R/orthus_sim.R

In jsilve24/mongrel: Bayesian Multinomial Logistic Normal Regression

#' Simulate simple orthus dataset and priors (for testing)
#' 
#' @param D number of multinomial categories
#' @param P number of dimensions of second dataset Z
#' @param N number of samples
#' @param Q number of covariates (first one is an intercept, must be > 1)
#' @param use_names should samples, covariates, and categories be named
#' @param true_priors should Xi and upsilon be chosen to have mean at true 
#'   simulated value
#' @return list
#' @export
#' @importFrom driver alrInv
#' @importFrom stats rnorm rmultinom
#' @examples 
#' sim <- orthus_sim()
orthus_sim <- function(D=10, P=10, N=30, Q=2, use_names=TRUE, true_priors=FALSE){
  
  # Simulate Data
  Sigma <- diag(sample(1:8, D-1+P, replace=TRUE))
  Sigma[2, 3] <- Sigma[3,2] <- -1
  Gamma <- diag(sqrt(rnorm(Q)^2))
  Theta <- matrix(0, D-1+P, Q)
  Phi <-  Theta + t(chol(Sigma))%*%matrix(rnorm(Q*(D-1+P)), nrow=D-1+P)%*%chol(Gamma)
  X <- matrix(rnorm(N*(Q-1)), Q-1, N)
  X <- rbind(1, X)
  Eta <- Phi%*%X + t(chol(Sigma))%*%matrix(rnorm(N*(D-1+P)), nrow=D-1+P)
  Z <- Eta[D:(D-1+P),]
  Eta <- Eta[1:(D-1),]
  Pi <- t(driver::alrInv(t(Eta)))
  Y <- matrix(0, D, N)
  for (i in 1:N) Y[,i] <- rmultinom(1, sample(5000:10000), prob = Pi[,i])
  if (use_names){
    colnames(X) <- colnames(Y) <- paste0("s", 1:N)
    rownames(Y) <- paste0("c", 1:D)
    rownames(X) <- paste0("x", 1:Q)  
  }
  
  # Priors
  if (true_priors){
    upsilon <- D+10
    Xi <- Sigma*(upsilon-D-P)
  } else {
    upsilon <- D
    Xi <- diag(D-1+P)
  }
  
  # Precompute
  KInv <- solve(Xi)
  AInv <- solve(diag(N)+ t(X)%*%Gamma%*%X)
  
  return(list(Sigma=Sigma, Gamma=Gamma, D=D, N=N, Q=Q, P=P, Theta=Theta, Phi=Phi, 
              X=X, Y=Y,Z=Z, Eta=Eta, upsilon=upsilon, Xi=Xi, KInv=KInv, AInv=AInv))
}