[Analogue-commits] r302 - pkg/tests/Examples
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Jan 16 23:06:11 CET 2013
Author: gsimpson
Date: 2013-01-16 23:06:10 +0100 (Wed, 16 Jan 2013)
New Revision: 302
Modified:
pkg/tests/Examples/analogue-Ex.Rout.save
Log:
Update the reference output following changes in r300 and r301
Modified: pkg/tests/Examples/analogue-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/analogue-Ex.Rout.save 2013-01-16 20:49:39 UTC (rev 301)
+++ pkg/tests/Examples/analogue-Ex.Rout.save 2013-01-16 22:06:10 UTC (rev 302)
@@ -27,10 +27,7 @@
This is vegan 2.0-5
Loading required package: lattice
Loading required package: grid
-Loading required package: MASS
Loading required package: princurve
-Loading required package: mgcv
-This is mgcv 1.7-22. For overview type 'help("mgcv-package")'.
This is analogue 0.11-0
>
> assign(".oldSearch", search(), pos = 'CheckExEnv')
@@ -4829,6 +4826,10 @@
> ## continue the example from ?wa
> example(wa)
+wa> ## Don't show:
+wa> od <- options(digits = 5)
+
+wa> ## End Don't show
wa> data(ImbrieKipp)
wa> data(SumSST)
@@ -4855,51 +4856,45 @@
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
- 3.730960 3.859921 4.107664 4.293906 8.287580 9.244409 4.076131 13.815481
+-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
-14.334514 16.521301 15.804396 18.736542 18.289596 18.458708 17.388560 20.401983
+-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
-19.969421 19.708591 18.781540 22.789225 22.407882 20.785492 22.454368 22.181405
+ 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
-21.562333 23.337938 23.360786 22.844542 24.219277 25.625657 25.498810 23.377913
+ 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
-23.747234 23.112465 24.516578 25.383739 25.796840 26.258495 24.162462 25.464444
+ 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
-26.240175 25.824031 26.677965 26.394526 26.091336 25.719139 25.862682 26.338544
+-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
-26.789758 26.696906 26.821669 25.987374 26.882435 26.906235 26.515335 26.067970
+ 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
-26.608829 27.231642 26.765410 26.945937 26.833012
+ 0.891171 0.268358 0.234590 0.054063 1.166988
-wa> ## residuals for the training set
-wa> residuals(mod)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22
--1.73095992 1.14007886 1.39233574 2.70609421 -1.28758014 1.25559075
- V2.12 V23.29 V12.43 R9.7 A157.3 V23.81
- 6.92386910 -3.81548071 -1.33451363 -4.52130053 -1.80439553 -4.23654166
- V23.82 V12.53 V23.83 V12.56 A152.84 V16.50
--3.28959647 -3.95870813 -1.38855980 -2.40198307 0.03057864 -1.70859145
- V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 0.21846025 -4.28922519 -0.90788238 0.21450778 -1.45436796 1.81859450
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189
- 2.43766723 -0.33793825 0.63921359 0.15545775 -1.21927713 -1.62565746
- V12.18 V7.67 V17.165 V19.310 V16.190 A153.154
--0.49880978 2.62208666 2.25276621 2.88753454 0.48342160 0.61626144
- V19.308 V22.172 V10.98 V22.219 V16.33 V22.204
- 0.20316050 -1.75849478 2.83753826 0.73555554 -1.24017489 0.67596920
- V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--0.47796506 -0.39452607 -0.09133632 1.28086084 1.13731805 1.16145583
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128
- 0.21024193 0.30309427 0.17833109 1.01262616 0.11756523 2.09376547
- A179.13 V9.31 V20.230 V20.7 V20.234 V18.21
- 1.98466463 1.43202977 0.89117138 0.26835826 0.23458963 0.05406326
- V12.122
- 1.16698821
-
wa> ## deshrinking coefficients
wa> coef(mod)
-[1] -5.687554 1.265881
+[1] -5.6876 1.2659
wa> ## diagnostics plots
wa> par(mfrow = c(1,2))
@@ -4945,18 +4940,18 @@
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
-tolerances 3.746359 1.895600 1.909561 2.124799 1.979651 1.968294 3.941352
-model.tol 3.746359 2.124799 2.124799 2.124799 2.124799 2.124799 3.941352
- G.pacR G.bullo G.falco G.calid G.aequi G.gluti G.duter
-tolerances 5.181162 5.82798 3.109193 2.973112 2.561697 5.898256 1.998304
-model.tol 5.181162 5.82798 3.109193 2.973112 2.561697 5.898256 2.124799
- G.infla G.trnL G.trnR G.crasf G.scitu G.mentu P.obliq
-tolerances 4.723884 4.161704 3.43492 3.354021 3.990673 2.386584 1.554762
-model.tol 4.723884 4.161704 3.43492 3.354021 3.990673 2.386584 2.124799
- C.nitid S.dehis G.digit Other G.quin G.hirsu
-tolerances 1.461725 3.84473 3.108881 5.112464 4.268777 3.942135
-model.tol 2.124799 3.84473 3.108881 5.112464 4.268777 3.942135
+ 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,
@@ -5003,47 +4998,49 @@
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
- 5.898451 5.959142 6.075776 6.163532 8.126549 8.641443 6.060926 11.563327
+-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
-11.970987 14.010916 13.276208 16.803976 16.168939 16.404646 15.002783 19.397640
+ 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
-18.706537 18.292346 16.869999 22.940318 22.427838 20.008273 22.491527 22.112760
+ 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
-21.211500 23.637275 23.665250 23.012758 24.659931 26.099307 25.975387 23.686165
+ 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
-24.126195 23.356724 24.980999 25.862528 26.266124 26.716472 24.597292 25.941735
+ 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
-26.698566 26.292611 27.127317 26.849545 26.553215 26.190434 26.330267 26.794756
+-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
-27.236962 27.145893 27.268262 26.451803 27.327863 27.351207 26.967878 26.530415
+-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
-27.059522 27.670385 27.213081 27.390149 27.279387
+ 0.440478 -0.170385 -0.213081 -0.390149 0.720613
-wa> ## residuals for the training set
-wa> residuals(mod4)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22
--3.89845110 -0.95914236 -0.57577610 0.83646773 -1.12654865 1.85855684
- V2.12 V23.29 V12.43 R9.7 A157.3 V23.81
- 4.93907447 -1.56332718 1.02901346 -2.01091601 0.72379237 -2.30397582
- V23.82 V12.53 V23.83 V12.56 A152.84 V16.50
--1.16893886 -1.90464621 0.99721685 -1.39764003 1.29346294 -0.29234573
- V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 2.13000055 -4.44031821 -0.92783839 0.99172654 -1.49152688 1.88724027
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189
- 2.78850049 -0.63727536 0.33475038 -0.01275751 -1.65993108 -2.09930701
- V12.18 V7.67 V17.165 V19.310 V16.190 A153.154
--0.97538682 2.31383515 1.87380536 2.64327583 0.01900072 0.13747169
- V19.308 V22.172 V10.98 V22.219 V16.33 V22.204
--0.26612390 -2.21647192 2.40270759 0.25826513 -1.69856608 0.20738893
- V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--0.92731700 -0.84954530 -0.55321548 0.80956624 0.66973330 0.70524398
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128
--0.23696237 -0.14589252 -0.26826158 0.54819699 -0.32786314 1.64879299
- A179.13 V9.31 V20.230 V20.7 V20.234 V18.21
- 1.53212229 0.96958483 0.44047759 -0.17038549 -0.21308146 -0.39014886
- V12.122
- 0.72061292
+wa> ## Don't show:
+wa> options(od)
+
+wa> ## End Don't show
+wa>
+wa>
+wa>
>
> ## the model performance statistics
> performance(mod)
@@ -7018,6 +7015,9 @@
>
> ### ** Examples
>
+> ## Don't show:
+> od <- options(digits = 5)
+> ## End Don't show
> data(ImbrieKipp)
> data(SumSST)
>
@@ -7042,51 +7042,45 @@
>
> ## extract the fitted values
> 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
+>
+> ## residuals for the training set
+> residuals(mod)
V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 3.730960 3.859921 4.107664 4.293906 8.287580 9.244409 4.076131 13.815481
+-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
-14.334514 16.521301 15.804396 18.736542 18.289596 18.458708 17.388560 20.401983
+-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
-19.969421 19.708591 18.781540 22.789225 22.407882 20.785492 22.454368 22.181405
+ 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
-21.562333 23.337938 23.360786 22.844542 24.219277 25.625657 25.498810 23.377913
+ 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
-23.747234 23.112465 24.516578 25.383739 25.796840 26.258495 24.162462 25.464444
+ 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
-26.240175 25.824031 26.677965 26.394526 26.091336 25.719139 25.862682 26.338544
+-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
-26.789758 26.696906 26.821669 25.987374 26.882435 26.906235 26.515335 26.067970
+ 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
-26.608829 27.231642 26.765410 26.945937 26.833012
+ 0.891171 0.268358 0.234590 0.054063 1.166988
>
-> ## residuals for the training set
-> residuals(mod)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22
--1.73095992 1.14007886 1.39233574 2.70609421 -1.28758014 1.25559075
- V2.12 V23.29 V12.43 R9.7 A157.3 V23.81
- 6.92386910 -3.81548071 -1.33451363 -4.52130053 -1.80439553 -4.23654166
- V23.82 V12.53 V23.83 V12.56 A152.84 V16.50
--3.28959647 -3.95870813 -1.38855980 -2.40198307 0.03057864 -1.70859145
- V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 0.21846025 -4.28922519 -0.90788238 0.21450778 -1.45436796 1.81859450
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189
- 2.43766723 -0.33793825 0.63921359 0.15545775 -1.21927713 -1.62565746
- V12.18 V7.67 V17.165 V19.310 V16.190 A153.154
--0.49880978 2.62208666 2.25276621 2.88753454 0.48342160 0.61626144
- V19.308 V22.172 V10.98 V22.219 V16.33 V22.204
- 0.20316050 -1.75849478 2.83753826 0.73555554 -1.24017489 0.67596920
- V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--0.47796506 -0.39452607 -0.09133632 1.28086084 1.13731805 1.16145583
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128
- 0.21024193 0.30309427 0.17833109 1.01262616 0.11756523 2.09376547
- A179.13 V9.31 V20.230 V20.7 V20.234 V18.21
- 1.98466463 1.43202977 0.89117138 0.26835826 0.23458963 0.05406326
- V12.122
- 1.16698821
->
> ## deshrinking coefficients
> coef(mod)
-[1] -5.687554 1.265881
+[1] -5.6876 1.2659
>
> ## diagnostics plots
> par(mfrow = c(1,2))
@@ -7126,18 +7120,18 @@
>
> ## compare actual tolerances to working values
> with(mod2, rbind(tolerances, model.tol))
- O.univ G.cglob G.ruber G.tenel G.saccu G.rubes G.pacL
-tolerances 3.746359 1.895600 1.909561 2.124799 1.979651 1.968294 3.941352
-model.tol 3.746359 2.124799 2.124799 2.124799 2.124799 2.124799 3.941352
- G.pacR G.bullo G.falco G.calid G.aequi G.gluti G.duter
-tolerances 5.181162 5.82798 3.109193 2.973112 2.561697 5.898256 1.998304
-model.tol 5.181162 5.82798 3.109193 2.973112 2.561697 5.898256 2.124799
- G.infla G.trnL G.trnR G.crasf G.scitu G.mentu P.obliq
-tolerances 4.723884 4.161704 3.43492 3.354021 3.990673 2.386584 1.554762
-model.tol 4.723884 4.161704 3.43492 3.354021 3.990673 2.386584 2.124799
- C.nitid S.dehis G.digit Other G.quin G.hirsu
-tolerances 1.461725 3.84473 3.108881 5.112464 4.268777 3.942135
-model.tol 2.124799 3.84473 3.108881 5.112464 4.268777 3.942135
+ 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
>
> ## tolerance DW
> mod3 <- wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
@@ -7182,51 +7176,48 @@
>
> ## extract the fitted values
> 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
+>
+> ## residuals for the training set
+> residuals(mod4)
V14.61 V17.196 V18.110 V16.227 V14.47 V23.22 V2.12 V23.29
- 5.898451 5.959142 6.075776 6.163532 8.126549 8.641443 6.060926 11.563327
+-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
-11.970987 14.010916 13.276208 16.803976 16.168939 16.404646 15.002783 19.397640
+ 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
-18.706537 18.292346 16.869999 22.940318 22.427838 20.008273 22.491527 22.112760
+ 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
-21.211500 23.637275 23.665250 23.012758 24.659931 26.099307 25.975387 23.686165
+ 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
-24.126195 23.356724 24.980999 25.862528 26.266124 26.716472 24.597292 25.941735
+ 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
-26.698566 26.292611 27.127317 26.849545 26.553215 26.190434 26.330267 26.794756
+-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
-27.236962 27.145893 27.268262 26.451803 27.327863 27.351207 26.967878 26.530415
+-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
-27.059522 27.670385 27.213081 27.390149 27.279387
+ 0.440478 -0.170385 -0.213081 -0.390149 0.720613
>
-> ## residuals for the training set
-> residuals(mod4)
- V14.61 V17.196 V18.110 V16.227 V14.47 V23.22
--3.89845110 -0.95914236 -0.57577610 0.83646773 -1.12654865 1.85855684
- V2.12 V23.29 V12.43 R9.7 A157.3 V23.81
- 4.93907447 -1.56332718 1.02901346 -2.01091601 0.72379237 -2.30397582
- V23.82 V12.53 V23.83 V12.56 A152.84 V16.50
--1.16893886 -1.90464621 0.99721685 -1.39764003 1.29346294 -0.29234573
- V22.122 V16.41 V4.32 V12.66 V19.245 V4.8
- 2.13000055 -4.44031821 -0.92783839 0.99172654 -1.49152688 1.88724027
- A180.15 V18.34 V20.213 V19.222 A180.39 V16.189
- 2.78850049 -0.63727536 0.33475038 -0.01275751 -1.65993108 -2.09930701
- V12.18 V7.67 V17.165 V19.310 V16.190 A153.154
--0.97538682 2.31383515 1.87380536 2.64327583 0.01900072 0.13747169
- V19.308 V22.172 V10.98 V22.219 V16.33 V22.204
--0.26612390 -2.21647192 2.40270759 0.25826513 -1.69856608 0.20738893
- V20.167 V10.89 V12.79 V19.216 V14.90 A180.72
--0.92731700 -0.84954530 -0.55321548 0.80956624 0.66973330 0.70524398
- V16.21 A180.76 V15.164 A180.78 V14.5 V3.128
--0.23696237 -0.14589252 -0.26826158 0.54819699 -0.32786314 1.64879299
- A179.13 V9.31 V20.230 V20.7 V20.234 V18.21
- 1.53212229 0.96958483 0.44047759 -0.17038549 -0.21308146 -0.39014886
- V12.122
- 0.72061292
+> ## Don't show:
+> options(od)
+> ## End Don't show
>
>
>
->
> graphics::par(get("par.postscript", pos = 'CheckExEnv'))
> cleanEx()
> nameEx("weightedCor")
@@ -7370,7 +7361,7 @@
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
-Time elapsed: 21.537 0.227 22.33 0 0
+Time elapsed: 21.325 0.23 22.692 0 0
> grDevices::dev.off()
null device
1
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