Rename ori abreviates into ort

R/miic.R

@@ -178,30 +178,30 @@
 #' 3-point information of unshielded triples and, in temporal mode, using time.
 #' If set to FALSE, the orientation step is not performed.
 #'
-#' @param ori_proba_ratio [a floating point between 0 and 1, optional,
+#' @param ort_proba_ratio [a floating point between 0 and 1, optional,
 #' 1 by default]
 #'
 #' The threshold when deducing the type of an edge tip (head/tail)
 #' from the probability of orientation.
 #' For a given edge tip, denote by p the probability of it being a head,
-#' the orientation is accepted if (1 - p) / p < \emph{ori_proba_ratio}.
+#' the orientation is accepted if (1 - p) / p < \emph{ort_proba_ratio}.
 #' 0 means reject all orientations, 1 means accept all orientations.
 #'
-#' @param ori_consensus_ratio [a floating point between 0 and 1, optional,
+#' @param ort_consensus_ratio [a floating point between 0 and 1, optional,
 #' NULL by default]
 #' Used to determine if orientations correspond to genuine causal edges
 #' and, when consistency is activated, to deduce the orientations in
 #' the consensus graph.\cr
 #' Oriented edges will be marked as genuine causal when:
-#'      \eqn{ (1 - p_{head}) / p_{head} < } \emph{ori_consensus_ratio}
-#' and  \eqn{ p_{tail} / (1 - p_{tail}) < } \emph{ori_consensus_ratio}.\cr
-#' When consistency is activated, \emph{ori_consensus_ratio} is used as
+#'      \eqn{ (1 - p_{head}) / p_{head} < } \emph{ort_consensus_ratio}
+#' and  \eqn{ p_{tail} / (1 - p_{tail}) < } \emph{ort_consensus_ratio}.\cr
+#' When consistency is activated, \emph{ort_consensus_ratio} is used as
 #' threshold when deducing the type of an consensus edge tip (head/tail)
 #' from the average probability of orientations over the cycle of graphs.
 #' For a given edge tip, denote by p the average probability of it being a head,
-#' the orientation is accepted if (1 - p) / p < \emph{ori_consensus_ratio}.\cr
-#' If not supplied, the \emph{ori_consensus_ratio} will be initialized with
-#' the \emph{ori_proba_ratio} value.
+#' the orientation is accepted if (1 - p) / p < \emph{ort_consensus_ratio}.\cr
+#' If not supplied, the \emph{ort_consensus_ratio} will be initialized with
+#' the \emph{ort_proba_ratio} value.
 #'
 #' @param propagation [a boolean value, optional, FALSE by default]
 #'
@@ -449,11 +449,11 @@
 #'  arrow tips of an edge, based on the probabilities given in the columns
 #'  \emph{p_y2x} and \emph{p_x2y}. TRUE: when the edges is directed
 #'  and both the head and the tail are set with high probabilities
-#'  (adjustable with the \emph{ori_consensus_ratio} parameter),
+#'  (adjustable with the \emph{ort_consensus_ratio} parameter),
 #'  FALSE otherwise or NA if the edge is not retained.
 #'  More formally, an oriented edge is marked as genuine causal when
-#'       \eqn{ (1 - p_{head}) / p_{head} < } \emph{ori_consensus_ratio}
-#'  and  \eqn{ p_{tail} / (1 - p_{tail}) < } \emph{ori_consensus_ratio}.\cr
+#'       \eqn{ (1 - p_{head}) / p_{head} < } \emph{ort_consensus_ratio}
+#'  and  \eqn{ p_{tail} / (1 - p_{tail}) < } \emph{ort_consensus_ratio}.\cr
 #'  A directed edge not marked as genuine causal indicates that only
 #'  the head is set with high probability, while the tail probability
 #'  is not extreme enough to be either head or tail. In this case,
@@ -465,7 +465,7 @@
 #'  consistency is not activated or, when consistency is on,
 #'  if there is only one graph returned (no cycle).
 #'  When computed, indicates the consensus orientation of the edge
-#'  determined from the consensus skeleton and the \emph{ori_consensus_ratio}
+#'  determined from the consensus skeleton and the \emph{ort_consensus_ratio}
 #'  threshold on averaged orientation probabilities over the cycle of graphs.
 #'  Possible values are 0: not connected, 1: not oriented, -2 or 2: oriented
 #'  and 6: bi-directional (latent variable).}
@@ -700,8 +700,8 @@ miic <- function(input_data,
                  n_threads = 1,
                  cplx = "nml",
                  orientation = TRUE,
-                 ori_proba_ratio = 1,
-                 ori_consensus_ratio = NULL,
+                 ort_proba_ratio = 1,
+                 ort_consensus_ratio = NULL,
                  propagation = FALSE,
                  latent = "orientation",
                  n_eff = -1,
@@ -737,8 +737,8 @@ miic <- function(input_data,
                               n_threads = n_threads,
                               cplx = cplx,
                               orientation = orientation,
-                              ori_proba_ratio = ori_proba_ratio,
-                              ori_consensus_ratio = ori_consensus_ratio,
+                              ort_proba_ratio = ort_proba_ratio,
+                              ort_consensus_ratio = ort_consensus_ratio,
                               propagation = propagation,
                               latent = latent,
                               n_eff = n_eff,
@@ -836,7 +836,7 @@ miic <- function(input_data,
                            black_box = black_box,
                            n_shuffles = params$n_shuffles,
                            orientation = params$orientation,
-                           ori_proba_ratio = params$ori_proba_ratio,
+                           ort_proba_ratio = params$ort_proba_ratio,
                            propagation = params$propagation,
                            conf_threshold = params$conf_threshold,
                            verbose = params$verbose,
@@ -865,7 +865,7 @@ miic <- function(input_data,
     true_edges = true_edges,
     state_order = state_order,
     consensus_threshold = params$consensus_threshold,
-    ori_consensus_ratio = params$ori_consensus_ratio,
+    ort_consensus_ratio = params$ort_consensus_ratio,
     latent = (params$latent != "no"),
     propagation = params$propagation)
 

R/miic.reconstruct.R

@@ -10,7 +10,7 @@ miic.reconstruct <- function(input_data = NULL,
                              latent = "orientation",
                              n_shuffles = 0,
                              orientation = TRUE,
-                             ori_proba_ratio = 1,
+                             ort_proba_ratio = 1,
                              propagation = FALSE,
                              conf_threshold = 0,
                              verbose = FALSE,
@@ -72,7 +72,7 @@ miic.reconstruct <- function(input_data = NULL,
     "n_threads" = n_threads,
     "no_init_eta" = FALSE,
     "orientation" = orientation,
-    "ori_proba_ratio" = ori_proba_ratio,
+    "ort_proba_ratio" = ort_proba_ratio,
     "propagation" = propagation,
     "test_mar" = test_mar,
     "mode" = mode,
@@ -175,7 +175,7 @@ miic.reconstruct <- function(input_data = NULL,
 
   res$time <- stats::setNames(
     as.numeric(time),
-    c("init", "iter", "cut", "ori", "cpp")
+    c("init", "iter", "cut", "ort", "cpp")
   )
 
   # create the data frame of the structures after orientation

R/miic.utils.R

@@ -1041,7 +1041,7 @@ test_param_wrong_float <- function (value, min=NA, max=NA)
 # Returns: a list with all the parameters, eventually modified or initialized
 #-------------------------------------------------------------------------------
 check_parameters <- function (input_data, n_threads, cplx,
-  orientation, ori_proba_ratio, ori_consensus_ratio, propagation, latent,
+  orientation, ort_proba_ratio, ort_consensus_ratio, propagation, latent,
   n_eff, n_shuffles, conf_threshold, sample_weights, test_mar,
   consistent, max_iteration, consensus_threshold,
   mode, negative_info, verbose) {
@@ -1050,28 +1050,28 @@ check_parameters <- function (input_data, n_threads, cplx,
   list_ret$cplx = check_param_string (cplx, "complexity", c("nml", "mdl"))
   list_ret$orientation = check_param_logical (orientation, "orientation", TRUE)
 
-  if ( test_param_wrong_float (ori_proba_ratio, min=0, max=1) )
+  if ( test_param_wrong_float (ort_proba_ratio, min=0, max=1) )
     {
-    miic_warning ("parameters", "supplied value ", ori_proba_ratio,
-      " for the orientation probabilty ratio parameter is invalid.",
+    miic_warning ("parameters", "supplied value ", ort_proba_ratio,
+      " for the orientation probability ratio parameter is invalid.",
       " It must be a floating point between 0 and 1.",
       " The default value (1) will be used.")
-    ori_proba_ratio = 1
+    ort_proba_ratio = 1
     }
-  list_ret$ori_proba_ratio = ori_proba_ratio
+  list_ret$ort_proba_ratio = ort_proba_ratio
 
-  if ( is.null (ori_consensus_ratio) )
-    ori_consensus_ratio = list_ret$ori_proba_ratio
-  else if ( test_param_wrong_float (ori_consensus_ratio, min=0, max=1) )
+  if ( is.null (ort_consensus_ratio) )
+    ort_consensus_ratio = list_ret$ort_proba_ratio
+  else if ( test_param_wrong_float (ort_consensus_ratio, min=0, max=1) )
     {
-    miic_warning ("parameters", "supplied value ", ori_consensus_ratio,
+    miic_warning ("parameters", "supplied value ", ort_consensus_ratio,
       " for the orientation concensus ratio parameter is invalid.",
       " It must be a floating point between 0 and 1.",
       " The default value (same as orientation probabilty ratio: ",
-      ori_proba_ratio, ") will be used.")
-    ori_consensus_ratio = list_ret$ori_proba_ratio
+      ort_proba_ratio, ") will be used.")
+    ort_consensus_ratio = list_ret$ort_proba_ratio
     }
-  list_ret$ori_consensus_ratio = ori_consensus_ratio
+  list_ret$ort_consensus_ratio = ort_consensus_ratio
 
   list_ret$propagation = check_param_logical (propagation, "propagation", FALSE)
   list_ret$latent = check_param_string (latent, "latent", MIIC_VALID_LATENT)

R/parseResults.R

@@ -17,7 +17,7 @@
 # - state_order: the state order data frame used, optional, NULL by default.
 # - consensus_threshold: a float, optional, 0.8 by default. Used when
 #   consistency is activated to construct the consensus graph skeleton.
-# - ori_consensus_ratio: a float, optional, 0.1 by default. Used to determine
+# - ort_consensus_ratio: a float, optional, 0.1 by default. Used to determine
 #   if oriented edges are genuine causal and, when consistency is activated,
 #   to determine the consensus graph orientations.
 # - latent: a boolean, optional, TRUE by default. Indicates if latent
@@ -76,7 +76,7 @@
 #-------------------------------------------------------------------------------
 summarizeResults = function (observations, results,
                              true_edges = NULL, state_order = NULL,
-                             consensus_threshold = 0.8, ori_consensus_ratio = 0.1,
+                             consensus_threshold = 0.8, ort_consensus_ratio = 0.1,
                              latent = TRUE, propagation = FALSE)
   {
   # Keep only edges remaining and edges removed using conditioning
@@ -296,25 +296,25 @@ summarizeResults = function (observations, results,
       ratio_x2y <- (1 - proba_x2y) / proba_x2y
       ratio_y2x <- (1 - proba_y2x) / proba_y2x
 
-      if (  (ratio_x2y < ori_consensus_ratio)
-         && (ratio_y2x < ori_consensus_ratio) )
+      if (  (ratio_x2y < ort_consensus_ratio)
+         && (ratio_y2x < ort_consensus_ratio) )
         summary[i, "ort_consensus"] <- 6
-      else if (  (ratio_x2y < ori_consensus_ratio)
-              && (ratio_y2x >= ori_consensus_ratio) )
+      else if (  (ratio_x2y < ort_consensus_ratio)
+              && (ratio_y2x >= ort_consensus_ratio) )
         {
         summary[i, "ort_consensus"] <- 2
-        if (1 / ratio_y2x < ori_consensus_ratio && causality_deducible)
+        if (1 / ratio_y2x < ort_consensus_ratio && causality_deducible)
           {
           summary[i, "is_causal_consensus"] <- TRUE
           if (row$ort_inferred == 2)
             summary[i, "is_causal"] <- TRUE
           }
         }
-      else if (  (ratio_y2x < ori_consensus_ratio)
-              && (ratio_x2y >= ori_consensus_ratio) )
+      else if (  (ratio_y2x < ort_consensus_ratio)
+              && (ratio_x2y >= ort_consensus_ratio) )
         {
         summary[i, "ort_consensus"] <- -2
-        if (1 / ratio_x2y < ori_consensus_ratio && causality_deducible)
+        if (1 / ratio_x2y < ort_consensus_ratio && causality_deducible)
           {
           summary[i, "is_causal_consensus"] <- TRUE
           if (row$ort_inferred == -2)
@@ -343,12 +343,12 @@ summarizeResults = function (observations, results,
       ratio_x2y <- (1 - proba_x2y) / proba_x2y
       ratio_y2x <- (1 - proba_y2x) / proba_y2x
       if (  (row$ort_inferred == 2)
-         && (ratio_x2y < ori_consensus_ratio)
-         && (1 / ratio_y2x < ori_consensus_ratio) )
+         && (ratio_x2y < ort_consensus_ratio)
+         && (1 / ratio_y2x < ort_consensus_ratio) )
         summary[i, "is_causal"] <- TRUE
       if (  (row$ort_inferred == -2)
-         && (ratio_y2x < ori_consensus_ratio)
-         && (1 / ratio_x2y < ori_consensus_ratio) )
+         && (ratio_y2x < ort_consensus_ratio)
+         && (1 / ratio_x2y < ort_consensus_ratio) )
         summary[i, "is_causal"] <- TRUE
       }
     }

R/tmiic.plot.R

@@ -544,7 +544,7 @@ tmiic_compute_row_layout_layers <- function (tmiic_res)
 #-------------------------------------------------------------------------------
 # Internal function to precompute a layout suited for the display of raw and
 # lagged graphs
-# This function computes the layout using Sugiyama algorihtm to
+# This function computes the layout using Sugiyama algorithm to
 # minimize crossing edges
 #
 # param: tmiic_res, a tmiic objectreturned by the execution of miic

R/tmiic.utils.R

@@ -726,7 +726,7 @@ tmiic_extract_trajectories <- function (input_data, check=T)
                       FUN=function (x) { return (x[1,1] != 1) } ) ) )
     if (length (wrong_starts) > 0)
       miic_warning ("check trajectories", length (wrong_starts),
-        " trajectorie(s) don't start with 1 as first time step value")
+        " trajectories don't start with 1 as first time step value")
     max_nb_ts = max (unlist (lapply (list_ts, FUN=nrow) ) )
     if (max_nb_ts == 1)
       miic_error ("trajectories check",

src/environment.h

@@ -46,7 +46,7 @@ struct Environment {
   vector<Node> nodes;
   Grid2d<Edge> edges;
   bool orientation = false;
-  double ori_proba_ratio = 1;
+  double ort_proba_ratio = 1;
   bool propagation = false;
   // Level of consistency required for the graph
   // 0: no consistency requirement

src/orientation.cpp

@@ -29,8 +29,8 @@ namespace {
 
 constexpr double kEpsI3 = 1.0e-10;
 
-bool acceptProba(double proba, double ori_proba_ratio) {
-  return (1 - proba) / proba < ori_proba_ratio;
+bool acceptProba(double proba, double ort_proba_ratio) {
+  return (1 - proba) / proba < ort_proba_ratio;
 }
 
 }  // anonymous namespace
@@ -39,10 +39,10 @@ bool acceptProba(double proba, double ori_proba_ratio) {
 // x2y: probability that there is an arrow from node x to y
 void updateAdj(Environment& env, int x, int y, double y2x, double x2y) {
   env.edges(x, y).proba_head = x2y;
-  if (acceptProba(x2y, env.ori_proba_ratio))
+  if (acceptProba(x2y, env.ort_proba_ratio))
     env.edges(x, y).status = 2;
   env.edges(y, x).proba_head = y2x;
-  if (acceptProba(y2x, env.ori_proba_ratio))
+  if (acceptProba(y2x, env.ort_proba_ratio))
     env.edges(y, x).status = 2;
 }
 

src/r_cpp_interface.cpp

@@ -49,8 +49,8 @@ void setEnvironmentFromR(const Rcpp::List& input_data,
   if (arg_list.containsElementNamed("orientation"))
     environment.orientation = as<bool>(arg_list["orientation"]);
 
-  if (arg_list.containsElementNamed("ori_proba_ratio"))
-    environment.ori_proba_ratio = as<double>(arg_list["ori_proba_ratio"]);
+  if (arg_list.containsElementNamed("ort_proba_ratio"))
+    environment.ort_proba_ratio = as<double>(arg_list["ort_proba_ratio"]);
 
   if (arg_list.containsElementNamed("propagation"))
     environment.propagation = as<bool>(arg_list["propagation"]);