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plsRcox (version 1.0)

DR_coxph: (Deviance) Residuals Computation

Description

This function computes the Residuals for a Cox-Model fitted with an intercept as the only explanatory variable. Default behaviour gives the Deviance residuals.

Usage

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=
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 e
type
character string specifying the type of censoring. Possible values are "right", "left", "counting", "interval", or "interval2". The default is "right" or "counting"
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 rar
typeres
character string indicating the type of residual desired. Possible values are "martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas", and "scaledsch".
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,
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 numVector of the residual values.

References

plsRcox : mod?les{mod`eles} de Cox en pr?sence{pr'esence} d'un grand nombre de variables explicatives, Fr?d?ric{Fr'ed'eric} Bertrand, Myriam Maumy-Bertrand, Marie-Pierre Gaub, Nicolas Meyer, Chimiom?trie{Chimiom'etrie} 2010, Paris, 2010.

See Also

coxph

Examples

Run this code
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)
DR_coxph(Y_train_micro,C_train_micro,scaleY=FALSE,plot=TRUE)
DR_coxph(Y_train_micro,C_train_micro,scaleY=TRUE,plot=TRUE)

rm(Y_train_micro,C_train_micro)

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