fix potential bug of kocmi

R/kocmi.R

@@ -4,29 +4,23 @@
 #'
 #' @inheritParams surd
 #' @param conds Integer vector of column indices for the conditioning variables.
-#' @param knockoff Knockoff realizations constructed for the `agent` variable
-#'   while keeping the `target` variable unchanged. Each column corresponds to
-#'   one Monte Carlo knockoff sample generated using the remaining variables
-#'   except the target.
-#' @param null_knockoff (optional) Knockoff realizations generated under the
-#'   null setting where all variables are jointly used to construct knockoffs.
-#'   Each column represents one Monte Carlo sample. If `contain_null = FALSE`,
-#'   this argument can be `NULL`.
+#' @param knockoff Knockoff realizations for the `agent` variable. Each column is 
+#'   a Monte Carlo realization generated using the `agent` and `conds` variables.
+#' @param null_knockoff (optional) Knockoff realizations generated under the null 
+#'   setting where all variables (`target`, `agent`, `conds`) are jointly used to 
+#'   construct knockoffs. Each column represents one Monte Carlo realization. Must 
+#'   be `NULL` if `contain_null = FALSE`.
 #' @param type (optional) Estimation method: `"disc"` for discrete mutual information
 #'   or `"cont"` for continuous mutual information (KSG estimator).
 #' @param nboots (optional) Number of permutations used in the sign-flipping permutation
 #'   test for evaluating the significance of the mean information difference.
 ##' @param k (optional) For `type = "cont"`, the number of nearest neighbors used by
-#'   the continuous conditional mutual information estimator.
-#'   For `type = "disc"`, the number of bins used for discretization.
+#'   the continuous conditional mutual information estimator. For `type = "disc"`, the 
+#'   number of bins used for discretization.
 #' @param seed (optional) Random seed used for permutation test.
-#' @param contain_null (optional) Logical. If `TRUE`, the test statistic is
-#'   computed using knockoffs generated under the null model (provided in
-#'   `null_knockoff`). In this case the difference is defined as
-#'   \eqn{I(Y; X_{null} | Z) - I(Y; X_{knockoff} | Z)}.
-#'   If `FALSE`, the original conditional mutual information
-#'   \eqn{I(Y; X | Z)} is used instead and compared against the knockoff
-#'   estimates \eqn{I(Y; X_{knockoff} | Z)}.
+#' @param contain_null (optional) Logical. If `TRUE`, the test statistic is computed as 
+#' \eqn{I(Y; X_{null} | Z) - I(Y; X_{knockoff} | Z)}. If `FALSE`, it is computed as 
+#' \eqn{I(Y; X | Z) - I(Y; X_{knockoff} | Z)}.
 #'
 #' @returns A named numeric vector.
 #' @export
@@ -41,12 +35,12 @@
 #' infoxtr::kocmi(mat, 1, 2, 3, kn1, kn2)
 #' 
 kocmi = \(data, target, agent, conds, knockoff, null_knockoff = NULL,
-          type = c("cont", "disc"), nboots = 1e4, k = 3, threads = 1,
+          type = c("cont", "disc"), nboots = 1e2, k = 3, threads = 1,
           seed = 42, base = exp(1), method = "equal", contain_null = TRUE) {
   type = match.arg(type)
   mat = .convert2mat(data, contain_type = FALSE)
   knockoff = .convert2mat(knockoff, contain_type = FALSE)
-  null_knockoff = .convert2mat(null_knockoff, contain_type = FALSE)
+  if (!is.null(null_knockoff)) null_knockoff = .convert2mat(null_knockoff, contain_type = FALSE)
   return(RcppKOCMI(mat, abs(target), abs(agent), abs(conds), knockoff, null_knockoff, 
                    type, nboots, k, 0, threads, seed, base, method, contain_null))
 }