computeDR: Compute deviance residuals

In bigPLScox: Partial Least Squares for Cox Models with Big Matrices

Compute deviance residuals

Description

This function computes deviance residuals from a null Cox model. By default it delegates to survival::coxph(), but a high-performance C++ engine is also available for large in-memory or bigmemory::big.matrix design matrices.

Usage

computeDR(
  time,
  time2,
  event,
  type,
  origin,
  typeres = "deviance",
  collapse,
  weighted,
  scaleY = TRUE,
  plot = FALSE,
  engine = c("survival", "cpp", "qcpp"),
  method = c("efron", "breslow"),
  X = NULL,
  coef = NULL,
  eta = NULL,
  center = NULL,
  scale = NULL
)

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 ?
engine	Either "survival" (default) to call survival::coxph() or "cpp" to use the C++ implementation.
method	Tie handling to use with engine = "cpp": either "efron" (default) or "breslow".
X	Optional design matrix used to compute the linear predictor when engine = "cpp". Supports base matrices, data frames, and bigmemory::big.matrix objects.
coef	Optional coefficient vector associated with X when engine = "cpp".
eta	Optional precomputed linear predictor passed directly to the C++ engine.
center, scale	Optional centring and scaling vectors applied to X before computing the linear predictor with the C++ engine.

Value

Residuals from a null model fit. When engine = "cpp", the returned vector has attributes "martingale", "cumhaz", and "linear_predictor".

Author(s)

Frédéric Bertrand
[e-mail]
https://fbertran.github.io/homepage/

References

Bastien, P., Bertrand, F., Meyer, N., and Maumy-Bertrand, M. (2015). Deviance residuals-based sparse PLS and sparse kernel PLS for binary classification and survival analysis. BMC Bioinformatics, 16, 211.

Therneau, T.M., Grambsch, P.M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.

See Also

coxph

Examples

data(micro.censure, package = "bigPLScox")

Y_train_micro <- micro.censure$survyear[1:80]
C_train_micro <- micro.censure$DC[1:80]

Y_DR <- computeDR(Y_train_micro,C_train_micro)
Y_DR <- computeDR(Y_train_micro,C_train_micro,plot=TRUE)

Y_cpp <- computeDR(
  Y_train_micro,
  C_train_micro,
  engine = "cpp",
  eta = rep(0, length(Y_train_micro))
)

Y_qcpp <- computeDR(
  Y_train_micro,
  C_train_micro,
  engine = "qcpp"
)

bigPLScox documentation built on Nov. 18, 2025, 5:06 p.m.