[Analogue-commits] r331 - pkg/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed May 22 16:53:45 CEST 2013
Author: gsimpson
Date: 2013-05-22 16:53:45 +0200 (Wed, 22 May 2013)
New Revision: 331
Modified:
pkg/tests/Examples/analogue-Ex.Rout.save
Log:
update examples to latest current dev release checks
Modified: pkg/tests/Examples/analogue-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/analogue-Ex.Rout.save 2013-05-22 14:53:00 UTC (rev 330)
+++ pkg/tests/Examples/analogue-Ex.Rout.save 2013-05-22 14:53:45 UTC (rev 331)
@@ -1,8 +1,7 @@
-R version 2.15.3 RC (2013-02-25 r62062) -- "Security Blanket"
+R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
-ISBN 3-900051-07-0
-Platform: x86_64-unknown-linux-gnu (64-bit)
+Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
@@ -24,12 +23,12 @@
> library('analogue')
Loading required package: vegan
Loading required package: permute
-This is vegan 2.0-6
+This is vegan 2.0-7
Loading required package: princurve
Loading required package: lattice
-This is analogue 0.11-2
+This is analogue 0.11-3
>
-> assign(".oldSearch", search(), pos = 'CheckExEnv')
+> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("ImbrieKipp")
> ### * ImbrieKipp
@@ -408,7 +407,7 @@
>
> ### Name: analog
> ### Title: Analogue matching
-> ### Aliases: analog analog.default print.analog
+> ### Aliases: analog analog.default analog.distance print.analog
> ### Keywords: multivariate
>
> ### ** Examples
@@ -790,9 +789,49 @@
0.911
>
+> ## Can take pre-computed dissimilarity objects
+> d1 <- distance(ImbrieKipp, V12.122)
+> d2 <- distance(ImbrieKipp)
+> ik <- analog(d1, d2, keep.train = TRUE)
+> ik
+
+ Analogue matching for fossil samples
+
+Call: analog(x = d1, train = d2, keep.train = TRUE)
+Dissimilarity: euclidean
+
+Percentiles of the dissimilarities for the training set:
+
+ 1% 2% 5% 10% 20%
+0.0669 0.0956 0.1304 0.1739 0.2341
+
+ Minimum dissimilarity per sample
+
+Dissimilarity: euclidean
+
+ 0 10 20 30 40 50 60 70 80 90 100 110 120
+0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296
+ 130 140 150 160 170 180 190 200 210 220 230 240 250
+0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.297 0.296 0.297 0.296
+ 260 270 280 290 300 310 320 330 340 350 360 370 380
+0.296 0.297 0.296 0.297 0.296 0.297 0.296 0.296 0.296 0.296 0.296 0.296 0.296
+ 390 400 410 420 430 440 450 460 470 480 490 500 510
+0.296 0.296 0.296 0.296 0.296 0.296 0.297 0.296 0.296 0.296 0.297 0.296 0.297
+ 520 530 540 550 560 570 580 590 600 610 620 630 640
+0.296 0.296 0.295 0.296 0.296 0.296 0.296 0.296 0.296 0.297 0.297 0.296 0.297
+ 650 660 670 680 690 700 710 720 730 740 750 760 770
+0.296 0.296 0.296 0.297 0.296 0.296 0.296 0.296 0.297 0.296 0.297 0.296 0.296
+ 780 790 800 810 820 830 840 850 860 870 880 890 900
+0.297 0.297 0.296 0.296 0.297 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296
+ 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030
+0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.296 0.297 0.296 0.296
+ 1040 1050 1060 1070 1080 1090
+0.296 0.296 0.297 0.296 0.296 0.296
+
>
>
>
+>
> cleanEx()
> nameEx("bayesF")
> ### * bayesF
@@ -1223,7 +1262,7 @@
>
> ### Name: chooseTaxa
> ### Title: Select taxa (variables) on basis of maximum abundance attained
-> ### and number of occurrences
+> ### and number of occurrences.
> ### Aliases: chooseTaxa chooseTaxa.default
> ### Keywords: methods multivariate
>
@@ -1236,8 +1275,17 @@
> dim(IK2)
[1] 61 27
>
+> ## return a logical vector to select species/columns
+> chooseTaxa(ImbrieKipp, n.occ = 5, value = FALSE)
+ O.univ G.cglob G.ruber G.tenel G.saccu G.rubes G.pacL G.pacR G.bullo G.falco
+ TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
+G.calid G.aequi G.gluti G.duter G.infla G.trnL G.trnR G.crasf G.scitu G.mentu
+ TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
+P.obliq C.nitid S.dehis G.digit Other G.quin G.hirsu
+ TRUE TRUE TRUE TRUE TRUE TRUE TRUE
>
>
+>
> cleanEx()
> nameEx("cma")
> ### * cma
@@ -2297,6 +2345,7 @@
..$ : chr [1:20] "A" "B" "C" "D" ...
..$ : chr [1:10] "a" "b" "c" "d" ...
- attr(*, "method")= chr "euclidean"
+ - attr(*, "type")= chr "asymmetric"
- attr(*, "class")= chr [1:2] "distance" "matrix"
>
> ## calculate Gower's general coefficient for mixed data
@@ -2315,6 +2364,8 @@
[1] 0.3380952
attr(,"method")
[1] "mixed"
+attr(,"type")
+[1] "asymmetric"
attr(,"class")
[1] "distance" "matrix"
>
@@ -2333,7 +2384,7 @@
>
> ### Name: distance3
> ### Title: Flexibly calculate dissimilarity or distance measures
-> ### Aliases: distance3 distance3.default
+> ### Aliases: distance3 distanceX distance3.default
> ### Keywords: multivariate methods
>
> ### ** Examples
@@ -2364,6 +2415,8 @@
> ## Using distance on an object of class join
> #dists <- distance3(join(train, fossil))
> #str(dists)
+> dists <- distance(join(train, fossil))
+> ##distsX <- distanceX(join(train, fossil))
>
> ## calculate Gower's general coefficient for mixed data
> ## first, make a couple of variables factors
@@ -2382,12 +2435,16 @@
[1,] 0.3380952
attr(,"method")
[1] "mixed"
+attr(,"type")
+[1] "asymmetric"
attr(,"class")
[1] "distance" "matrix"
> distance(x1, x2, method = "mixed", R = Rj)
[1] 0.3380952
attr(,"method")
[1] "mixed"
+attr(,"type")
+[1] "asymmetric"
attr(,"class")
[1] "distance" "matrix"
>
@@ -5044,7 +5101,7 @@
> ## the model performance statistics
> performance(mod)
RMSE R2 Avg.Bias Max.Bias
- 2.019e+00 9.173e-01 2.228e-14 -3.815e+00
+ 2.019e+00 9.173e-01 2.854e-15 -3.815e+00
>
>
>
@@ -6008,7 +6065,7 @@
+ vary = FALSE, penalty = 1.4)
--------------------------------------------------------------------------------
Initial curve: d.sq: 103233.4502
-Iteration 1: d.sq: 4853.7912
+Iteration 1: d.sq: 4853.7911
Iteration 2: d.sq: 5013.4971
Iteration 3: d.sq: 5109.9732
Iteration 4: d.sq: 5135.6541
@@ -6205,6 +6262,34 @@
>
>
> cleanEx()
+> nameEx("rankDC")
+> ### * rankDC
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: rankDC
+> ### Title: Rank correlation between environmental and species
+> ### dissimilarities.
+> ### Aliases: rankDC print.rankDC plot.rankDC dotplot.rankDC
+> ### Keywords: hplot methods utilities multivariate
+>
+> ### ** Examples
+>
+> data(swappH)
+> data(swapdiat)
+>
+> rc <- rankDC(swappH, swapdiat, dc = c("chord","euclidean","gower"))
+>
+> ## base plot uses dotchart()
+> plot(rc)
+>
+> ## lattice version of the base plot
+> dotplot(rc)
+>
+>
+>
+>
+> cleanEx()
> nameEx("reconPlot")
> ### * reconPlot
>
@@ -6424,6 +6509,62 @@
>
>
> cleanEx()
+> nameEx("scores.prcurve")
+> ### * scores.prcurve
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: scores.prcurve
+> ### Title: 'scores' method for principal curve objects of class
+> ### '"prcurve"'.
+> ### Aliases: scores.prcurve
+> ### Keywords: methods
+>
+> ### ** Examples
+>
+> ## Load the Abernethy Forest data set
+> data(abernethy)
+>
+> ## Remove the Depth and Age variables
+> abernethy2 <- abernethy[, -(37:38)]
+>
+> ## Fit the principal curve using varying complexity of smoothers
+> ## for each species
+> aber.pc <- prcurve(abernethy2, method = "ca", trace = TRUE,
++ vary = TRUE, penalty = 1.4)
+--------------------------------------------------------------------------------
+Initial curve: d.sq: 103233.4502
+Iteration 1: d.sq: 4283.4308
+Iteration 2: d.sq: 4312.2976
+Iteration 3: d.sq: 4340.6911
+Iteration 4: d.sq: 4355.3876
+Iteration 5: d.sq: 4366.4975
+Iteration 6: d.sq: 4369.9444
+--------------------------------------------------------------------------------
+PC Converged in 6 iterations.
+--------------------------------------------------------------------------------
+>
+> ## Extract position on the curve
+> pos <- scores(aber.pc, display = "curve")
+> head(pos)
+ PrC
+1 251.3134
+2 253.7651
+3 273.9467
+4 267.3053
+5 286.3480
+6 277.9563
+>
+> ## Extract the coordinates of the curve
+> coord <- scores(aber.pc, display = "dimensions")
+> dim(coord)
+[1] 49 36
+> all.equal(dim(coord), dim(abernethy2))
+[1] TRUE
+>
+>
+>
+> cleanEx()
> nameEx("screeplot")
> ### * screeplot
>
@@ -6565,6 +6706,46 @@
>
>
> cleanEx()
+> nameEx("sppResponse.prcurve")
+> ### * sppResponse.prcurve
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: sppResponse
+> ### Title: Species responses along gradients.
+> ### Aliases: sppResponse sppResponse.prcurve
+> ### Keywords: methods
+>
+> ### ** Examples
+>
+> ## Load the Abernethy Forest data set
+> data(abernethy)
+>
+> ## Remove the Depth and Age variables
+> abernethy2 <- abernethy[, -(37:38)]
+>
+> ## Fit the principal curve using varying complexity of smoothers
+> ## for each species
+> aber.pc <- prcurve(abernethy2, method = "ca", trace = TRUE,
++ vary = TRUE, penalty = 1.4)
+--------------------------------------------------------------------------------
+Initial curve: d.sq: 103233.4502
+Iteration 1: d.sq: 4283.4308
+Iteration 2: d.sq: 4312.2976
+Iteration 3: d.sq: 4340.6911
+Iteration 4: d.sq: 4355.3876
+Iteration 5: d.sq: 4366.4975
+Iteration 6: d.sq: 4369.9444
+--------------------------------------------------------------------------------
+PC Converged in 6 iterations.
+--------------------------------------------------------------------------------
+>
+> ## Extract the fitted species response curves
+> resp <- sppResponse(aber.pc)
+>
+>
+>
+> cleanEx()
> nameEx("stdError")
> ### * stdError
>
@@ -7416,8 +7597,9 @@
>
> ### * <FOOTER>
> ###
-> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 20.547 0.247 21.325 0 0
+> options(digits = 7L)
+> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
+Time elapsed: 19.461 0.18 19.653 0 0
> grDevices::dev.off()
null device
1
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