Author: gsimpson
Date: 2013-07-20 22:31:00 +0200 (Sat, 20 Jul 2013)
New Revision: 343
Modified:
pkg/tests/Examples/analogue-Ex.Rout.save
Log:
Update example test output
Modified: pkg/tests/Examples/analogue-Ex.Rout.save
===================================================================
--- pkg/tests/Examples/analogue-Ex.Rout.save 2013-07-20 20:30:28 UTC (rev 342)
+++ pkg/tests/Examples/analogue-Ex.Rout.save 2013-07-20 20:31:00 UTC (rev 343)
@@ -1,7 +1,7 @@
-R version 3.0.1 (2013-05-16) -- "Good Sport"
+R version 3.0.1 RC (2013-05-11 r62732) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
-Platform: x86_64-pc-linux-gnu (64-bit)
+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.
@@ -23,10 +23,11 @@
> library('analogue')
Loading required package: vegan
Loading required package: permute
-This is vegan 2.0-7
+This is vegan 2.1-29
Loading required package: princurve
Loading required package: lattice
-This is analogue 0.11-3
+Loading required package: rgl
+This is analogue 0.11-4
>
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> cleanEx()
@@ -3441,14 +3442,14 @@
Logit regression models
- In Out Est.(Dij) Std.Err Z-value p-value Dij(p=0.9) Std.Err(Dij)
-1 46 121 -15.8 2.80 -5.63 1.8454e-08 0.338 0.0332
-2 35 132 -32.2 6.87 -4.68 2.8597e-06 0.409 0.0200
-3 22 145 -34.1 10.19 -3.35 0.00081417 0.366 0.0264
-4 24 143 -27.2 6.74 -4.03 5.6098e-05 0.495 0.0274
-5 25 142 -16.2 3.93 -4.12 3.7464e-05 0.461 0.0494
-6 15 152 -11.0 2.71 -4.06 4.8045e-05 0.283 0.0794
-Combined 167 835 -16.2 1.41 -11.51 < 2.22e-16 0.359 0.0166
+ In Out E[Dij] SE Z p-value Dij(p=0.9) SE (Dij)
+1 46 121 -15.8 2.80 -5.63 1.8454e-08 0.338 0.0332
+2 35 132 -32.2 6.87 -4.68 2.8597e-06 0.409 0.0200
+3 22 145 -34.1 10.19 -3.35 0.00081417 0.366 0.0264
+4 24 143 -27.2 6.74 -4.03 5.6098e-05 0.495 0.0274
+5 25 142 -16.2 3.93 -4.12 3.7464e-05 0.461 0.0494
+6 15 152 -11.0 2.71 -4.06 4.8045e-05 0.283 0.0794
+Combined 167 835 -16.2 1.41 -11.51 < 2.22e-16 0.359 0.0166
>
> ## plot the fitted logit curves
@@ -3480,8 +3481,25 @@
002.3 0.9372000 0.02182155 0.3034852 0.031319684 1.133939e-05 0.01383880
002.8 0.9732644 0.05464832 0.5834611 0.027163318 4.119295e-05 0.03144971
>
+> ## Bias reduction
+> ## fit the logit models to the analog object
+> swap.brlrm <- logitreg(swap.ana, grps, biasReduced = TRUE)
+> summary(swap.brlrm)
+
+Logit regression models
+
+ In Out E[Dij] SE Z p-value Dij(p=0.9) SE (Dij)
+1 46 121 -15.1 2.68 -5.63 1.7690e-08 0.331 0.0346
+2 35 132 -29.5 6.08 -4.85 1.2174e-06 0.403 0.0212
+3 22 145 -28.5 7.82 -3.65 0.00026651 0.353 0.0287
+4 24 143 -24.2 5.73 -4.22 2.4163e-05 0.485 0.0298
+5 25 142 -14.5 3.34 -4.33 1.4819e-05 0.442 0.0530
+6 15 152 -10.1 2.44 -4.15 3.3636e-05 0.260 0.0858
+Combined 167 835 -16.0 1.38 -11.55 < 2.22e-16 0.357 0.0167
+
>
>
+>
> cleanEx()
> nameEx("mat")
> ### * mat
@@ -5101,7 +5119,7 @@
> ## the model performance statistics
> performance(mod)
RMSE R2 Avg.Bias Max.Bias
- 2.019e+00 9.173e-01 2.854e-15 -3.815e+00
+ 2.019e+00 9.173e-01 2.228e-14 -3.815e+00
>
>
>
@@ -5953,17 +5971,97 @@
> ## 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
---------------------------------------------------------------------------------
+
+ Determining initial DFs for each variable...
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+
+Fitting Principal Curve:
+
+Initial curve: d.sq: 103233.450
+Iteration 1: d.sq: 4283.431
+Iteration 2: d.sq: 4312.298
+Iteration 3: d.sq: 4340.691
+Iteration 4: d.sq: 4355.388
+Iteration 5: d.sq: 4366.497
+Iteration 6: d.sq: 4369.944
+
PC Converged in 6 iterations.
---------------------------------------------------------------------------------
+
>
> ## Plot the curve
> plot(aber.pc, abernethy2)
@@ -6033,6 +6131,125 @@
>
>
> cleanEx()
+> nameEx("plot3d.prcurve")
+> ### * plot3d.prcurve
+>
+> flush(stderr()); flush(stdout())
+>
+> ### Name: plot3d.prcurve
+> ### Title: Interactive 3D plof of a principal curve in principal coordinate
+> ### space
+> ### Aliases: plot3d.prcurve
+> ### Keywords: dynamic graphics
+>
+> ### ** Examples
+>
+> data(abernethy)
+>
+> ## Remove the Depth and Age variables
+> abernethy2 <- abernethy[, -(37:38)]
+>
+> ## Fit the principal curve using the median complexity over
+> ## all species
+> aber.pc <- prcurve(abernethy2, method = "ca", trace = TRUE,
++ vary = FALSE, penalty = 1.4)
+
+ Determining initial DFs for each variable...
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+Fitting Principal Curve:
+
+Initial curve: d.sq: 103233.450
+Iteration 1: d.sq: 4853.791
+Iteration 2: d.sq: 5013.497
+Iteration 3: d.sq: 5109.973
+Iteration 4: d.sq: 5135.654
+Iteration 5: d.sq: 5137.944
+
+PC Converged in 5 iterations.
+
+>
+> ## 3D plot of data with curve superimposed
+> plot3d(aber.pc, abernethy2)
+>
+>
+>
+> cleanEx()
> nameEx("prcurve")
> ### * prcurve
>
@@ -6040,58 +6257,304 @@
>
> ### Name: prcurve
> ### Title: Fits a principal curve to m-dimensional data
-> ### Aliases: prcurve initCurve smoothSpline print.prcurve
+> ### Aliases: prcurve initCurve print.prcurve
> ### Keywords: multivariate nonparametric smooth
>
> ### ** Examples
>
+> ## Load Abernethy Forest data set
> data(abernethy)
>
-> ## Plot the most common taxa
-> Stratiplot(Age ~ . - Depth, data =
-+ chooseTaxa(abernethy, max.abun = 15, n.occ = 10),
-+ type = c("g","poly"), sort = "wa")
->
> ## Remove the Depth and Age variables
> abernethy2 <- abernethy[, -(37:38)]
>
-> ## Fit PCA and CA
-> aber.pca <- rda(abernethy2)
-> aber.ca <- cca(abernethy2)
->
> ## Fit the principal curve using the median complexity over
> ## all species
> aber.pc <- prcurve(abernethy2, method = "ca", trace = TRUE,
+ vary = FALSE, penalty = 1.4)
---------------------------------------------------------------------------------
-Initial curve: d.sq: 103233.4502
-Iteration 1: d.sq: 4853.7911
-Iteration 2: d.sq: 5013.4971
-Iteration 3: d.sq: 5109.9732
-Iteration 4: d.sq: 5135.6541
-Iteration 5: d.sq: 5137.9439
---------------------------------------------------------------------------------
+
+ Determining initial DFs for each variable...
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+Fitting Principal Curve:
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+Initial curve: d.sq: 103233.450
+Iteration 1: d.sq: 4853.791
+Iteration 2: d.sq: 5013.497
+Iteration 3: d.sq: 5109.973
+Iteration 4: d.sq: 5135.654
+Iteration 5: d.sq: 5137.944
+
PC Converged in 5 iterations.
---------------------------------------------------------------------------------
+
>
> ## Fit the principal curve using varying complexity of smoothers
> ## for each species
> aber.pc2 <- 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
---------------------------------------------------------------------------------
+
+ Determining initial DFs for each variable...
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+Fitting Principal Curve:
+
+Initial curve: d.sq: 103233.450
+Iteration 1: d.sq: 4283.431
+Iteration 2: d.sq: 4312.298
+Iteration 3: d.sq: 4340.691
+Iteration 4: d.sq: 4355.388
+Iteration 5: d.sq: 4366.497
+Iteration 6: d.sq: 4369.944
+
PC Converged in 6 iterations.
---------------------------------------------------------------------------------
+
>
+> ## Fit principal curve using a GAM - currently slow ~10secs
+> aber.pc3 <- prcurve(abernethy2, method = "ca", trace = TRUE,
++ vary = TRUE, smoother = smoothGAM, bs = "cr")
+
+ Determining initial DFs for each variable...
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/analogue -r 343