# 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. ```r ## S3 method for class '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`. ). ## Returns No output value. ## Note In `coxphw` version 4.0.0 the old `plotshape` function is replaced with `predict.coxphw` and `plot.coxphw.predict`. ## Author(s) Georg Heinze, Meinhard Ploner, Daniela Dunkler ## References Royston P and Altman D (1994). Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling. **Applied Statistics J 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 `coxphw`, `predict.coxphw` ## Examples ```r 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 ```

plot.coxphw.predict function