new.arm.copula.sim: Simulating new multivariate datasets with shifted mean vector...

In copulaSim: Virtual Patient Simulation by Copula Invariance Property

Simulating new multivariate datasets with shifted mean vector from existing empirical data

Description

Simulating new multivariate datasets with shifted mean vector from existing empirical data

Usage

new.arm.copula.sim(
  data.input,
  id.vec,
  arm.vec,
  shift.vec.list,
  n.patient,
  n.simulation,
  seed = NULL,
  validation.type = "none",
  validation.sig.lvl = 0.05,
  rmvnorm.matrix.decomp.method = "svd",
  verbose = TRUE
)

Arguments

data.input, id.vec, arm.vec, n.patient, n.simulation, seed	Please refer to the function copula.sim.
shift.vec.list	A list of numeric vectors to specify the mean-shifted values for new arms.
validation.type, validation.sig.lvl, rmvnorm.matrix.decomp.method, verbose	Please refer to the function copula.sim.

Value

Please refer to the function copula.sim.

Author(s)

Pei-Shan Yen, Xuemin Gu, Jenny Jiao, Jane Zhang

Examples

library(copulaSim)

## Generate Empirical Data
 # Assume that the single-arm, 3-dimensional empirical data follows multivariate normal data
library(mvtnorm)
arm1 <- rmvnorm(n = 80, mean = c(10,10.5,11), sigma = diag(3) + 0.5)
test_data <- as.data.frame(cbind(1:80, rep(1,80), arm1))
colnames(test_data) <- c("id", "arm", paste0("time_", 1:3))

## Generate 1 simulated datasets with one empirical arm and two new-arm.
## The mean difference between empirical arm and
 # (i) the 1st new arm is assumed to be 2.5, 2.55, and 2.6 at each time point
 # (ii) the 2nd new arm is assumed to be 4.5, 4.55, and 4.6 at each time point
new.arm.copula.sim(data.input = test_data[,-c(1,2)],
  id.vec = test_data$id, arm.vec = test_data$arm,
  n.patient = 100 , n.simulation = 1, seed = 2022,
  shift.vec.list = list(c(2.5,2.55,2.6), c(4.5,4.55,4.6)))

copulaSim documentation built on Aug. 19, 2022, 5:15 p.m.