#'Generates Case II interval-Censored Failure Time data
#'
#'The \code{ICSimple} generates case II interval-censored failure time data. The failure
#'time follows the Weibull regression model
#'
#'First the function generates the failure time using [rweibull()]. The \code{shape}
#'is the same and the second parameter is lambda. Lambda is equal to:
#'\deqn{lambda = scale x exp(X*beta)
#'
#'@param n number of observations to be generated
#'@param shape the first parameter of Weibull distribution
#'@param scale the second parameter of Weibull distribution
#'@param pc the proportion of right-censored expected in the sample
#'@param h the maximum of time between the intervals
#'@param X the vector with the values of covariates. The number of rows of \code{X} must be equal \code{n}
#'@param beta the vector with the value of the regression coefficients. The lenght of \code{beta} must be equal \code{n}
#'
#'
#'@keywords interval censored
#'@examples
#'ICsimple(50,0.8,0.8)
ICSimple <- function(n,shape, scale, pc, h, X = as.matrix(0), beta = as.matrix(0)){
X <- as.matrix(X)
beta <- as.matrix(beta)
#CHECAGENS
if(!is.numeric(n)) stop('"n" must be numeric')
if(n < 0) stop('"n" must be greater than or equal to zero')
if(!is.numeric(scale)) stop('"scale" must be numeric')
if(scale < 0) stop('"scale" must be greater than or equal to zero')
if(!is.numeric(shape)) stop('"shape" must be numeric')
if(shape < 0) stop('"shape" must be greater than or equal to zero')
if(!is.numeric(pc)) stop('"pc" must be numeric')
if(pc > 1 | pc < 0) stop('"pc" must be greater than zero and less than 1')
if(!is.numeric(h)) stop('"h" must be numeric')
if(h < 0) stop('"h" must be greater than zero')
if(!all(X == 0) & nrow(X) != n) stop('Number of observations of "x" must be the
same number of observations
random generated')
if((!all(X == 0)) & (!all(beta == 0)) & (ncol(X) != length(beta))) stop(
'Lenght of "beta" must be the same number of columns of "x".')
if(any(!is.numeric(X))) stop('"x" must be numeric')
if(any(!is.numeric(beta))) stop('"beta" must be numeric')
# generating times from the following Weibull distribution:
u <- runif(n)
lambda <- scale*exp(X%*%beta)
tau <- qweibull(rep(1-pc,n), shape = shape, scale = lambda)
t <- rweibull(n, shape = shape, scale = lambda)
L=c(rep(0,n)) #left interval limit
R=c(rep(0,n)) #right interval limit
for (i in 1:n){
while (R[i]<=min(tau[i],t[i])) {
a<-runif(1,0,2*h)
R[i]<-L[i]+a
L[i]<-R[i]
}
L[i]<-R[i]-a
}
delta <-as.numeric(t<tau & tau<R)
R[delta==1] <- tau[delta == 1]
delta <-as.numeric(tau<t)
R[delta==1] <- Inf
L[L == 0] <- -Inf
event <- rep(0,n)
event[delta != 1] <- 3
y <- cbind(L, R)
dados<-data.frame(y,event, X)
return(dados)
}
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