This function computes the Residuals for a Cox-Model fitted with an intercept as the only explanatory variable. Default behaviour gives the Deviance residuals.
DR_coxph( time, time2, event, type, origin, typeres = "deviance", collapse, weighted, scaleY = TRUE, plot = FALSE, ... )
| time | for right censored data, this is the follow up time. For interval data, the first argument is the starting time for the interval. |
|---|---|
| time2 | The status indicator, normally 0=alive, 1=dead. Other choices
are |
| event | ending time of the interval for interval censored or counting
process data only. Intervals are assumed to be open on the left and closed
on the right, |
| type | character string specifying the type of censoring. Possible
values are |
| origin | for counting process data, the hazard function origin. This option was intended to be used in conjunction with a model containing time dependent strata in order to align the subjects properly when they cross over from one strata to another, but it has rarely proven useful. |
| typeres | character string indicating the type of residual desired.
Possible values are |
| collapse | vector indicating which rows to collapse (sum) over. In
time-dependent models more than one row data can pertain to a single
individual. If there were 4 individuals represented by 3, 1, 2 and 4 rows of
data respectively, then |
| weighted | if |
| scaleY | Should the |
| plot | Should the survival function be plotted ?) |
| ... | Arguments to be passed on to |
Vector of the residual values.
plsRcox, Cox-Models in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.
Frédéric Bertrand
frederic.bertrand@math.unistra.fr
http://www-irma.u-strasbg.fr/~fbertran/
data(micro.censure) Y_train_micro <- micro.censure$survyear[1:80] C_train_micro <- micro.censure$DC[1:80] DR_coxph(Y_train_micro,C_train_micro,plot=TRUE)#> 1 2 3 4 5 6 #> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 #> 7 8 9 10 11 12 #> -0.38667660 1.57418914 -0.54695398 -0.15811388 2.10405254 -0.23145502 #> 13 14 15 16 17 18 #> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 #> 19 20 21 22 23 24 #> 0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 #> 25 26 27 28 29 30 #> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 #> 31 32 33 34 35 36 #> 2.30072697 -0.49023986 -0.54695398 -0.73444882 1.31082939 -0.97633722 #> 37 38 39 40 41 42 #> 1.70134282 -0.54695398 -0.15811388 1.07714870 -0.15811388 -0.49023986 #> 43 44 45 46 47 48 #> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 #> 49 50 51 52 53 54 #> -0.38667660 -0.09836581 -0.79392956 0.46851068 -0.34003013 1.95366297 #> 55 56 57 58 59 60 #> 2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 #> 61 62 63 64 65 66 #> -0.28961087 1.44879795 1.82660327 -0.38667660 0.96936094 -0.15811388 #> 67 68 69 70 71 72 #> -0.43627414 -0.49023986 1.18850436 -0.97633722 -0.97633722 0.86322194 #> 73 74 75 76 77 78 #> -0.43627414 -0.49023986 -0.38667660 0.76231394 -0.97633722 -0.43627414 #> 79 80 #> -0.54695398 -0.43627414DR_coxph(Y_train_micro,C_train_micro,scaleY=FALSE,plot=TRUE)#> 1 2 3 4 5 6 #> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 #> 7 8 9 10 11 12 #> -0.38667660 1.57418914 -0.54695398 -0.15811388 2.10405254 -0.23145502 #> 13 14 15 16 17 18 #> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 #> 19 20 21 22 23 24 #> 0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 #> 25 26 27 28 29 30 #> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 #> 31 32 33 34 35 36 #> 2.30072697 -0.49023986 -0.54695398 -0.73444882 1.31082939 -0.97633722 #> 37 38 39 40 41 42 #> 1.70134282 -0.54695398 -0.15811388 1.07714870 -0.15811388 -0.49023986 #> 43 44 45 46 47 48 #> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 #> 49 50 51 52 53 54 #> -0.38667660 -0.09836581 -0.79392956 0.46851068 -0.34003013 1.95366297 #> 55 56 57 58 59 60 #> 2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 #> 61 62 63 64 65 66 #> -0.28961087 1.44879795 1.82660327 -0.38667660 0.96936094 -0.15811388 #> 67 68 69 70 71 72 #> -0.43627414 -0.49023986 1.18850436 -0.97633722 -0.97633722 0.86322194 #> 73 74 75 76 77 78 #> -0.43627414 -0.49023986 -0.38667660 0.76231394 -0.97633722 -0.43627414 #> 79 80 #> -0.54695398 -0.43627414DR_coxph(Y_train_micro,C_train_micro,scaleY=TRUE,plot=TRUE)#> 1 2 3 4 5 6 #> -1.48432960 -0.54695398 -0.23145502 -0.34003013 -0.97633722 -0.38667660 #> 7 8 9 10 11 12 #> -0.38667660 1.57418914 -0.54695398 -0.15811388 2.10405254 -0.23145502 #> 13 14 15 16 17 18 #> -0.38667660 -1.09692040 -0.15811388 -0.15811388 -0.54695398 -0.38667660 #> 19 20 21 22 23 24 #> 0.65978609 -1.09692040 -0.43627414 -0.28961087 -0.38667660 -0.97633722 #> 25 26 27 28 29 30 #> -1.09692040 -0.15811388 -0.43627414 -0.43627414 -0.38667660 -0.23145502 #> 31 32 33 34 35 36 #> 2.30072697 -0.49023986 -0.54695398 -0.73444882 1.31082939 -0.97633722 #> 37 38 39 40 41 42 #> 1.70134282 -0.54695398 -0.15811388 1.07714870 -0.15811388 -0.49023986 #> 43 44 45 46 47 48 #> -0.34003013 -0.97633722 -0.15811388 -0.91410465 -1.09692040 -0.43627414 #> 49 50 51 52 53 54 #> -0.38667660 -0.09836581 -0.79392956 0.46851068 -0.34003013 1.95366297 #> 55 56 57 58 59 60 #> 2.60558118 -0.54695398 -1.09692040 -0.15811388 -0.49023986 -0.97633722 #> 61 62 63 64 65 66 #> -0.28961087 1.44879795 1.82660327 -0.38667660 0.96936094 -0.15811388 #> 67 68 69 70 71 72 #> -0.43627414 -0.49023986 1.18850436 -0.97633722 -0.97633722 0.86322194 #> 73 74 75 76 77 78 #> -0.43627414 -0.49023986 -0.38667660 0.76231394 -0.97633722 -0.43627414 #> 79 80 #> -0.54695398 -0.43627414