ipw | R Documentation |
Internal function. Calculates Inverse Probability of Censoring Weights (IPCW) and adds them to a data.frame
ipw( formula, data, cluster, same.cens = FALSE, obs.only = FALSE, weight.name = "w", trunc.prob = FALSE, weight.name2 = "wt", indi.weight = "pr", cens.model = "aalen", pairs = FALSE, theta.formula = ~1, ... )
formula |
Formula specifying the censoring model |
data |
data frame |
cluster |
clustering variable |
same.cens |
For clustered data, should same censoring be assumed (bivariate probability calculated as mininum of the marginal probabilities) |
obs.only |
Return data with uncensored observations only |
weight.name |
Name of weight variable in the new data.frame |
trunc.prob |
If TRUE truncation probabilities are also calculated and stored in 'weight.name2' (based on Clayton-Oakes gamma frailty model) |
weight.name2 |
Name of truncation probabilities |
indi.weight |
Name of individual censoring weight in the new data.frame |
cens.model |
Censoring model (default Aalens additive model) |
pairs |
For paired data (e.g. twins) only the complete pairs are returned (With pairs=TRUE) |
theta.formula |
Model for the dependence parameter in the Clayton-Oakes model (truncation only) |
... |
Additional arguments to censoring model |
Klaus K. Holst
## Not run: data("prt",package="mets") prtw <- ipw(Surv(time,status==0)~country, data=prt[sample(nrow(prt),5000),], cluster="id",weight.name="w") plot(0,type="n",xlim=range(prtw$time),ylim=c(0,1),xlab="Age",ylab="Probability") count <- 0 for (l in unique(prtw$country)) { count <- count+1 prtw <- prtw[order(prtw$time),] with(subset(prtw,country==l), lines(time,w,col=count,lwd=2)) } legend("topright",legend=unique(prtw$country),col=1:4,pch=-1,lty=1) ## End(Not run)
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