Random generation from proportional odds models

Function to generate a random sample of survival data from proportional odds models.

Usage

rporeg(u, formula, baseline, beta, dist = NULL, package = NULL, data, ...)

Arguments

u: a numeric vector of quantiles.
formula: formula specifying the linear predictors.
baseline: the name of the baseline survival distribution.
beta: vector of regression coefficients.
dist: an alternative way to specify the baseline survival distribution.
package: the name of the package where the assumed quantile function is implemented.
data: data frame containing the covariates used to generate the survival times.
...: further arguments passed to other methods.

Value

a numeric vector containing the generated random sample.

Examples

library(rsurv)
library(dplyr)
n <-  1000
simdata <- data.frame(
  age = rnorm(n),
  sex = sample(c("f", "m"), size = n, replace = TRUE)
) %>%
  mutate(
    t = rporeg(runif(n), ~ age+sex, beta = c(1, 2),
                dist = "weibull", shape = 1.5, scale = 1),
    c = runif(n, 0, 10)
  ) %>%
  rowwise() %>%
  mutate(
    time = min(t, c),
    status = as.numeric(time == t)
  )
glimpse(simdata)
#> Rows: 1,000
#> Columns: 6
#> Rowwise: 
#> $ age    <dbl> -1.1422132, -0.9301405, 0.1053670, 0.2732270, -0.9740712, -0.82…
#> $ sex    <chr> "f", "m", "m", "m", "m", "f", "f", "m", "f", "m", "f", "m", "m"…
#> $ t      <dbl> 0.322956533, 0.731323342, 0.025335643, 0.007979669, 0.655896927…
#> $ c      <dbl> 5.2751784, 2.7173149, 5.8006588, 0.1070868, 2.7210765, 5.108660…
#> $ time   <dbl> 0.322956533, 0.731323342, 0.025335643, 0.007979669, 0.655896927…
#> $ status <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, …