predict.clogitboost: Predicting after fitting a boosting conditional logit model
In clogitboost: Boosting Conditional Logit Model
Description
Usage
Arguments
Value
Author(s)
See Also
Examples
View source: R/predict.clogitboost.R
Description
predict methods for the clogitboost objects, which produce marginal predictions of the covariate effects.
Usage
1
2
Arguments
object
output object from the clogitboost function.
x
new matrix or data frame with each column being a covariate.
strata
new vector of group memberships, i.e., items in the same group have the same value.
...
not currently used.
Value
The method predict returns the following list of values:
prob
probability of the outcome equal to 1.
utility
predicted utility.
prediction
0-1 prediction of the outcome variable.
Author(s)
Haolun Shi shl2003@connect.hku.hk
Guosheng Yin gyin@hku.hk
See Also
Examples
1
2
3
4
5
6
7
Example output
$prob
[1] 0.103380915 0.129816799 0.157400339 0.609401948 0.010503567 0.008162615
[7] 0.933347060 0.047986759 0.136851853 0.108892605 0.131523838 0.622731705
[13] 0.014465063 0.890131259 0.015989365 0.079414313 0.132062130 0.115128578
[19] 0.153951387 0.598857905 0.128627564 0.115584160 0.154560597 0.601227680
[25] 0.021018356 0.023781921 0.891327241 0.063872482 0.007899218 0.007956108
[31] 0.955884099 0.028260575 0.138639546 0.118147987 0.145546929 0.597665538
[37] 0.081118073 0.092075590 0.094064682 0.732741655 0.084674619 0.081030304
[43] 0.094862983 0.739432094 0.021551687 0.016049884 0.899705909 0.062692521
[49] 0.016686343 0.017108223 0.897217135 0.068988299 0.123716283 0.130146366
[55] 0.114895412 0.631241940 0.014355510 0.929700689 0.012532006 0.043411795
[61] 0.028275837 0.881478255 0.019670816 0.070575092 0.024408508 0.875458823
[67] 0.023931046 0.076201623 0.006757070 0.955116976 0.007022390 0.031103564
[73] 0.013343193 0.926336498 0.015299410 0.045020899 0.017499687 0.890450852
[79] 0.028913773 0.063135687 0.009650044 0.942617023 0.010073983 0.037658950
[85] 0.006736074 0.955490705 0.007037018 0.030736204 0.011372195 0.922731736
[91] 0.012544555 0.053351514 0.912561920 0.012760255 0.015042673 0.059635152
[97] 0.894918737 0.016816645 0.024647102 0.063617516 0.921622082 0.011139086
[103] 0.014996832 0.052242000 0.930893282 0.007804625 0.008493708 0.052808384
[109] 0.932652517 0.010943469 0.013759646 0.042644368 0.907982724 0.013884471
[115] 0.012496941 0.065635865 0.885491845 0.018936224 0.016802169 0.078769763
[121] 0.929311021 0.013422456 0.010757014 0.046509509 0.874417018 0.021149559
[127] 0.023582232 0.080851192 0.917165019 0.011542908 0.012371359 0.058920714
[133] 0.875653130 0.013704775 0.017009335 0.093632760 0.877410983 0.022563263
[139] 0.021494670 0.078531084 0.909342741 0.015951639 0.017730228 0.056975392
[145] 0.119926343 0.166620018 0.183023155 0.530430483 0.128712504 0.143443104
[151] 0.154300966 0.573543427 0.134816740 0.112254443 0.129217359 0.623711457
[157] 0.138951443 0.170457510 0.183745826 0.506845221 0.145322951 0.123551599
[163] 0.148093126 0.583032323 0.110617515 0.120364886 0.129194877 0.639822722
[169] 0.154551728 0.161525155 0.201782378 0.482140738 0.129998885 0.133896567
[175] 0.146266959 0.589837588 0.107743964 0.170471526 0.205716957 0.516067553
[181] 0.016708352 0.015471072 0.909028614 0.058791962 0.012412934 0.010408470
[187] 0.940886865 0.036291731 0.015055815 0.016420414 0.903243620 0.065280151
[193] 0.132408049 0.118781744 0.134295455 0.614514752 0.029026334 0.025101608
[199] 0.870029741 0.075842317 0.016920673 0.015073962 0.899032854 0.068972512
[205] 0.012741814 0.010544575 0.934579065 0.042134547 0.919000715 0.014356836
[211] 0.013786828 0.052855621 0.913204098 0.011132097 0.012494072 0.063169733
[217] 0.131796648 0.109832688 0.135077789 0.623292875 0.111215496 0.108910545
[223] 0.126934416 0.652939543 0.017671943 0.883839223 0.015076217 0.083412616
[229] 0.022981599 0.869025551 0.017062475 0.090930375 0.011778799 0.921096763
[235] 0.012602250 0.054522189 0.013070438 0.013401630 0.916627391 0.056900540
[241] 0.904552738 0.018317158 0.017813782 0.059316322 0.922758675 0.010955242
[247] 0.012808772 0.053477311 0.122350836 0.099246453 0.119872920 0.658529791
[253] 0.100861179 0.121758456 0.126997820 0.650382546 0.161875801 0.119109039
[259] 0.156979065 0.562036095 0.912176152 0.015098937 0.015945369 0.056779542
[265] 0.902338491 0.018354210 0.019471926 0.059835374 0.016077543 0.017317803
[271] 0.895266328 0.071338325 0.010574760 0.929827642 0.009294416 0.050303182
[277] 0.016319256 0.895000085 0.016797202 0.071883456 0.121738705 0.122319285
[283] 0.118957815 0.636984195 0.905254850 0.017618915 0.018534169 0.058592065
[289] 0.906073924 0.010848959 0.012176292 0.070900826 0.919106528 0.009996297
[295] 0.011145561 0.059751614 0.012602592 0.014331110 0.899773612 0.073292686
[301] 0.163416643 0.142803511 0.170330919 0.523448927 0.022679873 0.017625534
[307] 0.882594576 0.077100017 0.012653395 0.011043153 0.924780185 0.051523267
[313] 0.012257530 0.011417219 0.927664336 0.048660915 0.085963134 0.107931471
[319] 0.121723020 0.684382375 0.902442155 0.018141967 0.019084392 0.060331485
[325] 0.016130800 0.013523036 0.906190217 0.064155947 0.126178996 0.158424712
[331] 0.178668319 0.536727973 0.145024176 0.137513662 0.146137400 0.571324762
$utility
505 506 507 508 509 510 511
-1.4063981 -1.1786943 -0.9860260 0.3676596 -1.1928095 -1.4449598 3.2942528
512 513 514 515 516 517 518
0.3264007 -0.9593091 -1.1878459 -0.9990199 0.5559077 -1.4065042 2.7131284
519 520 521 522 523 524 525
-1.3063167 0.2964381 -1.2668720 -1.4040949 -1.1135076 0.2448798 -1.2971727
526 527 528 529 530 531 532
-1.4040949 -1.1135076 0.2448798 -0.9856948 -0.8621654 2.7616206 0.1257976
533 534 535 536 537 538 539
-1.1401519 -1.1329759 3.6557209 0.1345520 -1.2162741 -1.3762135 -1.1676529
540 541 542 543 544 545 546
0.2448798 -1.2566870 -1.1299829 -1.1086101 0.9442004 -1.1227773 -1.1667700
547 548 549 550 551 552 553
-1.0091597 1.0442892 -0.8942539 -1.1890064 2.8373599 0.1735342 -0.9762139
554 555 556 557 558 559 560
-0.9512453 3.0084934 0.4431324 -1.2523203 -1.2016515 -1.3262889 0.3773780
561 562 563 564 565 566 567
-0.8764761 3.2942528 -1.0123241 0.2301212 -0.5906372 2.8489556 -0.9535087
568 569 570 571 572 573 574
0.3240325 -0.9942388 2.5855775 -1.0139940 0.1442122 -1.3335492 3.6176952
575 576 577 578 579 580 581
-1.2950351 0.1931838 -0.9805806 3.2596505 -0.8437728 0.2355398 -1.1219466
582 583 584 585 586 587 588
2.8075983 -0.6198116 0.1611565 -1.0474998 3.5341979 -1.0045061 0.3141084
589 590 591 592 593 594 595
-1.3623895 3.5923583 -1.3186822 0.1555745 -1.3980530 2.9981142 -1.2999376
596 597 598 599 600 601 602
0.1476781 3.1048752 -1.1650455 -1.0004897 0.3768645 2.8206751 -1.1536887
603 604 605 606 607 608 609
-0.7713985 0.1768310 3.2755039 -1.1401712 -0.8427924 0.4052554 3.3657723
610 611 612 613 614 615 616
-1.4156558 -1.3310466 0.4962976 3.3032160 -1.1420738 -0.9130766 0.2180785
617 618 619 620 621 622 623
2.9290336 -1.2514207 -1.3567078 0.3019306 2.6111115 -1.2339551 -1.3535237
624 625 626 627 628 629 630
0.1914974 3.1249428 -1.1125716 -1.3339427 0.1301561 2.8376865 -0.8842518
631 632 633 634 635 636 637
-0.7753773 0.4567395 2.9950213 -1.3801949 -1.3108821 0.2499266 2.7331733
638 639 640 641 642 643 644
-1.4240524 -1.2080344 0.4975836 2.7646208 -0.8960316 -0.9445497 0.3511398
645 646 647 648 649 650 651
2.9330001 -1.1101604 -1.0044510 0.1628975 -1.1525825 -0.8237443 -0.7298475
652 653 654 655 656 657 658
0.3342287 -1.3649099 -1.2565527 -1.1835861 0.1293425 -1.2143794 -1.3975277
659 660 661 662 663 664 665
-1.2567999 0.3173921 -0.9167031 -0.7123416 -0.6372742 0.3773780 -1.2089653
666 667 668 669 670 671 672
-1.3712650 -1.1900825 0.1803188 -1.4208840 -1.3364346 -1.2656405 0.3342287
673 674 675 676 677 678 679
-0.9895441 -0.9454120 -0.7228831 0.1481631 -1.3649099 -1.3353681 -1.2470023
680 681 682 683 684 685 686
0.1474115 -1.1801661 -0.7213555 -0.5334225 0.3863139 -1.0951990 -1.1721357
687 688 689 690 691 692 693
2.9012689 0.1628975 -0.9382731 -1.1143922 3.3898108 0.1345778 -1.3324043
694 695 696 697 698 699 700
-1.2456433 2.7618237 0.1345194 -1.0057915 -1.1143922 -0.9916376 0.5291530
701 702 703 704 705 706 707
-0.7105985 -0.8558701 2.6897254 0.2498544 -0.8679816 -0.9835489 3.1048018
708 709 710 711 712 713 714
0.5371903 -0.9216951 -1.1109726 3.3735122 0.2742839 2.9950213 -1.1640394
715 716 717 718 719 720 721
-1.2045519 0.1392984 3.0274975 -1.3796294 -1.2642077 0.3563633 -0.9978486
722 723 724 725 726 727 728
-1.1801506 -0.9732580 0.5559077 -1.1034535 -1.1243964 -0.9712527 0.6665613
729 730 731 732 733 734 735
-1.1978481 2.7144488 -1.3567078 0.3539732 -0.9942388 2.6384399 -1.2920511
736 737 738 739 740 741 742
0.3811614 -1.3408091 3.0184548 -1.2732350 0.1914974 -1.3380691 -1.3130459
743 744 745 746 747 748 749
2.9122788 0.1328826 2.9160535 -0.9835489 -1.0114146 0.1914974 3.0110077
750 751 752 753 754 755 756
-1.4225420 -1.2662298 0.1628975 -0.9785595 -1.1878459 -0.9990199 0.7045576
757 758 759 760 761 762 763
-1.0951990 -0.9069049 -0.8647742 0.7686166 -0.8250104 -1.1318004 -0.8557273
764 765 766 767 768 769 770
0.4197263 3.0489250 -1.0522838 -0.9977397 0.2722680 2.9048901 -0.9902406
771 772 773 774 775 776 777
-0.9311259 0.1914974 -1.3145655 -1.2402540 2.7051322 0.1754447 -1.1475984
778 779 780 781 782 783 784
3.3289309 -1.2766545 0.4119999 -1.2579987 2.7464793 -1.2291322 0.2247016
785 786 787 788 789 790 791
-0.9883066 -0.9835489 -1.0114146 0.6665613 2.8969539 -1.0422896 -0.9916466
792 793 794 795 796 797 798
0.1593367 3.0454224 -1.3796294 -1.2642077 0.4975836 3.1034744 -1.4177129
799 800 801 802 803 804 805
-1.3088863 0.3702686 -1.2547089 -1.1261786 3.0135318 0.5058495 -1.0057915
806 807 808 809 810 811 812
-1.1406249 -0.9643514 0.1583450 -0.9216951 -1.1738243 2.7396930 0.3019306
813 814 815 816 817 818 819
-1.0529681 -1.1890831 3.2386624 0.3511398 -1.0547032 -1.1257210 3.2718263
820 821 822 823 824 825 826
0.3240325 -1.1303978 -0.9028199 -0.7825682 0.9442004 2.8645872 -1.0422896
827 828 829 830 831 832 833
-0.9916466 0.1593367 -0.8246786 -1.0010145 3.2038402 0.5559077 -1.1303978
834 835 836 837 838 839 840
-0.9028199 -0.7825682 0.3173921 -1.2127124 -1.2658896 -1.2050656 0.1583450
$prediction
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clogitboost documentation built on May 2, 2019, 6:28 a.m.
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Description Usage Arguments Value Author(s) See Also Examples
View source: R/predict.clogitboost.R
Description
predict methods for the clogitboost objects, which produce marginal predictions of the covariate effects.
Usage
1 2 |
Arguments
object |
output object from the |
x |
new matrix or data frame with each column being a covariate. |
strata |
new vector of group memberships, i.e., items in the same group have the same value. |
... |
not currently used. |
Value
The method predict returns the following list of values:
prob |
probability of the outcome equal to 1. |
utility |
predicted utility. |
prediction |
0-1 prediction of the outcome variable. |
Author(s)
Haolun Shi shl2003@connect.hku.hk
Guosheng Yin gyin@hku.hk
See Also
Examples
1 2 3 4 5 6 7 |
Example output
$prob
[1] 0.103380915 0.129816799 0.157400339 0.609401948 0.010503567 0.008162615
[7] 0.933347060 0.047986759 0.136851853 0.108892605 0.131523838 0.622731705
[13] 0.014465063 0.890131259 0.015989365 0.079414313 0.132062130 0.115128578
[19] 0.153951387 0.598857905 0.128627564 0.115584160 0.154560597 0.601227680
[25] 0.021018356 0.023781921 0.891327241 0.063872482 0.007899218 0.007956108
[31] 0.955884099 0.028260575 0.138639546 0.118147987 0.145546929 0.597665538
[37] 0.081118073 0.092075590 0.094064682 0.732741655 0.084674619 0.081030304
[43] 0.094862983 0.739432094 0.021551687 0.016049884 0.899705909 0.062692521
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