Description Usage Arguments Author(s) See Also Examples
For Cox regression models, this function generates a KaplanMeier plot of survival probability as a function of time for a given alpha
. The default behavior is to partition the cohort in two groups by the predicted risk median, but custom partitions in two or more groups (specified by a vector of predicted risk percentiles) are also possible. In the former case, provided that the eNetXplorer
object was generated with the logrank=TRUE
argument, the corresponding crossvalidated logrank test pvalue is displayed in the default title.
1 2 3 
x 

alpha.index 
Integer indices to select alpha values. Default is 
xlab 
Custom xaxis label. 
ylab 
Custom yaxis label. 
cex.lab 
Axis label size. 
main 
Custom title. 
col.main 
Title color. 
cex.main 
Title size. 
conf.int 
Logical to display 95% confidence intervals. Default is 
breaks_ptiles 
Vector of percentiles (in 01 range) to partition the cohort based on predicted risk. Default is 0.5. 
risk.col 
Vector of colors to display the predicted riskbased subcohorts. 
legend 
Logical to display legend. Default is 
legend.cex 
Legend size. 
... 
Additional plotting parameters. 
Julian Candia and John S. Tsang
Maintainer: Julian Candia julian.candia@nih.gov
1 2 3 4 5  data(breastCancerSurv)
fit = eNetXplorer(x=breastCancerSurv$predictor, y=breastCancerSurv$response, family="cox",
n_run=25, n_perm_null=15, seed=111, logrank=TRUE)
plot(x=fit, plot.type="KaplanMeier")
plotKaplanMeier(x=fit, alpha.index=6, conf.int=FALSE, breaks_ptiles=c(0.333,0.667))

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