simID {SemiCompRisks} | R Documentation |
The function to simulate independent/cluster-correlated semi-competing risks data under semi-Markov Weibull/Weibull-MVN models.
simID(id=NULL, x1, x2, x3, beta1.true, beta2.true, beta3.true, alpha1.true, alpha2.true, alpha3.true, kappa1.true, kappa2.true, kappa3.true, theta.true, SigmaV.true=NULL, cens)
id |
a vector of cluster information for |
x1 |
covariate matrix, |
x2 |
covariate matrix, |
x3 |
covariate matrix, |
beta1.true |
true value for β_1. |
beta2.true |
true value for β_2. |
beta3.true |
true value for β_3. |
alpha1.true |
true value for α_1. |
alpha2.true |
true value for α_2. |
alpha3.true |
true value for α_3. |
kappa1.true |
true value for κ_1. |
kappa2.true |
true value for κ_2. |
kappa3.true |
true value for κ_3. |
theta.true |
true value for θ. |
SigmaV.true |
true value for Σ_V. Required only when generating clustered data. |
cens |
a vector with two numeric elements. The right censoring times are generated from Uniform(cens[1], cens[2]). |
simIDcor
returns a data.frame containing semi-competing risks outcomes from n
subjects.
It is of dimension n\times 4: the columns correspond to y_1, δ_1, y_2, δ_2.
y1 |
a vector of |
y2 |
a vector of |
delta1 |
a vector of |
delta2 |
a vector of |
Kyu Ha Lee and Sebastien Haneuse
Maintainer: Kyu Ha Lee <klee15239@gmail.com>
library(MASS) set.seed(123456) J = 110 nj = 50 n = J * nj id <- rep(1:J, each = nj) kappa1.true <- 0.05 kappa2.true <- 0.01 kappa3.true <- 0.01 alpha1.true <- 0.8 alpha2.true <- 1.1 alpha3.true <- 0.9 beta1.true <- c(0.5, 0.8, -0.5) beta2.true <- c(0.5, 0.8, -0.5) beta3.true <- c(1, 1, -1) SigmaV.true <- matrix(0.25,3,3) theta.true <- 0.5 cens <- c(90, 90) cov1 <- matrix(rnorm((length(beta1.true)-1)*n, 0, 1), n, length(beta1.true)-1) cov2 <- sample(c(0, 1), n, replace = TRUE) x1 <- as.data.frame(cbind(cov1, cov2)) x2 <- as.data.frame(cbind(cov1, cov2)) x3 <- as.data.frame(cbind(cov1, cov2)) simData <- simID(id, x1, x2, x3, beta1.true, beta2.true, beta3.true, alpha1.true, alpha2.true, alpha3.true, kappa1.true, kappa2.true, kappa3.true, theta.true, SigmaV.true, cens)