Calculates projections for a new Data Object

Description

Calculates the projections of a new data object onto the directions from an existing ebblab object.

Usage

## S3 method for class 'epplab'
predict(object, which = 1, data = NULL, ...)

Arguments

Details

The default projection direction is the direction with the best objective criterion. In case that no data is given to the function, the fitted scores for the original data will be returned.

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)

newData <- matrix(rnorm(80), ncol=8)

# Projection on the best direction
predict(res, data=newData)

# Projection on the best 3 directions
predict(res, which=1:3, data=newData)

# Similar with function fitted() when no data is given:
predict(res)
fitted(res)

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/predict.epplab.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.epplab
> ### Title: Calculates projections for a new Data Object
> ### Aliases: predict,epplab-method predict-method predict.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 -6.686931699 in 0.638s)
Simulation 1... finished (I -32.637849041 in 0.158s)
Simulation 2... finished (I -43.215716680 in 0.053s)
Simulation 3... finished (I -14.158937772 in 0.023s)
Simulation 4... finished (I -14.474540060 in 0.470s)
Simulation 5... finished (I -1.953292607 in 0.271s)
Simulation 6... finished (I -13.813622374 in 0.023s)
Simulation 7... finished (I -37.518866906 in 0.025s)
Simulation 8... finished (I -2.185131121 in 0.045s)
Simulation 9... finished (I -19.567386479 in 0.025s)
Warning message:
In EpplabOutputConv(jepplab, maxiter) :
  There were 10 non-converged simulation runs!
> 
> newData <- matrix(rnorm(80), ncol=8)
> 
> # Projection on the best direction
> predict(res, data=newData)
           [,1]
 [1,] -18.68355
 [2,] -18.66689
 [3,] -18.66192
 [4,] -18.65937
 [5,] -18.67878
 [6,] -18.66048
 [7,] -18.67276
 [8,] -18.65396
 [9,] -18.67194
[10,] -18.66617
> 
> # Projection on the best 3 directions
> predict(res, which=1:3, data=newData)
           Run1      Run2     Run3
 [1,] -18.68355 -17.53676 19.84546
 [2,] -18.66689 -17.53833 19.82271
 [3,] -18.66192 -17.56144 19.85230
 [4,] -18.65937 -17.58420 19.90047
 [5,] -18.67878 -17.56793 19.86952
 [6,] -18.66048 -17.55505 19.86975
 [7,] -18.67276 -17.56979 19.83860
 [8,] -18.65396 -17.55983 19.88298
 [9,] -18.67194 -17.55952 19.86077
[10,] -18.66617 -17.56436 19.87285
> 
> # Similar with function fitted() when no data is given:
> predict(res)
         [,1]
1   -18.43165
2   -18.44087
3   -18.56290
4   -18.53024
5   -18.43222
6   -18.57029
7   -18.68828
8   -18.36384
9   -18.45808
10  -18.32600
11  -18.31507
12  -18.29542
13  -18.44879
14  -18.66332
15  -18.56148
16  -18.38875
17  -18.60510
18  -18.27945
19  -18.38878
20  -18.71197
21  -18.43892
22  -18.59954
23  -18.42074
24  -18.32052
25  -18.39304
26  -18.49261
27  -18.54485
28  -18.56614
29  -18.57082
30  -18.44154
31  -18.41562
32  -18.69721
33  -18.49217
34  -18.42962
35  -18.58842
36  -18.56868
37  -18.54207
38  -18.51527
39  -18.70807
40  -18.40897
41  -18.50301
42  -18.83816
43  -18.61146
44  -18.54562
45  -18.55437
46  -18.41587
47  -18.65292
48  -18.75032
49  -18.77712
50  -18.74004
51  -18.53279
52  -18.71521
53  -18.71870
54  -18.65797
55  -18.82234
56  -18.42046
57  -18.84787
58  -18.65760
59  -18.65832
60  -18.47343
61  -18.59028
62  -18.74127
63  -18.61161
64  -18.58498
65  -18.64938
66  -18.44697
67  -18.54597
68  -18.42587
69  -18.26739
70  -18.60743
71  -18.56557
72  -18.64345
73  -18.50274
74  -18.53441
75  -18.65794
76  -18.61313
77  -18.74363
78  -18.46300
79  -18.66664
80  -18.62714
81  -18.44394
82  -18.42369
83  -18.57561
84  -18.94692
85  -18.54599
86  -18.50151
87  -18.55808
88  -18.95984
89  -18.65657
90  -18.84677
91  -18.66258
92  -18.84166
93  -18.48409
94  -18.66923
95  -18.65515
96  -18.72593
97  -18.41194
98  -18.68507
99  -18.57735
100 -18.70359
101 -18.54125
102 -18.46295
103 -18.75984
104 -18.63332
105 -18.56766
106 -18.69186
107 -18.54672
108 -18.60312
109 -18.52235
110 -18.59096
111 -18.67683
112 -18.62102
113 -18.60567
114 -18.51574
115 -18.65579
116 -18.48571
117 -18.75078
118 -18.63896
119 -18.65260
120 -18.61871
121 -18.70912
122 -18.71447
123 -18.92963
124 -18.84260
125 -18.63071
126 -18.67054
127 -18.64228
128 -18.73170
129 -18.53500
130 -18.65317
131 -18.84499
132 -18.52064
133 -18.86766
134 -18.66405
135 -18.56460
136 -18.43959
137 -18.56255
138 -18.64418
139 -18.56375
140 -18.78409
141 -18.68548
142 -18.59319
143 -18.60001
144 -18.66358
145 -18.57459
146 -18.68739
147 -18.67599
148 -18.84312
149 -18.67465
150 -18.59959
151 -18.60125
152 -18.80766
153 -18.67071
154 -18.75720
155 -18.56900
156 -18.53775
157 -18.66890
158 -18.70152
159 -18.57645
160 -18.51600
161 -18.70023
162 -18.61061
163 -18.72001
164 -18.50365
165 -18.53043
166 -18.59561
167 -18.78445
168 -18.74429
169 -18.66194
170 -18.49781
171 -18.36671
172 -18.60266
173 -18.54105
174 -18.72627
175 -18.58185
176 -18.66541
177 -18.85193
178 -18.73895
179 -18.78300
180 -18.83711
181 -18.44892
182 -18.47775
183 -18.46370
184 -18.42254
185 -18.55318
186 -18.68755
187 -18.53885
188 -18.96514
189 -18.38511
190 -18.44949
191 -18.68998
192 -18.66037
193 -18.68859
194 -18.90401
195 -18.68407
196 -18.75551
197 -18.69715
198 -18.85999
199 -18.78862
200 -18.93656
201 -18.71311
202 -18.69961
203 -18.71127
204 -18.93426
205 -18.72756
206 -18.68223
207 -18.72341
208 -18.66395
209 -18.63980
210 -18.62636
211 -18.56822
212 -18.70596
213 -18.75418
214 -18.67513
215 -18.61218
216 -18.70558
217 -18.46386
218 -18.43132
219 -18.51769
220 -18.59753
221 -18.47170
222 -18.42013
223 -18.48743
224 -18.36947
225 -18.47849
226 -18.48276
227 -18.56716
228 -18.55904
229 -18.56582
230 -18.44449
231 -18.66973
232 -18.69816
233 -18.36607
234 -18.54841
235 -18.67179
236 -18.46662
237 -18.56880
238 -18.60904
239 -18.53204
240 -18.65065
241 -18.54356
242 -18.67961
243 -18.55859
244 -18.66789
245 -18.53580
246 -18.49449
247 -18.68526
248 -18.62496
249 -18.58580
250 -18.57973
251 -18.57809
252 -18.53085
253 -18.45873
254 -18.48588
255 -18.66128
256 -18.73399
257 -18.84206
258 -18.51133
259 -18.69012
260 -18.78211
261 -18.90891
262 -18.75384
263 -18.68361
264 -18.46743
265 -18.71172
266 -18.78939
267 -18.77914
268 -18.65017
269 -18.43320
270 -18.60838
271 -18.48044
272 -18.60065
273 -18.45100
274 -18.57198
275 -18.35972
276 -18.57203
277 -18.68003
278 -18.69341
279 -18.48790
280 -18.34089
281 -18.29323
282 -18.30497
283 -18.43666
284 -18.61797
285 -18.60708
286 -18.43232
287 -18.52444
288 -18.49835
289 -18.57880
290 -18.75085
291 -18.49215
292 -18.42925
293 -18.53333
294 -18.56984
295 -18.63471
296 -18.41717
297 -18.59519
298 -18.54435
299 -18.28258
300 -18.40680
301 -18.36291
302 -18.42603
303 -18.55981
304 -18.52015
305 -18.37120
306 -18.55596
307 -18.28547
308 -18.40836
309 -18.65226
310 -18.61289
311 -18.43413
312 -18.52635
313 -18.54614
314 -18.56894
315 -18.31371
316 -18.51645
317 -18.32150
318 -18.66754
319 -18.49166
320 -18.69413
321 -18.56968
322 -18.44213
323 -18.46532
324 -19.03777
325 -19.09668
326 -18.89942
327 -18.96406
328 -19.00603
329 -18.94299
330 -19.03710
331 -18.82789
332 -18.87806
333 -18.94004
334 -18.91585
335 -18.98645
336 -19.02657
337 -19.02439
338 -19.17656
339 -18.89593
340 -18.93099
341 -18.87111
342 -18.91457
343 -18.97711
344 -18.78962
345 -18.82320
346 -18.73404
347 -18.83874
348 -18.94685
349 -18.83226
350 -18.87138
351 -19.06153
352 -18.77251
353 -18.75565
354 -18.84792
355 -18.58629
356 -18.92479
357 -18.85516
358 -18.90037
359 -18.95523
360 -18.99122
361 -18.83410
362 -18.85444
363 -19.01973
364 -18.85385
365 -18.90850
366 -18.89995
367 -18.82364
368 -18.86227
369 -18.63982
370 -18.92882
371 -19.07304
372 -19.04269
373 -19.09667
374 -18.98685
375 -18.98981
376 -19.03552
377 -19.09054
378 -18.92813
379 -19.00304
380 -18.95620
381 -19.03597
382 -19.14526
383 -18.98334
384 -19.03651
385 -19.06646
386 -18.87711
387 -18.87007
388 -18.91577
389 -18.86383
390 -18.97963
391 -19.08574
392 -18.94814
393 -18.94542
394 -18.95603
395 -18.87285
396 -18.97898
397 -18.96803
398 -18.79838
399 -19.00079
400 -18.79013
401 -19.09309
402 -18.90237
403 -19.01696
404 -18.83954
405 -19.01611
406 -18.81755
407 -18.83572
408 -18.80634
409 -18.95873
410 -18.86237
411 -18.94898
412 -18.89680
413 -19.02679
414 -18.93862
415 -19.01711
416 -19.11762
417 -19.03121
418 -18.99412
419 -19.01857
420 -18.98234
421 -19.00953
422 -18.52701
423 -18.54135
424 -18.51234
425 -18.52819
426 -18.50523
427 -18.56657
428 -18.55133
429 -18.59171
430 -18.55681
431 -18.55629
432 -18.51516
433 -18.52933
434 -18.52981
435 -18.53554
436 -18.49783
437 -18.50914
438 -18.51228
439 -18.50478
440 -18.53160
441 -18.51788
442 -18.50636
443 -18.49799
444 -18.51342
445 -18.58623
446 -18.56733
447 -18.50805
448 -18.47972
449 -18.52722
450 -18.53037
451 -18.54204
452 -18.57502
453 -18.53719
454 -18.54748
455 -18.52410
456 -18.54790
457 -18.49698
458 -18.50276
459 -18.34165
460 -18.70409
461 -18.47139
462 -18.40855
463 -18.37509
464 -18.46809
465 -18.37177
466 -18.70250
467 -18.52702
468 -18.66880
469 -18.43333
470 -18.49988
471 -18.45190
472 -18.47176
473 -18.63276
474 -18.66036
475 -18.85598
476 -18.73111
477 -18.73334
478 -18.37327
479 -18.30780
480 -18.58974
481 -18.86239
482 -18.69415
483 -18.70917
484 -18.80586
485 -18.80653
486 -18.86694
487 -18.89169
488 -18.90010
489 -19.05296
490 -18.90212
491 -18.85842
492 -18.74311
493 -18.45299
494 -18.69929
495 -18.70984
496 -18.79524
497 -18.76522
498 -18.84851
499 -18.65508
500 -18.82308
501 -18.76565
502 -18.60557
503 -18.35325
504 -18.36515
505 -18.55096
506 -18.64446
507 -18.37701
508 -18.40391
509 -18.48332
510 -18.77117
511 -18.78267
512 -18.47648
513 -18.51333
514 -18.60795
515 -18.50907
516 -18.70680
517 -18.64340
518 -18.64426
519 -18.52088
520 -18.55816
521 -18.72173
522 -19.08257
523 -18.91321
524 -18.70815
525 -18.92419
526 -18.71989
527 -19.14698
528 -18.91188
529 -18.98002
530 -18.71171
531 -18.76990
532 -18.56142
533 -18.73211
534 -18.78975
535 -18.98665
536 -18.95015
537 -19.10700
538 -18.69811
539 -18.68557
540 -18.74043
541 -18.65848
542 -18.77775
543 -18.93457
544 -18.76153
545 -18.66717
546 -18.86312
547 -18.85503
548 -19.00015
549 -18.74501
550 -18.69191
551 -19.01749
552 -18.80701
553 -19.05215
554 -19.16699
555 -18.78914
556 -18.91482
557 -18.67589
558 -18.83257
559 -18.69924
560 -18.77014
561 -18.77061
562 -18.96712
563 -18.56781
564 -18.51037
565 -18.54690
566 -18.74433
567 -18.64866
568 -18.64427
569 -18.77977
570 -18.67792
571 -18.90751
572 -18.76047
> fitted(res)
             Run1
1    0.2386540498
2    0.2294265431
3    0.1073967203
4    0.1400585456
5    0.2380775296
6    0.1000078835
7   -0.0179771451
8    0.3064573391
9    0.2122208457
10   0.3442978865
11   0.3552330455
12   0.3748776228
13   0.2215071302
14   0.0069792129
15   0.1088252514
16   0.2815463097
17   0.0651998272
18   0.3908551488
19   0.2815226410
20  -0.0416674252
21   0.2313781034
22   0.0707633110
23   0.2495632514
24   0.3497791147
25   0.2772656863
26   0.1776924185
27   0.1254538362
28   0.1041619336
29   0.0994846247
30   0.2287597777
31   0.2546799034
32  -0.0269056455
33   0.1781355428
34   0.2406798421
35   0.0818768856
36   0.1016247372
37   0.1282279292
38   0.1550303717
39  -0.0377677205
40   0.2613347037
41   0.1672868503
42  -0.1678594614
43   0.0588421491
44   0.1246786971
45   0.1159283880
46   0.2544357097
47   0.0173853717
48  -0.0800192321
49  -0.1068173016
50  -0.0697389285
51   0.1375144923
52  -0.0449067864
53  -0.0484007993
54   0.0123338332
55  -0.1520356383
56   0.2498397024
57  -0.1775685499
58   0.0126974973
59   0.0119808126
60   0.1968735311
61   0.0800184452
62  -0.0709700304
63   0.0586868303
64   0.0853255602
65   0.0209214631
66   0.2233259928
67   0.1243293095
68   0.2444356762
69   0.4029068284
70   0.0628679985
71   0.1047349385
72   0.0268507508
73   0.1675606488
74   0.1358920029
75   0.0123559379
76   0.0571712123
77  -0.0733291937
78   0.2073031612
79   0.0036629030
80   0.0431614535
81   0.2263564514
82   0.2466078672
83   0.0946893746
84  -0.2766177990
85   0.1243148375
86   0.1687874550
87   0.1122181887
88  -0.2895394774
89   0.0137327144
90  -0.1764740215
91   0.0077222946
92  -0.1713581810
93   0.1862068858
94   0.0010757885
95   0.0151510120
96  -0.0556329099
97   0.2583582743
98  -0.0147655699
99   0.0929518675
100 -0.0332849095
101  0.1290496380
102  0.2073467317
103 -0.0895419926
104  0.0369759566
105  0.1026379420
106 -0.0215627698
107  0.1235841830
108  0.0671773851
109  0.1479488271
110  0.0793446953
111 -0.0065268144
112  0.0492816275
113  0.0646325053
114  0.1545592040
115  0.0145112326
116  0.1845875019
117 -0.0804803271
118  0.0313415204
119  0.0176976297
120  0.0515896951
121 -0.0388224795
122 -0.0441668175
123 -0.2593278860
124 -0.1723019686
125  0.0395886049
126 -0.0002439244
127  0.0280180372
128 -0.0613996956
129  0.1352979831
130  0.0171349830
131 -0.1746886256
132  0.1496640225
133 -0.1973605161
134  0.0062514896
135  0.1057048581
136  0.2307142552
137  0.1077473352
138  0.0261251566
139  0.1065528119
140 -0.1137859599
141 -0.0151746965
142  0.0771063395
143  0.0702889540
144  0.0067248587
145  0.0957115866
146 -0.0170851771
147 -0.0056884699
148 -0.1728144233
149 -0.0043462883
150  0.0707118274
151  0.0690463347
152 -0.1373631190
153 -0.0004107086
154 -0.0868972669
155  0.1013030804
156  0.1325524049
157  0.0013991076
158 -0.0312195443
159  0.0938480806
160  0.1542973908
161 -0.0299252547
162  0.0596890675
163 -0.0497124570
164  0.1666470759
165  0.1398671699
166  0.0746914874
167 -0.1141446225
168 -0.0739850788
169  0.0083583890
170  0.1724914586
171  0.3035899600
172  0.0676416637
173  0.1292518391
174 -0.0559737069
175  0.0884461069
176  0.0048873541
177 -0.1816243576
178 -0.0686478303
179 -0.1126967291
180 -0.1668043214
181  0.2213777564
182  0.1925528227
183  0.2066010474
184  0.2477564493
185  0.1171215860
186 -0.0172467488
187  0.1314470168
188 -0.2948394760
189  0.2851930869
190  0.2208148094
191 -0.0196801107
192  0.0099315635
193 -0.0182901252
194 -0.2337088200
195 -0.0137719089
196 -0.0852134705
197 -0.0268475936
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572 -0.0901687696
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>