R/get_x.R

In ericdunipace/CoarsePosteriorSummary: Interpretable Model Summaries Using the Wasserstein Distance

#### X variable ####
gen_x <- function() {
  rX <- function(n, corr, p, ...) {
    p <- p-1 # leave room for intercept
    x_cov <- corr_mat_construct(corr, p)
    X <- cbind(1, SLIMpaper::rmvnorm(n, rep(0,p), x_cov))
    return(X)
  }

  scaleX <- function(x, sds,...) {
    if(all(x[,1]==1)) {
      stopifnot(length(sds) == (ncol(x)-1))
      x[,-1] <- x[,-1] %*% diag(sds)
    } else {
      stopifnot(length(sds) == ncol(x))
      x <- x %*% diag(sds)
    }
    return(x)
  }
  return(list(rX=rX, sX = scaleX))
}

corr_mat_construct <- function(corr, p) {
  if(is.matrix(corr)) {
    x_cov <- corr
  } else {
    x_cov <- matrix(0, nrow=p, ncol=p)
  }
  # diag(x_cov) <- 1
  #
  # if(p > 5) {
  #   p_lim <- 5
  #   p_lim1 <- p_lim + 1
  #   x_cov[p_lim1:p,1:p_lim] <- x_cov[1:p_lim, p_lim1:p] <- 0
  # }
  # if(p > 10) {
  #   p_lim <- 10
  #   p_lim1 <- p_lim + 1
  #   x_cov[p_lim1:p,1:p_lim] <- x_cov[1:p_lim, p_lim1:p] <- 0
  # }
  # if(p > 20) {
  #   p_lim <- 20
  #   p_lim1 <- p_lim + 1
  #   x_cov[p_lim1:p,1:p_lim] <- x_cov[1:p_lim, p_lim1:p] <- 0
  # }
  # if(p > 50) {
  #   p_lim <- 50
  #   p_lim1 <- p_lim + 1
  #   x_cov[p_lim1:p,1:p_lim] <- x_cov[1:p_lim, p_lim1:p] <- 0
  # }
  for(i in seq(1,p,5)) {
    ranges <- (i:(i + 4))
    if(ranges[5] > p) ranges <- ranges[ranges <= p]
    x_cov[ranges, ranges] <- corr
  }
  diag(x_cov) <- 1
  return(x_cov)
}