plot.HR | R Documentation |
Plots flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.
## S3 method for class 'HR' plot(x, predictor, prob=NULL, pred.value=NULL, conf.level=0.95, round.x=NULL, ref.label=NULL, col, main, xlab, ylab, lty, xlim, ylim, xx, ylog=TRUE, log=ifelse(ylog, "", "y"), ...)
x |
An object of class HR |
predictor |
Variable named in the formula or included as a predictor in the |
prob |
Value between 0 and 1. If |
pred.value |
Value from the variable |
conf.level |
Level of confidence. Defaults to 0.95 (corresponding to 95%). |
round.x |
Rounding of numbers in the plot. |
ref.label |
Label for the reference covariate. By default is the name of the covariate. |
col |
Vector of dimension 3 for the colors to plot. |
main |
These arguments to title have useful defaults here. |
xlab |
The range of x and y values with sensible defaults. |
ylab |
The range of x and y values with sensible defaults. |
lty |
Vector of dimension 2 for the line type. |
xlim |
The range of x and y values with sensible defaults. |
ylim |
The range of x and y values with sensible defaults. |
xx |
Vector of values (from the variable |
ylog |
If |
log |
Axis logarithmic scale. See |
... |
Other arguments. |
No value is returned.
Artur Araújo and Luís Meira-Machado
Cadarso-Suarez, C. and Meira-Machado, L. and Kneib, T. and Gude, F. (2010). Flexible hazard ratio curves for continuous predictors in multi-state models: an application to breast cancer data. Statistical Modelling, 10(3), 291-314. doi: 10.1177/1471082X0801000303
Meira-Machado, L. and Cadarso-Suárez, C. and Gude, F. and Araújo, A. (2013). smoothHR: An R Package for Pointwise Nonparametric Estimation of Hazard Ratio Curves of Continuous Predictors. Computational and Mathematical Methods in Medicine, 2013, 11 pages. doi: 10.1155/2013/745742
smoothHR
.
# Example 1 library(survival) data(whas500) fit <- coxph(Surv(lenfol, fstat)~age+hr+gender+diasbp+pspline(bmi)+pspline(los), data=whas500, x=TRUE) hr1 <- smoothHR(data=whas500, coxfit=fit) plot(hr1, predictor="bmi", prob=0, conf.level=0.95) # Example 2 hr2 <- smoothHR( data=whas500, time="lenfol", status="fstat", formula=~age+hr+gender+diasbp+ pspline(bmi)+pspline(los) ) plot(hr2, predictor="los", pred.value=7, conf.level=0.95, xlim=c(0,30), round.x=1, ref.label="Ref.", xaxt="n") xx <- c(0, 5, 10, 15, 20, 25, 30) axis(1, xx)
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