 0.913 0.910
+ 0.914 0.915 0.915 0.914 0.915 0.911 0.911 0.909 0.913 0.914 0.910
+ 0.914 0.915 0.915 0.914 0.915 0.912 0.911 0.910 0.914 0.914 0.910
+ 210 220 230 240 250 260 270 280 290 300 310
+ 0.907 0.906 0.906 0.906 0.906 0.906 0.909 0.910 0.907 0.907 0.912
+ 0.908 0.909 0.908 0.910 0.908 0.908 0.910 0.911 0.908 0.908 0.913
+ 0.908 0.909 0.908 0.911 0.909 0.908 0.911 0.911 0.909 0.908 0.913
+ 0.908 0.909 0.908 0.911 0.910 0.908 0.912 0.911 0.909 0.909 0.914
+ 0.909 0.909 0.909 0.911 0.910 0.908 0.912 0.912 0.910 0.909 0.915
+ 0.910 0.909 0.909 0.912 0.911 0.909 0.912 0.912 0.910 0.909 0.916
+ 0.910 0.909 0.910 0.912 0.911 0.909 0.913 0.912 0.910 0.909 0.916
+ 0.910 0.910 0.910 0.913 0.911 0.910 0.913 0.912 0.911 0.910 0.916
+ 0.910 0.910 0.910 0.913 0.911 0.910 0.913 0.913 0.912 0.910 0.916
+ 0.910 0.910 0.911 0.913 0.911 0.910 0.914 0.914 0.912 0.910 0.917
+ 320 330 340 350 360 370 380 390 400 410 420
+ 0.910 0.911 0.907 0.908 0.908 0.910 0.908 0.910 0.907 0.911 0.906
+ 0.911 0.911 0.908 0.909 0.909 0.910 0.909 0.910 0.907 0.911 0.907
+ 0.911 0.911 0.908 0.909 0.909 0.911 0.909 0.912 0.909 0.912 0.907
+ 0.912 0.911 0.909 0.910 0.909 0.912 0.909 0.912 0.910 0.912 0.907
+ 0.912 0.912 0.909 0.910 0.909 0.912 0.910 0.912 0.910 0.912 0.908
+ 0.912 0.912 0.910 0.910 0.909 0.912 0.911 0.912 0.910 0.912 0.908
+ 0.913 0.913 0.911 0.911 0.909 0.913 0.911 0.913 0.910 0.913 0.909
+ 0.913 0.913 0.911 0.911 0.909 0.913 0.911 0.913 0.911 0.913 0.909
+ 0.913 0.913 0.911 0.912 0.910 0.914 0.911 0.913 0.911 0.913 0.909
+ 0.913 0.913 0.911 0.912 0.910 0.914 0.911 0.913 0.911 0.914 0.909
+ 430 440 450 460 470 480 490 500 510 520 530
+ 0.909 0.905 0.906 0.907 0.906 0.909 0.907 0.906 0.911 0.907 0.910
+ 0.911 0.908 0.907 0.907 0.906 0.909 0.907 0.907 0.912 0.910 0.911
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/analogue -r 221
More information about the Analogue-commits
mailing list