Residuals for a fitted spbp model.
Arguments
- object
an object of class `spbp` result of a
spbpfit.- type
type of residuals, default is "cox-snell"
- ...
arguments passed to parent method.
Examples
library("spsurv")
data("veteran", package = "survival")
#> Warning: data set ‘veteran’ not found
fit <- bpph(Surv(time, status) ~ karno + factor(celltype),
data = veteran
)
residuals(fit)
#> 1 2 3 4 5 6
#> 0.620355305 -0.677763319 -0.233596483 0.308411163 0.526283630 0.841100965
#> 7 8 9 10 11 12
#> 0.186314769 0.677333138 -1.298648204 -0.397584194 0.789672141 0.932088933
#> 13 14 15 16 17 18
#> -1.009768966 -0.064601942 0.962812829 0.695190917 -3.401799392 0.930100064
#> 19 20 21 22 23 24
#> 0.689260562 0.874137431 -2.617582245 -1.095069394 -0.757299758 -0.614833444
#> 25 26 27 28 29 30
#> 0.281493867 0.603528197 -1.365189088 0.781213344 0.676610462 0.612745494
#> 31 32 33 34 35 36
#> 0.388110472 0.141782280 0.498506215 0.801706997 0.593438515 -2.209402620
#> 37 38 39 40 41 42
#> 0.551287931 0.457266058 0.249422687 0.727797798 0.576257869 0.909369653
#> 43 44 45 46 47 48
#> 0.066773121 -7.387883894 0.821681747 0.605445108 -0.135712627 -0.002298556
#> 49 50 51 52 53 54
#> -0.075726180 -0.218962002 0.763550784 -0.496968156 0.893342101 0.137858678
#> 55 56 57 58 59 60
#> -0.840701368 0.333964169 -1.222874650 -2.292038565 -1.071583479 0.856540874
#> 61 62 63 64 65 66
#> -0.044046658 0.184566572 0.124901681 -0.546735976 0.568573218 0.573054967
#> 67 68 69 70 71 72
#> 0.581710309 -0.371481068 0.246339502 -1.304268736 0.671136949 -0.250932941
#> 73 74 75 76 77 78
#> -1.702954416 -0.779104322 -3.157127756 0.555766504 0.984592689 -2.415990555
#> 79 80 81 82 83 84
#> 0.155351573 0.589513623 0.584589763 -0.421650533 -0.053602599 0.410627786
#> 85 86 87 88 89 90
#> 0.993924094 0.892888142 0.778718823 0.401900553 0.904508920 0.364292980
#> 91 92 93 94 95 96
#> -0.856300388 0.279876441 0.680837101 0.026236934 0.965295419 0.498506215
#> 97 98 99 100 101 102
#> 0.770179137 0.759991526 0.179192532 0.959016924 0.484553738 0.515605952
#> 103 104 105 106 107 108
#> 0.816161729 0.214741814 -0.212151658 -0.376267303 0.453483044 0.331797074
#> 109 110 111 112 113 114
#> 0.507355386 -0.413217535 0.745770458 0.187434758 -0.512390089 0.169952852
#> 115 116 117 118 119 120
#> -0.204175633 0.844406438 0.592487364 -2.585461631 0.814966582 -0.764935943
#> 121 122 123 124 125 126
#> -0.254627951 0.245302409 0.617538693 -0.317568776 -1.474762280 0.630692784
#> 127 128 129 130 131 132
#> 0.080771646 0.683566047 0.622897794 0.753160465 0.700024220 -0.372340645
#> 133 134 135 136 137
#> 0.361759650 0.157911098 -0.273015981 -0.547887113 0.122546492