[Analogue-commits] r346 - pkg/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Jul 20 23:08:47 CEST 2013
Author: gsimpson
Date: 2013-07-20 23:08:46 +0200 (Sat, 20 Jul 2013)
New Revision: 346
Modified:
pkg/tests/Examples/analogue-Ex.Rout.save
Log:
updated test checks
Modified: pkg/tests/Examples/analogue-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/analogue-Ex.Rout.save 2013-07-20 21:00:38 UTC (rev 345)
+++ pkg/tests/Examples/analogue-Ex.Rout.save 2013-07-20 21:08:46 UTC (rev 346)
@@ -1231,14 +1231,14 @@
> data(SumSST)
>
> ## default plot
-> caterpillarPlot(ImbrieKipp, SumSST)
+> caterpillar(ImbrieKipp, SumSST)
>
> ## customisation
> opttol <-
-+ caterpillarPlot(ImbrieKipp, SumSST, col = "red2",
-+ bg = "yellow", lcol = "blue",
-+ xlab = expression(Summer ~ Sea ~ Surface ~
-+ Temperature~(degree*C)))
++ caterpillar(ImbrieKipp, SumSST, col = "red2",
++ bg = "yellow", lcol = "blue",
++ xlab = expression(Summer ~ Sea ~ Surface ~
++ Temperature~(degree*C)))
>
> ## invisibly returns the optima and tolerances
> head(opttol)
@@ -1250,11 +1250,8 @@
G.saccu 26.18001 1.979651
G.mentu 26.13778 2.386584
>
-> ## The short form name may be easier on the typing fingers
-> caterpillar(ImbrieKipp, SumSST)
>
>
->
> cleanEx()
> nameEx("chooseTaxa")
> ### * chooseTaxa
@@ -3808,7 +3805,7 @@
> plot(ik.mat)
> par(mfrow = c(1,1))
>
-> ## reconstruct for the RLGH core data
+> ## reconstruct for the V12.122 core data
> coreV12.mat <- predict(ik.mat, V12.122, k = 3)
> coreV12.mat
@@ -3988,376 +3985,9 @@
10 1.857 0.935 0.362 5.927
>
-> ## model summary
-> summary(ik.mat2)
-
- Modern Analogue Technique
-
-Call:
-mat(x = ImbrieKipp, y = SumSST, method = "chord", kmax = 20)
-
-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 2.50 0.88 0.32 9.00
- 2 1.87 0.93 0.28 6.00
- 3 1.71 0.94 0.13 5.17
- 4 1.80 0.94 0.18 5.12
- 5 1.75 0.94 0.21 5.10
- 6 1.72 0.94 0.28 5.67
- 7 1.76 0.94 0.38 6.43
- 8 1.83 0.94 0.39 6.62
- 9 1.91 0.94 0.45 7.22
- 10 2.04 0.93 0.58 7.50
-
-Inferences based on the weighted mean of k-closest analogues:
-
- k RMSEP R2 Avg Bias Max Bias
- 1 2.50 0.88 0.32 9.00
- 2 1.89 0.93 0.26 6.18
- 3 1.73 0.94 0.14 5.47
- 4 1.77 0.94 0.17 5.38
- 5 1.75 0.94 0.19 5.37
- 6 1.71 0.94 0.22 5.49
- 7 1.71 0.94 0.25 5.63
- 8 1.76 0.94 0.25 5.69
- 9 1.78 0.94 0.27 5.84
- 10 1.86 0.94 0.36 5.93
-
-Results for training set
-
- * (W.)Est and (W.)Resi are based on k=10-closest analogues
- * minDC is the minimum distance to another sample in the training set
- * min(W.)Resi is the minimum residual for a k-closest model,
- where k = 1,...,10. Column k(.W) displays which k has minResi
-
- Obs Est Resi W.Est W.Resi minDC minResi k minW.Resi
-V14.61 2.0 9.50 7.50 7.93 5.9273 0.104 5.1000 5 5.37e+00
-V17.196 5.0 9.20 4.20 6.86 1.8572 0.130 0.1667 3 4.61e-01
-V18.110 5.5 9.15 3.65 7.13 1.6290 0.134 0.5000 1 4.84e-01
-V16.227 7.0 9.00 2.00 7.16 0.1554 0.134 0.1667 3 2.62e-02
-V14.47 7.0 9.00 2.00 8.66 1.6594 0.452 0.0000 4 3.63e-02
-V23.22 10.5 8.65 -1.85 8.68 -1.8229 0.467 0.5000 3 7.43e-01
-V2.12 11.0 8.60 -2.40 5.70 -5.2971 0.119 2.4000 10 5.30e+00
-V23.29 10.0 13.65 3.65 13.33 3.3347 0.467 1.2500 2 1.41e+00
-V12.43 13.0 13.25 0.25 13.31 0.3135 0.490 0.0000 5 1.71e-01
-R9.7 12.0 14.85 2.85 14.29 2.2876 0.432 1.0000 2 4.65e-01
-A157.3 14.0 16.30 2.30 16.04 2.0418 0.407 0.5000 2 3.14e-01
-V23.81 14.5 15.45 0.95 15.40 0.8996 0.299 0.1667 3 8.08e-02
-V23.82 15.0 16.20 1.20 15.84 0.8369 0.299 0.1429 7 1.40e-01
-V12.53 14.5 17.80 3.30 17.76 3.2607 0.442 2.3571 7 2.55e+00
-V23.83 16.0 15.30 -0.70 15.02 -0.9814 0.295 0.7000 10 9.81e-01
-V12.56 18.0 20.90 2.90 20.78 2.7832 0.380 1.0000 2 1.03e+00
-A152.84 20.0 21.60 1.60 21.64 1.6374 0.361 0.1429 7 5.77e-01
-V16.50 18.0 20.50 2.50 20.28 2.2759 0.429 0.0000 1 0.00e+00
-V22.122 19.0 17.95 -1.05 17.58 -1.4223 0.429 1.0500 10 1.42e+00
-V16.41 18.5 23.90 5.40 23.64 5.1402 0.380 2.5000 1 2.50e+00
-V4.32 21.5 23.60 2.10 23.62 2.1185 0.333 1.6250 8 1.73e+00
-V12.66 21.0 21.10 0.10 21.12 0.1228 0.421 0.0000 1 0.00e+00
-V19.245 21.0 23.20 2.20 22.72 1.7177 0.331 0.1667 3 4.41e-01
-V4.8 24.0 23.35 -0.65 23.38 -0.6203 0.280 0.0000 1 0.00e+00
-A180.15 24.0 22.95 -1.05 22.99 -1.0083 0.292 0.0000 1 0.00e+00
-V18.34 23.0 24.35 1.35 24.49 1.4904 0.411 1.3500 10 1.49e+00
-V20.213 24.0 24.57 0.57 24.56 0.5565 0.326 0.0833 6 1.38e-01
-V19.222 23.0 23.00 0.00 23.06 0.0580 0.384 0.0000 10 3.83e-02
-A180.39 23.0 24.25 1.25 24.24 1.2366 0.347 0.4167 6 5.12e-01
-V16.189 24.0 25.89 1.89 25.85 1.8495 0.399 1.0000 1 1.00e+00
-V12.18 25.0 25.39 0.39 25.48 0.4751 0.289 0.3000 9 4.19e-01
-V7.67 26.0 23.45 -2.55 23.76 -2.2436 0.308 0.0000 1 0.00e+00
-V17.165 26.0 24.77 -1.23 24.80 -1.2021 0.308 0.0000 1 0.00e+00
-V19.310 26.0 23.75 -2.25 23.97 -2.0257 0.296 0.0000 1 0.00e+00
-V16.190 25.0 25.32 0.32 25.34 0.3424 0.324 0.0000 2 3.98e-02
-A153.154 26.0 25.67 -0.33 25.73 -0.2694 0.222 0.1000 2 1.11e-01
-V19.308 26.0 25.99 -0.01 25.98 -0.0223 0.222 0.0100 10 2.23e-02
-V22.172 24.5 26.72 2.22 26.71 2.2110 0.307 1.5000 1 1.50e+00
-V10.98 27.0 24.82 -2.18 24.77 -2.2349 0.330 2.0000 1 2.00e+00
-V22.219 26.2 25.65 -0.55 25.63 -0.5709 0.189 0.2000 1 2.00e-01
-V16.33 25.0 26.37 1.37 26.37 1.3703 0.493 0.7333 3 7.48e-01
-V22.204 26.5 26.80 0.30 26.74 0.2362 0.325 0.0000 6 3.12e-02
-V20.167 26.2 26.90 0.70 26.87 0.6732 0.257 0.0333 3 1.17e-02
-V10.89 26.0 26.04 0.04 26.12 0.1184 0.308 0.0400 10 1.18e-01
-V12.79 26.0 26.90 0.90 26.88 0.8813 0.249 0.3750 4 5.16e-01
-V19.216 27.0 25.27 -1.73 25.32 -1.6776 0.363 0.9000 7 9.65e-01
-V14.90 27.0 26.95 -0.05 26.89 -0.1113 0.249 0.0000 4 2.56e-02
-A180.72 27.5 26.75 -0.75 26.74 -0.7623 0.185 0.4286 7 5.18e-01
-V16.21 27.0 26.87 -0.13 26.85 -0.1453 0.247 0.0000 1 0.00e+00
-A180.76 27.0 27.15 0.15 27.16 0.1582 0.233 0.0000 6 3.86e-02
-V15.164 27.0 26.82 -0.18 26.79 -0.2081 0.257 0.0000 1 0.00e+00
-A180.78 27.0 27.20 0.20 27.19 0.1940 0.405 0.0000 5 1.03e-04
-V14.5 27.0 27.02 0.02 27.07 0.0730 0.219 0.0000 1 3.55e-15
-V3.128 29.0 26.72 -2.28 26.69 -2.3050 0.366 2.1500 2 2.17e+00
-A179.13 28.5 26.09 -2.41 26.13 -2.3699 0.327 1.8333 3 1.85e+00
-V9.31 27.5 26.87 -0.63 26.92 -0.5805 0.309 0.0000 1 0.00e+00
-V20.230 27.5 26.87 -0.63 26.90 -0.6015 0.291 0.1667 3 1.77e-01
-V20.7 27.5 27.27 -0.23 27.25 -0.2452 0.431 0.0000 2 1.06e-03
-V20.234 27.0 27.02 0.02 27.08 0.0788 0.228 0.0000 1 0.00e+00
-V18.21 27.0 26.77 -0.23 26.87 -0.1318 0.252 0.0000 1 3.55e-15
-V12.122 28.0 26.92 -1.08 26.94 -1.0561 0.228 0.9000 5 9.17e-01
- k.W
-V14.61 5
-V17.196 3
-V18.110 2
-V16.227 9
-V14.47 6
-V23.22 3
-V2.12 10
-V23.29 2
-V12.43 5
-R9.7 2
-A157.3 2
-V23.81 4
-V23.82 7
-V12.53 7
-V23.83 10
-V12.56 2
-A152.84 7
-V16.50 1
-V22.122 10
-V16.41 1
-V4.32 8
-V12.66 1
-V19.245 4
-V4.8 1
-A180.15 1
-V18.34 10
-V20.213 6
-V19.222 9
-A180.39 6
-V16.189 1
-V12.18 9
-V7.67 1
-V17.165 1
-V19.310 1
-V16.190 3
-A153.154 2
-V19.308 10
-V22.172 1
-V10.98 1
-V22.219 2
-V16.33 3
-V22.204 7
-V20.167 4
-V10.89 10
-V12.79 4
-V19.216 7
-V14.90 4
-A180.72 7
-V16.21 1
-A180.76 7
-V15.164 1
-A180.78 5
-V14.5 1
-V3.128 2
-A179.13 3
-V9.31 1
-V20.230 3
-V20.7 2
-V20.234 1
-V18.21 1
-V12.122 5
-
>
-> ## fitted values
-> fitted(ik.mat2)
-
- Modern Analogue Technique: Fitted values
-
-No. of analogues (k) : 3
-User supplied k? : FALSE
-Weighted analysis? : FALSE
-
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 7.167 4.833 4.667 7.167 7.667 10.000 4.833 11.833
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
- 13.500 13.667 14.500 14.333 14.167 17.000 14.500 20.667
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 22.000 17.167 15.500 23.333 24.000 21.833 20.833 23.833
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- 23.333 25.333 24.500 23.833 24.000 25.733 26.067 25.333
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- 25.333 25.333 25.000 25.733 25.733 26.400 24.667 25.667
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
- 25.733 26.000 26.167 27.233 27.000 25.667 26.833 26.667
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
- 26.400 27.167 26.733 26.833 27.333 26.067 26.667 27.167
- V20.230 V20.7 V20.234 V18.21 V12.122
- 27.333 27.333 27.333 27.333 27.000
>
-> ## model residuals
-> resid(ik.mat2)
-
- Modern Analogue Technique Residuals
-
-No. of analogues (k) : 3
-User supplied k? : FALSE
-Weighted analysis? : FALSE
-
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 5.1667 -0.1667 -0.8333 0.1667 0.6667 -0.5000 -6.1667 1.8333
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
- 0.5000 1.6667 0.5000 -0.1667 -0.8333 2.5000 -1.5000 2.6667
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 2.0000 -0.8333 -3.5000 4.8333 2.5000 0.8333 -0.1667 -0.1667
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- -0.6667 2.3333 0.5000 0.8333 1.0000 1.7333 1.0667 -0.6667
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- -0.6667 -0.6667 0.0000 -0.2667 -0.2667 1.9000 -2.3333 -0.5333
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
- 0.7333 -0.5000 -0.0333 1.2333 1.0000 -1.3333 -0.1667 -0.8333
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
- -0.6000 0.1667 -0.2667 -0.1667 0.3333 -2.9333 -1.8333 -0.3333
- V20.230 V20.7 V20.234 V18.21 V12.122
- -0.1667 -0.1667 0.3333 0.3333 -1.0000
>
-> ## draw summary plots of the model
-> par(mfrow = c(2,2))
-> plot(ik.mat2)
-> par(mfrow = c(1,1))
->
-> ## reconstruct for the V12.122 core data
-> coreV12.mat2 <- predict(ik.mat, V12.122, k = 3)
-> coreV12.mat2
-
- Modern Analogue Technique predictions
-
-Dissimilarity: chord
-k-closest analogues: 3, Chosen automatically? FALSE
-Weighted mean: FALSE
-Bootstrap estimates: FALSE
-
-Model error estimates:
- RMSEP r.squared avg.bias max.bias
- 1.7130 0.9409 0.1328 5.1667
-
-Predicted values:
- 0 10 20 30 40 50 60 70 80 90 100 110 120
-27.33 27.33 27.33 26.33 25.90 25.90 25.50 25.83 26.17 26.00 27.00 27.33 26.33
- 130 140 150 160 170 180 190 200 210 220 230 240 250
-27.17 26.50 25.57 26.57 26.57 27.23 27.33 26.50 26.83 27.50 27.33 27.73 26.00
- 260 270 280 290 300 310 320 330 340 350 360 370 380
-25.90 26.33 26.50 27.33 25.90 26.33 26.33 26.17 25.83 27.07 25.90 25.90 27.50
- 390 400 410 420 430 440 450 460 470 480 490 500 510
-26.83 27.00 27.73 27.17 27.50 27.17 27.00 27.40 25.90 26.83 27.67 27.33 27.00
- 520 530 540 550 560 570 580 590 600 610 620 630 640
-27.00 25.90 26.07 26.17 27.33 27.33 26.50 25.90 27.17 27.07 26.57 27.17 27.33
- 650 660 670 680 690 700 710 720 730 740 750 760 770
-27.17 26.83 27.00 26.83 27.17 26.90 27.40 26.17 27.83 27.00 26.83 26.83 27.40
- 780 790 800 810 820 830 840 850 860 870 880 890 900
-27.67 27.33 27.33 26.50 26.83 25.50 26.17 27.00 26.00 25.90 26.33 25.00 26.83
- 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030
-26.17 26.50 27.33 27.33 26.73 27.50 27.33 27.33 27.33 26.17 27.33 26.17 26.17
- 1040 1050 1060 1070 1080 1090
-27.00 26.00 26.83 25.50 26.50 26.00
-> summary(coreV12.mat2)
-
- Modern Analogue Technique predictions
-
-Dissimilarity: chord
-k-closest analogues: 3, Chosen automatically? FALSE
-Weighted mean: FALSE
-Bootstrap estimates: FALSE
-
-Model error estimates:
- RMSEP r.squared avg.bias max.bias
- 1.7130 0.9409 0.1328 5.1667
-
-Predicted values:
- 0 10 20 30 40 50 60 70 80 90 100 110 120
-27.33 27.33 27.33 26.33 25.90 25.90 25.50 25.83 26.17 26.00 27.00 27.33 26.33
- 130 140 150 160 170 180 190 200 210 220 230 240 250
-27.17 26.50 25.57 26.57 26.57 27.23 27.33 26.50 26.83 27.50 27.33 27.73 26.00
- 260 270 280 290 300 310 320 330 340 350 360 370 380
-25.90 26.33 26.50 27.33 25.90 26.33 26.33 26.17 25.83 27.07 25.90 25.90 27.50
- 390 400 410 420 430 440 450 460 470 480 490 500 510
-26.83 27.00 27.73 27.17 27.50 27.17 27.00 27.40 25.90 26.83 27.67 27.33 27.00
- 520 530 540 550 560 570 580 590 600 610 620 630 640
-27.00 25.90 26.07 26.17 27.33 27.33 26.50 25.90 27.17 27.07 26.57 27.17 27.33
- 650 660 670 680 690 700 710 720 730 740 750 760 770
-27.17 26.83 27.00 26.83 27.17 26.90 27.40 26.17 27.83 27.00 26.83 26.83 27.40
- 780 790 800 810 820 830 840 850 860 870 880 890 900
-27.67 27.33 27.33 26.50 26.83 25.50 26.17 27.00 26.00 25.90 26.33 25.00 26.83
- 910 920 930 940 950 960 970 980 990 1000 1010 1020 1030
-26.17 26.50 27.33 27.33 26.73 27.50 27.33 27.33 27.33 26.17 27.33 26.17 26.17
- 1040 1050 1060 1070 1080 1090
-27.00 26.00 26.83 25.50 26.50 26.00
-
-Training set assessment:
-
- Obs Est Resid
-V14.61 2.0 7.167 5.16667
-V17.196 5.0 4.833 -0.16667
-V18.110 5.5 4.667 -0.83333
-V16.227 7.0 7.167 0.16667
-V14.47 7.0 7.667 0.66667
-V23.22 10.5 10.000 -0.50000
-V2.12 11.0 4.833 -6.16667
-V23.29 10.0 11.833 1.83333
-V12.43 13.0 13.500 0.50000
-R9.7 12.0 13.667 1.66667
-A157.3 14.0 14.500 0.50000
-V23.81 14.5 14.333 -0.16667
-V23.82 15.0 14.167 -0.83333
-V12.53 14.5 17.000 2.50000
-V23.83 16.0 14.500 -1.50000
-V12.56 18.0 20.667 2.66667
-A152.84 20.0 22.000 2.00000
-V16.50 18.0 17.167 -0.83333
-V22.122 19.0 15.500 -3.50000
-V16.41 18.5 23.333 4.83333
-V4.32 21.5 24.000 2.50000
-V12.66 21.0 21.833 0.83333
-V19.245 21.0 20.833 -0.16667
-V4.8 24.0 23.833 -0.16667
-A180.15 24.0 23.333 -0.66667
-V18.34 23.0 25.333 2.33333
-V20.213 24.0 24.500 0.50000
-V19.222 23.0 23.833 0.83333
-A180.39 23.0 24.000 1.00000
-V16.189 24.0 25.733 1.73333
-V12.18 25.0 26.067 1.06667
-V7.67 26.0 25.333 -0.66667
-V17.165 26.0 25.333 -0.66667
-V19.310 26.0 25.333 -0.66667
-V16.190 25.0 25.000 0.00000
-A153.154 26.0 25.733 -0.26667
-V19.308 26.0 25.733 -0.26667
-V22.172 24.5 26.400 1.90000
-V10.98 27.0 24.667 -2.33333
-V22.219 26.2 25.667 -0.53333
-V16.33 25.0 25.733 0.73333
-V22.204 26.5 26.000 -0.50000
-V20.167 26.2 26.167 -0.03333
-V10.89 26.0 27.233 1.23333
-V12.79 26.0 27.000 1.00000
-V19.216 27.0 25.667 -1.33333
-V14.90 27.0 26.833 -0.16667
-A180.72 27.5 26.667 -0.83333
-V16.21 27.0 26.400 -0.60000
-A180.76 27.0 27.167 0.16667
-V15.164 27.0 26.733 -0.26667
-A180.78 27.0 26.833 -0.16667
-V14.5 27.0 27.333 0.33333
-V3.128 29.0 26.067 -2.93333
-A179.13 28.5 26.667 -1.83333
-V9.31 27.5 27.167 -0.33333
-V20.230 27.5 27.333 -0.16667
-V20.7 27.5 27.333 -0.16667
-V20.234 27.0 27.333 0.33333
-V18.21 27.0 27.333 0.33333
-V12.122 28.0 27.000 -1.00000
->
-> ## draw the reconstruction
-> reconPlot(coreV12.mat2, use.labels = TRUE, display.error = "bars",
-+ xlab = "Depth", ylab = "SumSST")
->
->
->
->
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("mcarlo")
@@ -4897,22 +4527,13 @@
>
> ### ** Examples
>
-> ## continue the example from ?wa
-> example(wa)
+> data(ImbrieKipp)
+> data(SumSST)
+>
+> ## fit the WA model
+> mod <- wa(SumSST ~., data = ImbrieKipp)
+> mod
-wa> ## Don't show:
-wa> od <- options(digits = 5)
-
-wa> ## End Don't show
-wa> data(ImbrieKipp)
-
-wa> data(SumSST)
-
-wa> ## fit the WA model
-wa> mod <- wa(SumSST ~., data = ImbrieKipp)
-
-wa> mod
-
Weighted Averaging Transfer Function
Call:
@@ -4927,194 +4548,6 @@
RMSE R-squared Avg. Bias Max. Bias
2.0188 0.9173 0.0000 -3.8155
-
-wa> ## extract the fitted values
-wa> fitted(mod)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 3.7310 3.8599 4.1077 4.2939 8.2876 9.2444 4.0761 13.8155
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
- 14.3345 16.5213 15.8044 18.7365 18.2896 18.4587 17.3886 20.4020
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 19.9694 19.7086 18.7815 22.7892 22.4079 20.7855 22.4544 22.1814
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- 21.5623 23.3379 23.3608 22.8445 24.2193 25.6257 25.4988 23.3779
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- 23.7472 23.1125 24.5166 25.3837 25.7968 26.2585 24.1625 25.4644
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
- 26.2402 25.8240 26.6780 26.3945 26.0913 25.7191 25.8627 26.3385
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
- 26.7898 26.6969 26.8217 25.9874 26.8824 26.9062 26.5153 26.0680
- V20.230 V20.7 V20.234 V18.21 V12.122
- 26.6088 27.2316 26.7654 26.9459 26.8330
-
-wa> ## residuals for the training set
-wa> residuals(mod)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
--1.730960 1.140079 1.392336 2.706094 -1.287580 1.255591 6.923869 -3.815481
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
--1.334514 -4.521301 -1.804396 -4.236542 -3.289596 -3.958708 -1.388560 -2.401983
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 0.030579 -1.708591 0.218460 -4.289225 -0.907882 0.214508 -1.454368 1.818595
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- 2.437667 -0.337938 0.639214 0.155458 -1.219277 -1.625657 -0.498810 2.622087
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- 2.252766 2.887535 0.483422 0.616261 0.203160 -1.758495 2.837538 0.735556
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--1.240175 0.675969 -0.477965 -0.394526 -0.091336 1.280861 1.137318 1.161456
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
- 0.210242 0.303094 0.178331 1.012626 0.117565 2.093765 1.984665 1.432030
- V20.230 V20.7 V20.234 V18.21 V12.122
- 0.891171 0.268358 0.234590 0.054063 1.166988
-
-wa> ## deshrinking coefficients
-wa> coef(mod)
-[1] -5.6876 1.2659
-
-wa> ## diagnostics plots
-wa> par(mfrow = c(1,2))
-
-wa> plot(mod)
-
-wa> par(mfrow = c(1,1))
-
-wa> ## caterpillar plot of optima and tolerances
-wa> caterpillarPlot(mod) ## observed tolerances
-
-wa> caterpillarPlot(mod, type = "model") ## with tolerances used in WA model
-
-wa> ## plot diagnostics for the WA model
-wa> par(mfrow = c(1,2))
-
-wa> plot(mod)
-
-wa> par(mfrow = c(1,1))
-
-wa> ## tolerance DW
-wa> mod2 <- wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
-wa+ min.tol = 2, small.tol = "min")
-
-wa> mod2
-
- Weighted Averaging Transfer Function
-
-Call:
-wa(formula = SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE, small.tol = "min",
-
- min.tol = 2)
-
-Deshrinking : Inverse
-Tolerance DW : Yes
-No. samples : 61
-No. species : 27
-
-Performance:
- RMSE R-squared Avg. Bias Max. Bias
- 2.0268 0.9166 0.0000 -2.4507
-
-
-wa> ## compare actual tolerances to working values
-wa> with(mod2, rbind(tolerances, model.tol))
- O.univ G.cglob G.ruber G.tenel G.saccu G.rubes G.pacL G.pacR G.bullo
-tolerances 3.7464 1.8956 1.9096 2.1248 1.9797 1.9683 3.9414 5.1812 5.828
-model.tol 3.7464 2.1248 2.1248 2.1248 2.1248 2.1248 3.9414 5.1812 5.828
- G.falco G.calid G.aequi G.gluti G.duter G.infla G.trnL G.trnR
-tolerances 3.1092 2.9731 2.5617 5.8983 1.9983 4.7239 4.1617 3.4349
-model.tol 3.1092 2.9731 2.5617 5.8983 2.1248 4.7239 4.1617 3.4349
- G.crasf G.scitu G.mentu P.obliq C.nitid S.dehis G.digit Other
-tolerances 3.354 3.9907 2.3866 1.5548 1.4617 3.8447 3.1089 5.1125
-model.tol 3.354 3.9907 2.3866 2.1248 2.1248 3.8447 3.1089 5.1125
- G.quin G.hirsu
-tolerances 4.2688 3.9421
-model.tol 4.2688 3.9421
-
-wa> ## tolerance DW
-wa> mod3 <- wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
-wa+ min.tol = 2, small.tol = "mean")
-
-wa> mod3
-
- Weighted Averaging Transfer Function
-
-Call:
-wa(formula = SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE, small.tol = "mean",
-
- min.tol = 2)
-
-Deshrinking : Inverse
-Tolerance DW : Yes
-No. samples : 61
-No. species : 27
-
-Performance:
- RMSE R-squared Avg. Bias Max. Bias
- 1.9924 0.9194 0.0000 -2.5992
-
-
-wa> ## fit a WA model with monotonic deshrinking
-wa> mod4 <- wa(SumSST ~., data = ImbrieKipp, deshrink = "monotonic")
-
-wa> mod4
-
- Weighted Averaging Transfer Function
-
-Call:
-wa(formula = SumSST ~ ., data = ImbrieKipp, deshrink = "monotonic")
-
-Deshrinking : Monotonic
-Tolerance DW : No
-No. samples : 61
-No. species : 27
-
-Performance:
- RMSE R-squared Avg. Bias Max. Bias
- 1.6107 0.9474 0.0000 -3.8985
-
-
-wa> ## extract the fitted values
-wa> fitted(mod4)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 5.8985 5.9591 6.0758 6.1635 8.1265 8.6414 6.0609 11.5633
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
- 11.9710 14.0109 13.2762 16.8040 16.1689 16.4046 15.0028 19.3976
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 18.7065 18.2923 16.8700 22.9403 22.4278 20.0083 22.4915 22.1128
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- 21.2115 23.6373 23.6652 23.0128 24.6599 26.0993 25.9754 23.6862
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- 24.1262 23.3567 24.9810 25.8625 26.2661 26.7165 24.5973 25.9417
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
- 26.6986 26.2926 27.1273 26.8495 26.5532 26.1904 26.3303 26.7948
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
- 27.2370 27.1459 27.2683 26.4518 27.3279 27.3512 26.9679 26.5304
- V20.230 V20.7 V20.234 V18.21 V12.122
- 27.0595 27.6704 27.2131 27.3901 27.2794
-
-wa> ## residuals for the training set
-wa> residuals(mod4)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
--3.898451 -0.959142 -0.575776 0.836468 -1.126549 1.858557 4.939074 -1.563327
- V12.43 R9.7 A157.3 V23.81 V23.82 V12.53 V23.83 V12.56
- 1.029013 -2.010916 0.723792 -2.303976 -1.168939 -1.904646 0.997217 -1.397640
- A152.84 V16.50 V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 1.293463 -0.292346 2.130001 -4.440318 -0.927838 0.991727 -1.491527 1.887240
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189 V12.18 V7.67
- 2.788500 -0.637275 0.334750 -0.012758 -1.659931 -2.099307 -0.975387 2.313835
- V17.165 V19.310 V16.190 A153.154 V19.308 V22.172 V10.98 V22.219
- 1.873805 2.643276 0.019001 0.137472 -0.266124 -2.216472 2.402708 0.258265
- V16.33 V22.204 V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--1.698566 0.207389 -0.927317 -0.849545 -0.553215 0.809566 0.669733 0.705244
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128 A179.13 V9.31
--0.236962 -0.145893 -0.268262 0.548197 -0.327863 1.648793 1.532122 0.969585
- V20.230 V20.7 V20.234 V18.21 V12.122
- 0.440478 -0.170385 -0.213081 -0.390149 0.720613
-
-wa> ## Don't show:
-wa> options(od)
-
-wa> ## End Don't show
-wa>
-wa>
-wa>
>
> ## the model performance statistics
> performance(mod)
@@ -5554,218 +4987,6 @@
>
> ## MAT
> ik.mat <- mat(ImbrieKipp, SumSST, method = "chord")
-> ik.mat
-
- 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
-
-> summary(ik.mat)
-
- 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 2.50 0.88 0.32 9.00
- 2 1.87 0.93 0.28 6.00
- 3 1.71 0.94 0.13 5.17
- 4 1.80 0.94 0.18 5.12
- 5 1.75 0.94 0.21 5.10
- 6 1.72 0.94 0.28 5.67
- 7 1.76 0.94 0.38 6.43
- 8 1.83 0.94 0.39 6.62
- 9 1.91 0.94 0.45 7.22
- 10 2.04 0.93 0.58 7.50
-
-Inferences based on the weighted mean of k-closest analogues:
-
- k RMSEP R2 Avg Bias Max Bias
- 1 2.50 0.88 0.32 9.00
- 2 1.89 0.93 0.26 6.18
- 3 1.73 0.94 0.14 5.47
- 4 1.77 0.94 0.17 5.38
- 5 1.75 0.94 0.19 5.37
- 6 1.71 0.94 0.22 5.49
- 7 1.71 0.94 0.25 5.63
- 8 1.76 0.94 0.25 5.69
- 9 1.78 0.94 0.27 5.84
- 10 1.86 0.94 0.36 5.93
-
-Results for training set
-
- * (W.)Est and (W.)Resi are based on k=10-closest analogues
- * minDC is the minimum distance to another sample in the training set
- * min(W.)Resi is the minimum residual for a k-closest model,
- where k = 1,...,10. Column k(.W) displays which k has minResi
-
- Obs Est Resi W.Est W.Resi minDC minResi k minW.Resi
-V14.61 2.0 9.50 7.50 7.93 5.9273 0.104 5.1000 5 5.37e+00
-V17.196 5.0 9.20 4.20 6.86 1.8572 0.130 0.1667 3 4.61e-01
-V18.110 5.5 9.15 3.65 7.13 1.6290 0.134 0.5000 1 4.84e-01
-V16.227 7.0 9.00 2.00 7.16 0.1554 0.134 0.1667 3 2.62e-02
-V14.47 7.0 9.00 2.00 8.66 1.6594 0.452 0.0000 4 3.63e-02
-V23.22 10.5 8.65 -1.85 8.68 -1.8229 0.467 0.5000 3 7.43e-01
-V2.12 11.0 8.60 -2.40 5.70 -5.2971 0.119 2.4000 10 5.30e+00
-V23.29 10.0 13.65 3.65 13.33 3.3347 0.467 1.2500 2 1.41e+00
-V12.43 13.0 13.25 0.25 13.31 0.3135 0.490 0.0000 5 1.71e-01
-R9.7 12.0 14.85 2.85 14.29 2.2876 0.432 1.0000 2 4.65e-01
-A157.3 14.0 16.30 2.30 16.04 2.0418 0.407 0.5000 2 3.14e-01
-V23.81 14.5 15.45 0.95 15.40 0.8996 0.299 0.1667 3 8.08e-02
-V23.82 15.0 16.20 1.20 15.84 0.8369 0.299 0.1429 7 1.40e-01
-V12.53 14.5 17.80 3.30 17.76 3.2607 0.442 2.3571 7 2.55e+00
-V23.83 16.0 15.30 -0.70 15.02 -0.9814 0.295 0.7000 10 9.81e-01
-V12.56 18.0 20.90 2.90 20.78 2.7832 0.380 1.0000 2 1.03e+00
-A152.84 20.0 21.60 1.60 21.64 1.6374 0.361 0.1429 7 5.77e-01
-V16.50 18.0 20.50 2.50 20.28 2.2759 0.429 0.0000 1 0.00e+00
-V22.122 19.0 17.95 -1.05 17.58 -1.4223 0.429 1.0500 10 1.42e+00
-V16.41 18.5 23.90 5.40 23.64 5.1402 0.380 2.5000 1 2.50e+00
-V4.32 21.5 23.60 2.10 23.62 2.1185 0.333 1.6250 8 1.73e+00
-V12.66 21.0 21.10 0.10 21.12 0.1228 0.421 0.0000 1 0.00e+00
-V19.245 21.0 23.20 2.20 22.72 1.7177 0.331 0.1667 3 4.41e-01
-V4.8 24.0 23.35 -0.65 23.38 -0.6203 0.280 0.0000 1 0.00e+00
-A180.15 24.0 22.95 -1.05 22.99 -1.0083 0.292 0.0000 1 0.00e+00
-V18.34 23.0 24.35 1.35 24.49 1.4904 0.411 1.3500 10 1.49e+00
-V20.213 24.0 24.57 0.57 24.56 0.5565 0.326 0.0833 6 1.38e-01
-V19.222 23.0 23.00 0.00 23.06 0.0580 0.384 0.0000 10 3.83e-02
-A180.39 23.0 24.25 1.25 24.24 1.2366 0.347 0.4167 6 5.12e-01
-V16.189 24.0 25.89 1.89 25.85 1.8495 0.399 1.0000 1 1.00e+00
-V12.18 25.0 25.39 0.39 25.48 0.4751 0.289 0.3000 9 4.19e-01
-V7.67 26.0 23.45 -2.55 23.76 -2.2436 0.308 0.0000 1 0.00e+00
-V17.165 26.0 24.77 -1.23 24.80 -1.2021 0.308 0.0000 1 0.00e+00
-V19.310 26.0 23.75 -2.25 23.97 -2.0257 0.296 0.0000 1 0.00e+00
-V16.190 25.0 25.32 0.32 25.34 0.3424 0.324 0.0000 2 3.98e-02
-A153.154 26.0 25.67 -0.33 25.73 -0.2694 0.222 0.1000 2 1.11e-01
-V19.308 26.0 25.99 -0.01 25.98 -0.0223 0.222 0.0100 10 2.23e-02
-V22.172 24.5 26.72 2.22 26.71 2.2110 0.307 1.5000 1 1.50e+00
-V10.98 27.0 24.82 -2.18 24.77 -2.2349 0.330 2.0000 1 2.00e+00
-V22.219 26.2 25.65 -0.55 25.63 -0.5709 0.189 0.2000 1 2.00e-01
-V16.33 25.0 26.37 1.37 26.37 1.3703 0.493 0.7333 3 7.48e-01
-V22.204 26.5 26.80 0.30 26.74 0.2362 0.325 0.0000 6 3.12e-02
-V20.167 26.2 26.90 0.70 26.87 0.6732 0.257 0.0333 3 1.17e-02
-V10.89 26.0 26.04 0.04 26.12 0.1184 0.308 0.0400 10 1.18e-01
-V12.79 26.0 26.90 0.90 26.88 0.8813 0.249 0.3750 4 5.16e-01
-V19.216 27.0 25.27 -1.73 25.32 -1.6776 0.363 0.9000 7 9.65e-01
-V14.90 27.0 26.95 -0.05 26.89 -0.1113 0.249 0.0000 4 2.56e-02
-A180.72 27.5 26.75 -0.75 26.74 -0.7623 0.185 0.4286 7 5.18e-01
-V16.21 27.0 26.87 -0.13 26.85 -0.1453 0.247 0.0000 1 0.00e+00
-A180.76 27.0 27.15 0.15 27.16 0.1582 0.233 0.0000 6 3.86e-02
-V15.164 27.0 26.82 -0.18 26.79 -0.2081 0.257 0.0000 1 0.00e+00
-A180.78 27.0 27.20 0.20 27.19 0.1940 0.405 0.0000 5 1.03e-04
-V14.5 27.0 27.02 0.02 27.07 0.0730 0.219 0.0000 1 3.55e-15
-V3.128 29.0 26.72 -2.28 26.69 -2.3050 0.366 2.1500 2 2.17e+00
-A179.13 28.5 26.09 -2.41 26.13 -2.3699 0.327 1.8333 3 1.85e+00
-V9.31 27.5 26.87 -0.63 26.92 -0.5805 0.309 0.0000 1 0.00e+00
-V20.230 27.5 26.87 -0.63 26.90 -0.6015 0.291 0.1667 3 1.77e-01
-V20.7 27.5 27.27 -0.23 27.25 -0.2452 0.431 0.0000 2 1.06e-03
-V20.234 27.0 27.02 0.02 27.08 0.0788 0.228 0.0000 1 0.00e+00
-V18.21 27.0 26.77 -0.23 26.87 -0.1318 0.252 0.0000 1 3.55e-15
-V12.122 28.0 26.92 -1.08 26.94 -1.0561 0.228 0.9000 5 9.17e-01
- k.W
-V14.61 5
-V17.196 3
-V18.110 2
-V16.227 9
-V14.47 6
-V23.22 3
-V2.12 10
-V23.29 2
-V12.43 5
-R9.7 2
-A157.3 2
-V23.81 4
-V23.82 7
-V12.53 7
-V23.83 10
-V12.56 2
-A152.84 7
-V16.50 1
-V22.122 10
-V16.41 1
-V4.32 8
-V12.66 1
-V19.245 4
-V4.8 1
-A180.15 1
-V18.34 10
-V20.213 6
-V19.222 9
-A180.39 6
-V16.189 1
-V12.18 9
-V7.67 1
-V17.165 1
-V19.310 1
-V16.190 3
-A153.154 2
-V19.308 10
-V22.172 1
-V10.98 1
-V22.219 2
-V16.33 3
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/analogue -r 346
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