# plot.coxphw.predict

##### Plot the Relative or Log Relative Hazard Versus Values of a Continuous Covariable.

This function visualizes a nonlinear or a time-dependent effect of a `predict.coxphw`

object.

- Keywords
- survival

##### Usage

```
# S3 method for coxphw.predict
plot(x, addci = TRUE, xlab = NULL, ylab = NULL, …)
```

##### Arguments

- x
an output object of

`coxphw`

.- addci
confidence intervalls are obtained. Default is TRUE.

- xlab
label for x-axis of plot, uses variable specified in

`x`

in`predict`

as default.- ylab
label for y-axis of plot, uses as appropriate either "relative hazard" or "log relative hazard" as default.

- …
further parameters, to be used for plots (e.g., scaling of axes).

##### Details

This function can be used to depict the estimated nonlinear or time-dependent
effect of an object of class `predict.coxphw`

. It supports simple nonlinear
effects as well as interaction effects of continuous variables with binary
covariates (see examples section in `predict.coxphw`

. ).

##### Value

No output value.

##### Note

In coxphw version 4.0.0 the old `plotshape`

function is replaced with
`predict.coxphw`

and `plot.coxphw.predict`

.

##### References

Royston P and Altman D (1994). Regression Using Fractional Polynomials of Continuous
Covariates: Parsimonious Parametric Modelling. *Applied StatisticsJ R STAT SOC C-APPL* **43**, 429-467.

Royston P and Sauerbrei W (2008). *Multivariable Model-building. A pragmatic approach to regression
analysis based on fractional polynomials for modelling continuous variables.* Wiley, Chichester, UK.

##### See Also

##### Examples

```
# NOT RUN {
set.seed(30091)
n <- 300
x <- 1:n
true.func <- function(x) 3 * (x / 100)^{2} - log(x / 100) - 3 * x / 100
x <- round(rnorm(n = x) * 10 + 40, digits = 0)
time <- rexp(n = n, rate = 1) / exp(true.func(x))
event <- rep(x = 1, times = n)
futime <- runif(n = n, min = 0, max = 309000)
event <- (time < futime) + 0
time[event == 0] <- futime[event == 0]
my.data <- data.frame(x, time, event)
fitahr <- coxphw(Surv(time, event) ~ x, data = my.data, template = "AHR")
# estimated function
plotx <- quantile(x, probs = 0.05):quantile(x, probs = 0.95)
plot(predict(fitahr, type = "shape", newx = plotx, refx = median(x), x = "x",
verbose = FALSE))
# true function
lines(x = plotx, true.func(plotx) - true.func(median(plotx)), lty = 2)
legend("topright", legend=c("AHR estimates", "true"), bty = "n", lty = 1:2, inset = 0.05)
# for more examples see predict.coxphw
# }
```

*Documentation reproduced from package coxphw, version 4.0.1, License: GPL-2*