coxph.detail {survival}  R Documentation 
Returns the individual contributions to the first and second derivative matrix, at each unique event time.
coxph.detail(object, riskmat=FALSE, rorder=c("data", "time"))
object 
a Cox model object, i.e., the result of 
riskmat 
include the atrisk indicator matrix in the output? 
rorder 
only applicable if 
This function may be useful for those who wish to investigate new methods or extensions to the Cox model. The example below shows one way to calculate the Schoenfeld residuals.
a list with components
time 
the vector of unique event times 
nevent 
the number of events at each of these time points. 
means 
a matrix with one row for each event time and one column for each variable
in the Cox model, containing the weighted mean of the variable at that time,
over all subjects still at risk at that time. The weights are the risk
weights 
nrisk 
number of subjects at risk. 
score 
the contribution to the score vector (first derivative of the log partial likelihood) at each time point. 
imat 
the contribution to the information matrix (second derivative of the log partial likelihood) at each time point. 
hazard 
the hazard increment. Note that the hazard and variance of the
hazard are always for some particular future subject. This routine
uses 
varhaz 
the variance of the hazard increment. 
x , y 
copies of the input data. 
strata 
only present for a stratified Cox model, this is
a table giving the number of time points of component 
riskmat 
a matrix with one row for each observation and one colum for each
unique event time,
containing a 0/1 value to indicate whether that observation was (1) or
was not (0) at risk at the given time point. Rows are in the order
of the original data (after removal of any missings by

fit < coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE)
fitd < coxph.detail(fit)
# There is one Schoenfeld residual for each unique death. It is a
# vector (covariates for the subject who died)  (weighted mean covariate
# vector at that time). The weighted mean is defined over the subjects
# still at risk, with exp(X beta) as the weight.
events < fit$y[,2]==1
etime < fit$y[events,1] #the event times  may have duplicates
indx < match(etime, fitd$time)
schoen < fit$x[events,]  fitd$means[indx,]