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[morpheus.git] / pkg / R / sampleIO.R

#' Generate sample inputs-outputs
#'
#' Generate input matrix X of size nxd and binary output of size n, where Y is subdivided
#' into K groups of proportions p. Inside one group, the probability law P(Y=1) is
#' described by the corresponding column parameter in the matrix β + intercept b.
#'
#' @param n Number of individuals
#' @param p Vector of K-1 populations relative proportions (sum <= 1)
#' @param β Vectors of model parameters for each population, of size dxK
#' @param b Vector of intercept values (use rep(0,K) for no intercept)
#' @param link Link type; "logit" or "probit"
#'
#' @return A list with
#' \itemize{
#'   \item{X: the input matrix (size nxd)}
#'   \item{Y: the output vector (size n)}
#'   \item{index: the population index (in 1:K) for each row in X}
#' }
#'
#' @export
generateSampleIO = function(n, p, β, b, link)
{
  # Check arguments
  tryCatch({n = as.integer(n)}, error=function(e) stop("Cannot convert n to integer"))
  if (length(n) > 1)
    warning("n is a vector but should be scalar: only first element used")
  if (n <= 0)
    stop("n: positive integer")
  if (!is.matrix(β) || !is.numeric(β) || any(is.na(β)))
    stop("β: real matrix, no NAs")
  K = ncol(β)
  if (!is.numeric(p) || length(p)!=K-1 || any(is.na(p)) || any(p<0) || sum(p) > 1)
    stop("p: positive vector of size K-1, no NA, sum<=1")
  p <- c(p, 1-sum(p))
  if (!is.numeric(b) || length(b)!=K || any(is.na(b)))
    stop("b: real vector of size K, no NA")

  #random generation of the size of each population in X~Y (unordered)
  classes = rmultinom(1, n, p)

  d = nrow(β)
  zero_mean = rep(0,d)
  id_sigma = diag(rep(1,d))
  # Always consider an intercept (use b=0 for none)
  d = d + 1
  β = rbind(β, b)
  X = matrix(nrow=0, ncol=d)
  Y = c()
  index = c()
  for (i in 1:ncol(β))
  {
    index = c(index, rep(i, classes[i]))
    newXblock = cbind( MASS::mvrnorm(classes[i], zero_mean, id_sigma), 1 )
    arg_link = newXblock %*% β[,i] #β
    probas =
      if (link == "logit")
      {
        e_arg_link = exp(arg_link)
        e_arg_link / (1 + e_arg_link)
      }
      else #"probit"
        pnorm(arg_link)
    probas[is.nan(probas)] = 1 #overflow of exp(x)
    #probas = rowSums(p * probas)
    X = rbind(X, newXblock)
    #Y = c( Y, vapply(probas, function(p) (ifelse(p >= .5, 1, 0)), 1) )
    Y = c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
  }
  shuffle = sample(n)
  # Returned X should not contain an intercept column (it's an argument of estimation
  # methods)
  list("X"=X[shuffle,-d], "Y"=Y[shuffle], "index"=index[shuffle])
}