Description Usage Arguments Author(s) See Also Examples
For Cox regression models, this function generates a Kaplan-Meier 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 cross-validated log-rank test p-value is displayed in the default title.
1 2 3 |
x |
|
alpha.index |
Integer indices to select alpha values. Default is |
xlab |
Custom x-axis label. |
ylab |
Custom y-axis 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 0-1 range) to partition the cohort based on predicted risk. Default is 0.5. |
risk.col |
Vector of colors to display the predicted risk-based 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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