Last data update: 2014.03.03

R: Calculates projections of the Data
fitted.epplabR Documentation

Calculates projections of the Data

Description

Calculates the projections of the data object onto the directions from an underlying ebblab object.

Usage

## S3 method for class 'epplab'
fitted(object, which = 1, ...)

Arguments

object

Object of class epplab.

which

Onto which direction should the new data be projected.

...

Additional parameters

Details

The default projection direction is the direction with the best objective criterion.

Value

A matrix, having in each column the projection onto the direction of a certain run, and in each row the projected value.

Author(s)

Daniel Fischer

Examples


library(tourr)
data(olive)
res <- EPPlab(olive[,3:10],PPalg="PSO",PPindex="KurtosisMin",n.simu=10, maxiter=20)

# Projection to the best direction
fitted(res)

# Projection to the 1,2,5 best directions:
fitted(res,which=c(1,2,5))

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
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.
Type 'license()' or 'licence()' for distribution details.

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.

> library(REPPlab)
Loading required package: rJava
Loading required package: lattice
Loading required package: LDRTools
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/REPPlab/fitted.epplab.Rd_%03d_medium.png", width=480, height=480)
> ### Name: fitted.epplab
> ### Title: Calculates projections of the Data
> ### Aliases: fitted,epplab-method fitted-method fitted.epplab
> ### Keywords: methods print
> 
> ### ** Examples
> 
> 
> library(tourr)
> data(olive)
> res <- EPPlab(olive[,3:10],PPalg="PSO",PPindex="KurtosisMin",n.simu=10, maxiter=20)
Simulation 0... finished (I -40.563388898 in 0.633s)
Simulation 1... finished (I -13.883229050 in 0.144s)
Simulation 2... finished (I -8.259537463 in 0.043s)
Simulation 3... finished (I -27.873642958 in 0.022s)
Simulation 4... finished (I -0.740216855 in 0.025s)
Simulation 5... finished (I -29.547568449 in 0.075s)
Simulation 6... finished (I -19.877104036 in 0.082s)
Simulation 7... finished (I -2.324882419 in 0.453s)
Simulation 8... finished (I -22.161224390 in 0.184s)
Simulation 9... finished (I -4.146719778 in 0.026s)
Warning message:
In EpplabOutputConv(jepplab, maxiter) :
  There were 10 non-converged simulation runs!
> 
> # Projection to the best direction
> fitted(res)
             Run1
1   -0.1237391842
2   -0.1204480748
3   -0.2323342031
4   -0.2817858186
5   -0.2610085497
6   -0.3919332027
7   -0.3033477533
8   -0.1535946556
9   -0.1771702170
10  -0.1345203128
11  -0.0666548494
12  -0.1989011953
13  -0.2043329555
14  -0.3342747634
15  -0.3601533147
16  -0.0599871473
17  -0.2816965893
18  -0.0345998529
19  -0.1202699007
20  -0.2860737311
21  -0.3153426898
22  -0.3201490872
23  -0.3786766990
24  -0.0400011627
25  -0.2167956862
26  -0.0501451320
27  -0.0952340398
28   0.0212210643
29   0.0093791741
30  -0.1228969695
31  -0.1513792190
32  -0.1534384540
33  -0.0887911554
34  -0.0207028480
35  -0.0707734056
36  -0.1761785690
37  -0.1038945274
38  -0.1272197422
39  -0.1436629817
40  -0.0987524877
41  -0.1158029465
42  -0.1213751535
43  -0.1475820922
44  -0.1520525505
45  -0.0346444745
46  -0.0857564073
47  -0.1074197259
48  -0.0557338054
49  -0.1879593548
50   0.0604762703
51  -0.0924186123
52  -0.0695717097
53  -0.1137693291
54  -0.0406881327
55  -0.1866094453
56   0.0306479692
57  -0.1121554974
58  -0.0901218665
59  -0.0166221164
60   0.0110732359
61   0.0162651562
62  -0.1792945713
63   0.0733712101
64   0.0547841709
65  -0.0671215404
66   0.0114646336
67   0.0306875730
68   0.0273826994
69  -0.0224947529
70   0.0838111131
71   0.0329419845
72  -0.0559320570
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75   0.0085755409
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498 -0.0126102949
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500  0.1236868693
501  0.0603355039
502  0.1609853714
503  0.3634328638
504  0.2968430170
505  0.0869921071
506  0.0859584918
507  0.2458817279
508  0.2534807185
509  0.1860455392
510  0.1162020687
511  0.1146517164
512  0.1701182708
513  0.2080353054
514  0.1246410489
515  0.1060378258
516  0.0055999598
517  0.0853437898
518  0.0700443689
519  0.0613155439
520  0.1347114697
521  0.0007080575
522 -0.3001522826
523  0.1416609310
524  0.0812476343
525 -0.2095079099
526  0.0425218857
527 -0.0835528880
528  0.0432932341
529  0.0273058622
530  0.0196705849
531  0.0877788859
532  0.1957442481
533  0.0020088063
534  0.1593039331
535  0.0765955933
536 -0.0125149781
537 -0.1117303382
538 -0.0503361948
539 -0.0228933225
540  0.0316548921
541  0.2257879312
542  0.0802937988
543 -0.0102495337
544 -0.0644926816
545  0.1387178354
546  0.0373086200
547 -0.0502077502
548 -0.0059725593
549  0.0097825878
550  0.0482606054
551 -0.0305305821
552 -0.0235340610
553 -0.0606758629
554 -0.0939829547
555  0.0179574935
556 -0.0407794741
557  0.0411417650
558 -0.0337406809
559  0.0178628036
560  0.1054556965
561  0.0311468297
562 -0.0744494402
563  0.0632337328
564  0.1969025263
565  0.1229226005
566 -0.0018418420
567  0.0492406801
568  0.1897060956
569  0.0264112871
570 -0.0144011616
571 -0.0385566563
572 -0.0450123007
> 
> # Projection to the 1,2,5 best directions:
> fitted(res,which=c(1,2,5))
             Run1          Run2          Run5
1   -0.1237391842 -0.2351526626  0.2701055041
2   -0.1204480748 -0.1215981201  0.3389697517
3   -0.2323342031 -0.3817570642  0.1446765557
4   -0.2817858186 -0.2725492173 -0.1160010293
5   -0.2610085497 -0.2326107544  0.0152353060
6   -0.3919332027 -0.2361968238 -0.2467269601
7   -0.3033477533 -0.3252823999 -0.3856961382
8   -0.1535946556 -0.1125695341  0.3090571557
9   -0.1771702170 -0.1270014465  0.0782550562
10  -0.1345203128 -0.2226873548  0.6015810725
11  -0.0666548494 -0.1717905175  0.7239844269
12  -0.1989011953 -0.2477642534  0.5570429677
13  -0.2043329555 -0.1220302785  0.0317611009
14  -0.3342747634 -0.2781916082 -0.1647335482
15  -0.3601533147 -0.1068697164 -0.1995644067
16  -0.0599871473 -0.1439832975  0.4191868532
17  -0.2816965893 -0.2670074566 -0.0599574224
18  -0.0345998529  0.2110799869  0.3691525066
19  -0.1202699007 -0.3692454141  0.0963206958
20  -0.2860737311 -0.2569032922 -0.2851041532
21  -0.3153426898 -0.1032372349 -0.0477092545
22  -0.3201490872 -0.1557872966 -0.3522288618
23  -0.3786766990 -0.0568374631  0.0277692249
24  -0.0400011627 -0.1834722557  0.2743020854
25  -0.2167956862 -0.1005828092  0.1444323343
26  -0.0501451320 -0.0845037341  0.1384870422
27  -0.0952340398  0.0852231565  0.0669199243
28   0.0212210643  0.1029010050 -0.0228340813
29   0.0093791741  0.1673812416 -0.0816521771
30  -0.1228969695  0.0804934219  0.1737241954
31  -0.1513792190  0.0259559940  0.3071736086
32  -0.1534384540  0.1165532742 -0.3442175597
33  -0.0887911554  0.0108141381  0.0706713317
34  -0.0207028480  0.0769628436  0.2002157784
35  -0.0707734056  0.0228689162  0.0471585957
36  -0.1761785690  0.0026759021 -0.1566068948
37  -0.1038945274  0.1139186453 -0.0213987214
38  -0.1272197422 -0.0795566853 -0.1770406470
39  -0.1436629817 -0.0009771516 -0.3176356479
40  -0.0987524877  0.1443599442  0.1942735914
41  -0.1158029465  0.0743145540 -0.0066131181
42  -0.1213751535  0.0190768303 -0.5159729893
43  -0.1475820922  0.0717873448 -0.1848793825
44  -0.1520525505  0.1115526013 -0.1242883000
45  -0.0346444745  0.0210429921 -0.0992637060
46  -0.0857564073 -0.0052192558  0.1483732123
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