(Deviance) Residuals Computation

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,
  ...
)

Arguments

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 TRUE/FALSE (TRUE = death) or 1/2 (2=death). For interval censored data, the status indicator is 0=right censored, 1=event at time, 2=left censored, 3=interval censored. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have an event.
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, (start, end]. For counting process data, event indicates whether an event occurred at the end of the interval.
type: character string specifying the type of censoring. Possible values are "right", "left", "counting", "interval", or "interval2". The default is "right" or "counting" depending on whether the time2 argument is absent or present, respectively.
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 "martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas", and "scaledsch". Only enough of the string to determine a unique match is required.
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 collapse=c(1,1,1,2,3,3,4,4,4,4) could be used to obtain per subject rather than per observation residuals.
weighted: if TRUE and the model was fit with case weights, then the weighted residuals are returned.
scaleY: Should the time values be standardized ?
plot: Should the survival function be plotted ?)
...: Arguments to be passed on to survival::coxph.

Value

Named num: Vector of the residual values.

References

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.

Author

Frédéric Bertrand
frederic.bertrand@utt.fr
http://www-irma.u-strasbg.fr/~fbertran/

Examples


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 -0.43627414 
DR_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 -0.43627414 
DR_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 

rm(Y_train_micro,C_train_micro)

Arguments

Value

References

See also

Author

Examples