A clever (?) but probably dangerous way to do resampling

permute.R

env.copy <- new.env(parent = parent.env(environment(psdesign$likelihood)))
ps.copy <- psdesign

objs <- ls(environment(psdesign$likelihood))
for(j in objs){
  assign(j, get(j, environment(psdesign$likelihood)), envir = env.copy)
}

assign("Y.trt", mixup(env.copy$Y.trt, env.copy$trtmat[, "Z"]), envir = env.copy)
assign("Y.untrt", mixup(env.copy$Y.untrt, env.copy$untrt.expand[, "Z"]), envir = env.copy)

environment(ps.copy$likelihood) <- env.copy
perm.est <- ps.copy + eval(ps.copy$estimate.call)

psdesign.0 <- psdesign
psdesign.0$augdata$Y <- mixup(psdesign.0$augdata$Y, psdesign.0$augdata$Z)

if(is.factor(psdesign.0$augdata$S.1)){
  
  if(length(unique(as.numeric(psdesign.0$augdata$S.1))) != length(unique(as.numeric(psdesign$augdata$S.1)))){
    bootpar[[i]] <- c(rep(NA, psdesign$nparam), convergence = 11)
    next
  }
  
}
## re-call integration models

psdesign2 <- psdesign.0
for(intj in psdesign$integration.models){
  psdesign2 <- psdesign2 + do.call(as.character(intj$model$args[[1]]), intj$model$args[-1])
}

## re-call risk model

psdesign3 <- psdesign2 + do.call(as.character(psdesign$risk.model$args[[1]]), psdesign$risk.model$args[-1])

# estimate

perm.est <- psdesign3 + eval(psdesign$estimate.call)