Function to generate a random sample of survival data from Yang and Prentice models.
Usage
rypreg(
u,
formula,
baseline,
beta,
phi,
dist = NULL,
package = NULL,
lwr = 0,
upr = Inf,
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 short-term regression coefficients.
- phi
vector of long-term 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.
- lwr
left-truncation time (default to 0 in the absence of left-truncation).
- upr
right-truncation time (default to Inf in the absence of right-truncation).
- data
data frame containing the covariates used to generate the survival times.
- ...
further arguments passed to other methods.
Examples
library(rsurv)
library(dplyr)
set.seed(123)
n <- 1000
simdata <- data.frame(
age = rnorm(n),
sex = sample(c("f", "m"), size = n, replace = TRUE)
) %>%
mutate(
t = rypreg(runif(n), ~ age+sex, beta = c(1, 2), phi = 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> -0.56047565, -0.23017749, 1.55870831, 0.07050839, 0.12928774, 1…
#> $ sex <chr> "f", "f", "f", "m", "m", "f", "f", "f", "m", "f", "f", "f", "m"…
#> $ t <dbl> 1.42670348, 0.17547254, 1.41589476, 0.15204544, 0.35032244, 0.4…
#> $ c <dbl> 3.04464185, 8.32818782, 5.93647508, 8.07196641, 2.94050778, 1.4…
#> $ time <dbl> 1.42670348, 0.17547254, 1.41589476, 0.15204544, 0.35032244, 0.4…
#> $ status <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …