Nothing
# Compute the Omega(Z) matrix
# based on the given covariate adaptive randomization scheme.
#
# This function returns a function to obtain the correct matrix
# for a specified vector of randomization probabilities pi_t.
omegaz.closure <- function(car_scheme){
omegaz.func <- function(pi_t){
# TODO: Check that minimization works out to 0
if(car_scheme %in% c("simple", "pocock-simon")){
pi_t <- c(pi_t)
omegaz <- diag(pi_t) - pi_t %*% t(pi_t)
} else {
omegaz <- matrix(0, nrow=length(pi_t), ncol=length(pi_t))
}
return(omegaz)
}
return(omegaz.func)
}
# Calculate nu_d for the randomization design
nu.d <- function(car_scheme, p_trt=0.5){
if(length(car_scheme) > 1 | length(car_scheme) == 0){
nu_d <- NA
}else{
if(is.na(car_scheme)){
nu_d <- NA
}else{
if(car_scheme %in% c("permuted-block", "biased-coin")){
nu_d <- 0
}else if(car_scheme == "simple"){
nu_d <- p_trt * (1 - p_trt)
}
else{
nu_d <- NA
}
}
}
return(nu_d)
}
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