summary.QuantifQuantile: Summary of QuantifQuantile results
In QuantifQuantile: Estimation of Conditional Quantiles using Optimal Quantization
Description
Usage
Arguments
Details
Author(s)
References
See Also
Examples
Description
This function displays a summary of QuantifQuantile results.
Usage
1
2
Arguments
object
An object of class QuantifQuantile, which is the result
of the
QuantifQuantile, QuantifQuantile.d2 or
QuantifQuantile.d functions.
...
Not used.
Details
This function prints the estimated conditional quantiles q_alpha(x) for each
x and alpha considered, as an array, and also the selected tuning parameter N_opt.
Author(s)
Isabelle Charlier, Davy Paindaveine, Jerome Saracco
References
Charlier, I. and Paindaveine, D. and Saracco, J.,
Conditional quantile estimation through optimal quantization,
Journal of Statistical Planning and Inference, 2015 (156), 14-30.
Charlier, I. and Paindaveine, D. and Saracco, J.,
Conditional quantile estimator based on optimal
quantization: from theory to practice, Submitted.
See Also
QuantifQuantile, QuantifQuantile.d2 and
QuantifQuantile.d
plot.QuantifQuantile, print.QuantifQuantile
Examples
1
2
3
4
5
6
Example output
Loading required package: rgl
Loading required package: parallel
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
[1] 10
[1] 15
[1] 20
[1] 25
** Resulting estimated conditional quantiles with N_opt= 20 **
For each x, the corresponding estimated q_alpha(x) for each alpha
1 2 3 4 5 6 7
x -1.982839 -1.942640 -1.902441 -1.862242 -1.822042 -1.781843 -1.741644
0.05 2.073442 2.016997 1.838284 1.874447 1.907095 1.880806 1.627620
0.25 2.885480 2.846911 2.760203 2.750845 2.728004 2.630250 2.404685
0.5 3.316173 3.275618 3.238437 3.261051 3.258058 3.164712 2.963722
0.75 3.747009 3.691835 3.608203 3.603696 3.616332 3.530881 3.347815
0.95 4.516303 4.455373 4.364542 4.297824 4.303351 4.164509 3.983568
8 9 10 11 12 13 14
x -1.701445 -1.661246 -1.6210473 -1.5808482 -1.5406492 -1.5004501 -1.4602511
0.05 1.499475 1.027641 0.8063486 0.5319739 0.2895155 0.2735656 0.2721411
0.25 2.286811 1.920373 1.7789106 1.6995945 1.6059918 1.5712670 1.5313198
0.5 2.852250 2.553772 2.4214459 2.3254008 2.2035008 2.1084861 2.0205683
0.75 3.268789 3.066780 2.9406310 2.9031772 2.8168377 2.7792500 2.7434251
0.95 3.905396 3.706993 3.6123792 3.6061754 3.5611459 3.5384172 3.4814236
15 16 17 18 19 20
x -1.4200521 -1.3798530 -1.3396540 -1.2994549 -1.2592558763 -1.2190568
0.05 0.3631543 0.3347247 0.2272246 0.1246124 -0.0008368414 -0.1014641
0.25 1.5318877 1.3676195 1.3087387 1.2330631 1.0934210794 0.9593931
0.5 1.9413705 1.7879808 1.7385044 1.7343682 1.7191749412 1.6106596
0.75 2.6307476 2.4475539 2.3961000 2.4301675 2.4591320530 2.3564410
0.95 3.3461431 3.1176411 3.0080061 2.9475506 2.9186605821 2.8526262
21 22 23 24 25 26
x -1.17885779 -1.1386587 -1.09845970 -1.0582607 -1.0180616 -0.9778626
0.05 -0.05370253 -0.1261913 -0.01878695 0.1937150 0.2280442 0.2774945
0.25 0.96607077 0.7900010 0.78464495 0.8621614 0.8822595 0.9248382
0.5 1.60293384 1.4529438 1.44105230 1.4541162 1.4498567 1.4927249
0.75 2.32434075 2.1359372 2.10880254 2.0500029 1.9740874 1.9745245
0.95 2.83196403 2.7155204 2.74577729 2.7494390 2.6826364 2.6155439
27 28 29 30 31 32
x -0.9376635 -0.897464481 -0.85726544 -0.8170664 -0.7768673 -0.7366683
0.05 0.1774933 0.009020793 -0.05736146 -0.1222264 -0.2962282 -0.4671046
0.25 0.8725182 0.749461928 0.70146016 0.6344711 0.4844861 0.3306321
0.5 1.4959450 1.396387831 1.35828317 1.2808414 1.0589716 0.8707602
0.75 1.9455031 1.833570201 1.79991844 1.7359003 1.5683873 1.4217363
0.95 2.5798650 2.514652458 2.50402573 2.4383499 2.2982100 2.1954053
33 34 35 36 37 38
x -0.6964693 -0.6562702 -0.61607117 -0.57587213 -0.5356731 -0.49547404
0.05 -0.6470038 -0.5997100 -0.64054298 -0.70834296 -0.7635236 -0.94316706
0.25 0.1732819 0.1769241 0.07153284 -0.09459752 -0.1757845 -0.30015463
0.5 0.6390646 0.6289867 0.48351363 0.27815013 0.1625115 0.08995035
0.75 1.2417973 1.1876065 1.02021454 0.74763456 0.5862331 0.55230250
0.95 2.0973347 2.0828945 1.85087516 1.48016379 1.2643496 1.19140882
39 40 41 42 43 44
x -0.45527500 -0.4150760 -0.3748769 -0.3346779 -0.2944788 -0.2542798
0.05 -0.88974412 -0.8303315 -0.8127110 -0.8819343 -1.0365107 -0.9562091
0.25 -0.34390223 -0.3370216 -0.2829101 -0.2228491 -0.2278612 -0.2516464
0.5 0.09109088 0.1375912 0.2057875 0.2558122 0.3220705 0.2706071
0.75 0.59908629 0.7050269 0.7658796 0.7562753 0.8196468 0.7648185
0.95 1.20882119 1.2567676 1.3150183 1.4092108 1.5866155 1.4734743
45 46 47 48 49 50
x -0.2140807 -0.17388169 -0.1336826 -0.09348360 -0.05328456 -0.01308552
0.05 -1.0015684 -1.09053908 -1.1241774 -1.11047483 -1.07986573 -1.05339976
0.25 -0.3783501 -0.52380504 -0.5565375 -0.54824005 -0.53077774 -0.49849880
0.5 0.1726129 0.05097388 0.1222979 0.08670777 0.11376053 0.12867365
0.75 0.6926809 0.64182341 0.7557388 0.71747821 0.64185257 0.58518259
0.95 1.4519685 1.40186601 1.5598376 1.53113198 1.52140524 1.53263073
51 52 53 54 55 56
x 0.02711353 0.06731257 0.1075116 0.1477107 0.18790970 0.22810875
0.05 -1.07824236 -1.16729127 -1.2323386 -1.3644766 -1.44419556 -1.46187514
0.25 -0.55287840 -0.64427630 -0.7095790 -0.8460259 -0.92306623 -0.91322817
0.5 0.06096504 -0.05662394 -0.1504331 -0.3097379 -0.39706769 -0.36796686
0.75 0.56907200 0.45919348 0.3288468 0.1475327 0.06444451 0.06930667
0.95 1.56097231 1.46308706 1.2899706 1.0422682 0.90199445 0.85844399
57 58 59 60 61 62
x 0.2683078 0.3085068 0.3487059 0.3889049 0.42910397 0.46930301
0.05 -1.4630912 -1.4498440 -1.5393949 -1.6060281 -1.58632384 -1.53437871
0.25 -0.8699728 -0.8157608 -0.9228247 -0.9809836 -0.86260493 -0.76277621
0.5 -0.3007789 -0.2364801 -0.2263595 -0.1938986 -0.02259364 0.05507415
0.75 0.1305363 0.2637317 0.4069769 0.5003417 0.67562938 0.81479606
0.95 0.8199553 0.9089707 1.0871109 1.1255719 1.21237860 1.29004600
63 64 65 66 67 68
x 0.5095021 0.5497011 0.5899001 0.6300992 0.6702982 0.7104973
0.05 -1.3137674 -1.2087880 -1.0346896 -0.9801638 -1.0628142 -1.0077728
0.25 -0.4894396 -0.3799444 -0.2320515 -0.1389304 -0.1509979 -0.1536646
0.5 0.1782080 0.2729945 0.3231562 0.3438685 0.3664798 0.3242052
0.75 0.9249859 1.0028536 1.1117069 1.1228258 1.0405881 0.9572565
0.95 1.4037654 1.4654436 1.5690313 1.5927808 1.5314917 1.4485229
69 70 71 72 73 74
x 0.7506963 0.79089536 0.831094405 0.87129345 0.91149249 0.95169154
0.05 -1.0091257 -0.97200251 -1.061287928 -1.26735256 -1.42090567 -1.37566627
0.25 -0.1592860 -0.09008406 -0.007522209 0.02425824 0.01538087 0.07140737
0.5 0.3547409 0.42794217 0.511569623 0.58054959 0.61559177 0.69412867
0.75 0.9209554 0.94390550 0.962056208 0.93366883 0.92937094 1.00706389
0.95 1.3982447 1.39958434 1.413626688 1.43609943 1.51141388 1.67750194
75 76 77 78 79 80
x 0.99189058 1.03208962 1.07228867 1.1124877 1.1526868 1.192886
0.05 -1.35118320 -1.40146074 -1.44825019 -1.3569132 -0.8911393 -0.592594
0.25 0.08983016 0.04123767 0.06344461 0.1688769 0.4802462 0.646330
0.5 0.77300639 0.78484065 0.84341250 0.9553236 1.1913669 1.341238
0.75 1.16228455 1.23727827 1.39025495 1.5745664 1.8457088 2.059856
0.95 1.93680884 2.06172637 2.25715524 2.4769725 2.7612778 2.994045
81 82 83 84 85 86 87
x 1.2330848 1.2732839 1.313482932 1.3536820 1.3938810 1.4340801 1.4742791
0.05 -0.2819574 -0.1286109 0.005376222 0.1437243 0.2320959 0.1956525 0.1246165
0.25 0.7630284 0.8270255 0.879744306 0.9336208 1.0469743 1.0960082 1.1006788
0.5 1.4694023 1.5286340 1.537690250 1.5613117 1.7129587 1.8475697 1.9437977
0.75 2.1745513 2.2075163 2.231534348 2.2481004 2.4082588 2.5054420 2.6591972
0.95 3.1075898 3.0959554 3.057476136 2.9578844 3.0618764 3.1245822 3.3672885
88 89 90 91 92 93 94 95
x 1.5144782 1.5546772 1.5948762 1.635075 1.675274 1.715473 1.755672 1.795871
0.05 0.3823492 0.8347933 0.9199263 1.020404 1.266325 1.590147 1.667652 1.785647
0.25 1.2379533 1.5859742 1.6662441 1.750063 1.974370 2.270476 2.331200 2.450511
0.5 2.1376688 2.4888034 2.5514714 2.598437 2.815066 3.070853 3.083183 3.142014
0.75 2.8263744 3.1773027 3.2804853 3.376590 3.591073 3.769073 3.763078 3.799006
0.95 3.5412114 3.9730090 4.1259315 4.285650 4.685605 5.197118 5.324819 5.612585
96 97 98 99 100
x 1.836071 1.876270 1.916469 1.956668 1.996867
0.05 1.784780 1.772409 1.645921 1.616403 1.691950
0.25 2.445582 2.433142 2.313590 2.295370 2.376708
0.5 3.134985 3.130216 3.092351 3.057890 3.149383
0.75 3.798789 3.788421 3.799435 3.741095 3.830514
0.95 5.619304 5.637230 5.793325 5.710625 5.770756
QuantifQuantile documentation built on May 2, 2019, 2:10 a.m.
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Description Usage Arguments Details Author(s) References See Also Examples
Description
This function displays a summary of QuantifQuantile results.
Usage
1 2 |
Arguments
object |
An object of class |
... |
Not used. |
Details
This function prints the estimated conditional quantiles q_alpha(x) for each
x and alpha considered, as an array, and also the selected tuning parameter N_opt.
Author(s)
Isabelle Charlier, Davy Paindaveine, Jerome Saracco
References
Charlier, I. and Paindaveine, D. and Saracco, J., Conditional quantile estimation through optimal quantization, Journal of Statistical Planning and Inference, 2015 (156), 14-30.
Charlier, I. and Paindaveine, D. and Saracco, J., Conditional quantile estimator based on optimal quantization: from theory to practice, Submitted.
See Also
QuantifQuantile, QuantifQuantile.d2 and
QuantifQuantile.d
plot.QuantifQuantile, print.QuantifQuantile
Examples
1 2 3 4 5 6 |
Example output
Loading required package: rgl
Loading required package: parallel
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
[1] 10
[1] 15
[1] 20
[1] 25
** Resulting estimated conditional quantiles with N_opt= 20 **
For each x, the corresponding estimated q_alpha(x) for each alpha
1 2 3 4 5 6 7
x -1.982839 -1.942640 -1.902441 -1.862242 -1.822042 -1.781843 -1.741644
0.05 2.073442 2.016997 1.838284 1.874447 1.907095 1.880806 1.627620
0.25 2.885480 2.846911 2.760203 2.750845 2.728004 2.630250 2.404685
0.5 3.316173 3.275618 3.238437 3.261051 3.258058 3.164712 2.963722
0.75 3.747009 3.691835 3.608203 3.603696 3.616332 3.530881 3.347815
0.95 4.516303 4.455373 4.364542 4.297824 4.303351 4.164509 3.983568
8 9 10 11 12 13 14
x -1.701445 -1.661246 -1.6210473 -1.5808482 -1.5406492 -1.5004501 -1.4602511
0.05 1.499475 1.027641 0.8063486 0.5319739 0.2895155 0.2735656 0.2721411
0.25 2.286811 1.920373 1.7789106 1.6995945 1.6059918 1.5712670 1.5313198
0.5 2.852250 2.553772 2.4214459 2.3254008 2.2035008 2.1084861 2.0205683
0.75 3.268789 3.066780 2.9406310 2.9031772 2.8168377 2.7792500 2.7434251
0.95 3.905396 3.706993 3.6123792 3.6061754 3.5611459 3.5384172 3.4814236
15 16 17 18 19 20
x -1.4200521 -1.3798530 -1.3396540 -1.2994549 -1.2592558763 -1.2190568
0.05 0.3631543 0.3347247 0.2272246 0.1246124 -0.0008368414 -0.1014641
0.25 1.5318877 1.3676195 1.3087387 1.2330631 1.0934210794 0.9593931
0.5 1.9413705 1.7879808 1.7385044 1.7343682 1.7191749412 1.6106596
0.75 2.6307476 2.4475539 2.3961000 2.4301675 2.4591320530 2.3564410
0.95 3.3461431 3.1176411 3.0080061 2.9475506 2.9186605821 2.8526262
21 22 23 24 25 26
x -1.17885779 -1.1386587 -1.09845970 -1.0582607 -1.0180616 -0.9778626
0.05 -0.05370253 -0.1261913 -0.01878695 0.1937150 0.2280442 0.2774945
0.25 0.96607077 0.7900010 0.78464495 0.8621614 0.8822595 0.9248382
0.5 1.60293384 1.4529438 1.44105230 1.4541162 1.4498567 1.4927249
0.75 2.32434075 2.1359372 2.10880254 2.0500029 1.9740874 1.9745245
0.95 2.83196403 2.7155204 2.74577729 2.7494390 2.6826364 2.6155439
27 28 29 30 31 32
x -0.9376635 -0.897464481 -0.85726544 -0.8170664 -0.7768673 -0.7366683
0.05 0.1774933 0.009020793 -0.05736146 -0.1222264 -0.2962282 -0.4671046
0.25 0.8725182 0.749461928 0.70146016 0.6344711 0.4844861 0.3306321
0.5 1.4959450 1.396387831 1.35828317 1.2808414 1.0589716 0.8707602
0.75 1.9455031 1.833570201 1.79991844 1.7359003 1.5683873 1.4217363
0.95 2.5798650 2.514652458 2.50402573 2.4383499 2.2982100 2.1954053
33 34 35 36 37 38
x -0.6964693 -0.6562702 -0.61607117 -0.57587213 -0.5356731 -0.49547404
0.05 -0.6470038 -0.5997100 -0.64054298 -0.70834296 -0.7635236 -0.94316706
0.25 0.1732819 0.1769241 0.07153284 -0.09459752 -0.1757845 -0.30015463
0.5 0.6390646 0.6289867 0.48351363 0.27815013 0.1625115 0.08995035
0.75 1.2417973 1.1876065 1.02021454 0.74763456 0.5862331 0.55230250
0.95 2.0973347 2.0828945 1.85087516 1.48016379 1.2643496 1.19140882
39 40 41 42 43 44
x -0.45527500 -0.4150760 -0.3748769 -0.3346779 -0.2944788 -0.2542798
0.05 -0.88974412 -0.8303315 -0.8127110 -0.8819343 -1.0365107 -0.9562091
0.25 -0.34390223 -0.3370216 -0.2829101 -0.2228491 -0.2278612 -0.2516464
0.5 0.09109088 0.1375912 0.2057875 0.2558122 0.3220705 0.2706071
0.75 0.59908629 0.7050269 0.7658796 0.7562753 0.8196468 0.7648185
0.95 1.20882119 1.2567676 1.3150183 1.4092108 1.5866155 1.4734743
45 46 47 48 49 50
x -0.2140807 -0.17388169 -0.1336826 -0.09348360 -0.05328456 -0.01308552
0.05 -1.0015684 -1.09053908 -1.1241774 -1.11047483 -1.07986573 -1.05339976
0.25 -0.3783501 -0.52380504 -0.5565375 -0.54824005 -0.53077774 -0.49849880
0.5 0.1726129 0.05097388 0.1222979 0.08670777 0.11376053 0.12867365
0.75 0.6926809 0.64182341 0.7557388 0.71747821 0.64185257 0.58518259
0.95 1.4519685 1.40186601 1.5598376 1.53113198 1.52140524 1.53263073
51 52 53 54 55 56
x 0.02711353 0.06731257 0.1075116 0.1477107 0.18790970 0.22810875
0.05 -1.07824236 -1.16729127 -1.2323386 -1.3644766 -1.44419556 -1.46187514
0.25 -0.55287840 -0.64427630 -0.7095790 -0.8460259 -0.92306623 -0.91322817
0.5 0.06096504 -0.05662394 -0.1504331 -0.3097379 -0.39706769 -0.36796686
0.75 0.56907200 0.45919348 0.3288468 0.1475327 0.06444451 0.06930667
0.95 1.56097231 1.46308706 1.2899706 1.0422682 0.90199445 0.85844399
57 58 59 60 61 62
x 0.2683078 0.3085068 0.3487059 0.3889049 0.42910397 0.46930301
0.05 -1.4630912 -1.4498440 -1.5393949 -1.6060281 -1.58632384 -1.53437871
0.25 -0.8699728 -0.8157608 -0.9228247 -0.9809836 -0.86260493 -0.76277621
0.5 -0.3007789 -0.2364801 -0.2263595 -0.1938986 -0.02259364 0.05507415
0.75 0.1305363 0.2637317 0.4069769 0.5003417 0.67562938 0.81479606
0.95 0.8199553 0.9089707 1.0871109 1.1255719 1.21237860 1.29004600
63 64 65 66 67 68
x 0.5095021 0.5497011 0.5899001 0.6300992 0.6702982 0.7104973
0.05 -1.3137674 -1.2087880 -1.0346896 -0.9801638 -1.0628142 -1.0077728
0.25 -0.4894396 -0.3799444 -0.2320515 -0.1389304 -0.1509979 -0.1536646
0.5 0.1782080 0.2729945 0.3231562 0.3438685 0.3664798 0.3242052
0.75 0.9249859 1.0028536 1.1117069 1.1228258 1.0405881 0.9572565
0.95 1.4037654 1.4654436 1.5690313 1.5927808 1.5314917 1.4485229
69 70 71 72 73 74
x 0.7506963 0.79089536 0.831094405 0.87129345 0.91149249 0.95169154
0.05 -1.0091257 -0.97200251 -1.061287928 -1.26735256 -1.42090567 -1.37566627
0.25 -0.1592860 -0.09008406 -0.007522209 0.02425824 0.01538087 0.07140737
0.5 0.3547409 0.42794217 0.511569623 0.58054959 0.61559177 0.69412867
0.75 0.9209554 0.94390550 0.962056208 0.93366883 0.92937094 1.00706389
0.95 1.3982447 1.39958434 1.413626688 1.43609943 1.51141388 1.67750194
75 76 77 78 79 80
x 0.99189058 1.03208962 1.07228867 1.1124877 1.1526868 1.192886
0.05 -1.35118320 -1.40146074 -1.44825019 -1.3569132 -0.8911393 -0.592594
0.25 0.08983016 0.04123767 0.06344461 0.1688769 0.4802462 0.646330
0.5 0.77300639 0.78484065 0.84341250 0.9553236 1.1913669 1.341238
0.75 1.16228455 1.23727827 1.39025495 1.5745664 1.8457088 2.059856
0.95 1.93680884 2.06172637 2.25715524 2.4769725 2.7612778 2.994045
81 82 83 84 85 86 87
x 1.2330848 1.2732839 1.313482932 1.3536820 1.3938810 1.4340801 1.4742791
0.05 -0.2819574 -0.1286109 0.005376222 0.1437243 0.2320959 0.1956525 0.1246165
0.25 0.7630284 0.8270255 0.879744306 0.9336208 1.0469743 1.0960082 1.1006788
0.5 1.4694023 1.5286340 1.537690250 1.5613117 1.7129587 1.8475697 1.9437977
0.75 2.1745513 2.2075163 2.231534348 2.2481004 2.4082588 2.5054420 2.6591972
0.95 3.1075898 3.0959554 3.057476136 2.9578844 3.0618764 3.1245822 3.3672885
88 89 90 91 92 93 94 95
x 1.5144782 1.5546772 1.5948762 1.635075 1.675274 1.715473 1.755672 1.795871
0.05 0.3823492 0.8347933 0.9199263 1.020404 1.266325 1.590147 1.667652 1.785647
0.25 1.2379533 1.5859742 1.6662441 1.750063 1.974370 2.270476 2.331200 2.450511
0.5 2.1376688 2.4888034 2.5514714 2.598437 2.815066 3.070853 3.083183 3.142014
0.75 2.8263744 3.1773027 3.2804853 3.376590 3.591073 3.769073 3.763078 3.799006
0.95 3.5412114 3.9730090 4.1259315 4.285650 4.685605 5.197118 5.324819 5.612585
96 97 98 99 100
x 1.836071 1.876270 1.916469 1.956668 1.996867
0.05 1.784780 1.772409 1.645921 1.616403 1.691950
0.25 2.445582 2.433142 2.313590 2.295370 2.376708
0.5 3.134985 3.130216 3.092351 3.057890 3.149383
0.75 3.798789 3.788421 3.799435 3.741095 3.830514
0.95 5.619304 5.637230 5.793325 5.710625 5.770756
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