BP based models residuals.

Residuals for a fitted spbp model.

# S3 method for spbp
residuals(object, type = c("cox-snell"), ...)

Arguments

object	an object of class `spbp` result of a `spbp` fit.
type	type of residuals, default is "cox-snell"
...	further arguments passed to or from other methods

Examples


library("spsurv")
data("veteran")

fit <- bpph(Surv(time, status) ~ karno + factor(celltype),
data = veteran)
#> Priors are ignored due to mle approach.

residuals(fit)
#>           time    survival1
#> 77   0.0000000  0.008047894
#> 85   0.0000000  0.008047894
#> 95  -0.6931472  0.016154046
#> 53  -1.0986123  0.024317778
#> 18  -1.3862944  0.032538408
#> 42  -1.9459101  0.057534783
#> 97  -1.9459101  0.057534783
#> 119 -1.9459101  0.057534783
#> 12  -2.0794415  0.065976082
#> 46  -2.0794415  0.065976082
#> 100 -2.0794415  0.065976082
#> 116 -2.0794415  0.065976082
#> 6   -2.3025851  0.083018223
#> 45  -2.3025851  0.083018223
#> 15  -2.3978953  0.091617646
#> 51  -2.4849066  0.100268353
#> 60  -2.4849066  0.100268353
#> 20  -2.5649494  0.108969626
#> 93  -2.5649494  0.108969626
#> 89  -2.7080502  0.126520990
#> 130 -2.7080502  0.126520990
#> 26  -2.7725887  0.135369634
#> 31  -2.8903718  0.153209212
#> 37  -2.8903718  0.153209212
#> 109 -2.8903718  0.153209212
#> 123 -2.9444390  0.162198687
#> 128 -2.9444390  0.162198687
#> 33  -2.9957323  0.171233645
#> 96  -2.9957323  0.171233645
#> 30  -3.0445224  0.180313352
#> 92  -3.0445224  0.180313352
#> 28  -3.0910425  0.189437074
#> 98  -3.1780538  0.207813624
#> 108 -3.1780538  0.207813624
#> 14  -3.2188758  0.217064980
#> 81  -3.2188758  0.217064980
#> 90  -3.2188758  0.217064980
#> 103 -3.2188758  0.217064980
#> 40  -3.2958369  0.235690171
#> 107 -3.3672958  0.254473759
#> 16  -3.4011974  0.263923112
#> 86  -3.4011974  0.263923112
#> 34  -3.4339872  0.273409856
#> 111 -3.4339872  0.273409856
#> 80  -3.4965076  0.292492584
#> 48  -3.5553481  0.311716079
#> 117 -3.5835189  0.321378788
#> 11  -3.7376696  0.380022766
#> 131 -3.7612001  0.389901322
#> 87  -3.7841896  0.399807844
#> 124 -3.8066625  0.409741624
#> 118 -3.8712010  0.439699459
#> 137 -3.8918203  0.449735237
#> 38  -3.9318256  0.469877380
#> 106 -3.9318256  0.469877380
#> 112 -3.9318256  0.469877380
#> 35  -3.9512437  0.479982370
#> 114 -3.9512437  0.479982370
#> 126 -3.9512437  0.479982370
#> 129 -3.9702919  0.490109064
#> 19  -3.9889840  0.500256782
#> 41  -3.9889840  0.500256782
#> 29  -4.0253517  0.520612608
#> 24  -4.0775374  0.551287353
#> 102 -4.1108739  0.571823544
#> 43  -4.1431347  0.592422810
#> 1   -4.2766661  0.685766363
#> 115 -4.2904594  0.696191128
#> 105 -4.3820266  0.769371356
#> 125 -4.3820266  0.769371356
#> 7   -4.4067192  0.790330100
#> 110 -4.4188406  0.800815182
#> 122 -4.4308168  0.811303390
#> 72  -4.4659081  0.842781793
#> 94  -4.4659081  0.842781793
#> 113 -4.4998097  0.874270700
#> 47  -4.5217886  0.895262897
#> 54  -4.5538769  0.926741607
#> 104 -4.5538769  0.926741607
#> 22  -4.5747110  0.947716003
#> 99  -4.5951199  0.968677406
#> 101 -4.5951199  0.968677406
#> 10  -4.6051702  0.979152237
#> 69  -4.6051702  0.979152237
#> 67  -4.6347290  1.010548639
#> 91  -4.6347290  1.010548639
#> 66  -4.6539604  1.031452470
#> 8   -4.7004804  1.083596853
#> 76  -4.7095302  1.094003359
#> 134 -4.7095302  1.094003359
#> 71  -4.7184989  1.104401684
#> 25  -4.7621739  1.156260508
#> 49  -4.7621739  1.156260508
#> 5   -4.7706846  1.166603804
#> 39  -4.8040210  1.207873697
#> 21  -4.8121844  1.218164192
#> 4   -4.8362819  1.248967507
#> 50  -4.8828019  1.310250457
#> 133 -4.8903491  1.320420370
#> 32  -4.9344739  1.381163805
#> 120 -4.9416424  1.391240650
#> 65  -4.9628446  1.421388648
#> 13  -4.9698133  1.431410221
#> 27  -5.0172798  1.501167122
#> 23  -5.0304379  1.520970045
#> 63  -5.0498560  1.550567780
#> 52  -5.0875963  1.609381169
#> 56  -5.0875963  1.609381169
#> 127 -5.0998664  1.628873563
#> 55  -5.1761497  1.754258768
#> 64  -5.2040067  1.801909658
#> 121 -5.2257467  1.839819194
#> 62  -5.2983174  1.971199177
#> 84  -5.3033049  1.980515613
#> 57  -5.3752784  2.119456976
#> 3   -5.4293456  2.229918698
#> 73  -5.4424177  2.257500375
#> 135 -5.4424177  2.257500375
#> 74  -5.4889377  2.358711202
#> 68  -5.5214609  2.432560237
#> 61  -5.5606816  2.525412561
#> 59  -5.6276211  2.694920441
#> 88  -5.6454469  2.742717470
#> 36  -5.6594822  2.781218270
#> 9   -5.7493930  3.048286406
#> 132 -5.8289456  3.319391015
#> 82  -5.8777358  3.504789944
#> 136 -5.9348942  3.742623755
#> 17  -5.9506426  3.812264637
#> 79  -5.9635793  3.870831863
#> 44  -5.9712618  3.906195203
#> 2   -6.0185932  4.133663438
#> 83  -6.1463293  4.824436019
#> 58  -6.3153580  5.830618119
#> 78  -6.3750248  6.168664639
#> 75  -6.8987145 10.271662961
#> 70  -6.9067548 10.588131805

Arguments

See also

Examples