edited alpha_vals names

R/exploreWSIRParams.R

@@ -23,12 +23,12 @@
 #' format: for the previous example, you could write
 #' samples = c(rep("sample 1", 5000), rep("sample 2", 5000)), and the result
 #' would be the same.
-#' @param alpha_vals vector of numbers as the values of parameter alpha to use
+#' @param optim_alpha vector of numbers as the values of parameter alpha to use
 #' in WSIR. 0 gives Sliced Inverse Regression
 #' (SIR) implementation, and larger values represent stronger spatial
 #' correlation. Suggest to use integers for interpretability,
 #' but can use non-integers. Values must be non-negative.
-#' @param slice_vals vector of integers as the values of parameter slices to
+#' @param optim_slices vector of integers as the values of parameter slices to
 #' use in WSIR. Suggest maximum value in the vector to
 #' be no more than around \eqn{\sqrt{n/20}}, as this upper bound ensures an
 #' average of at least 10 cells per tile in the training set.
@@ -58,7 +58,7 @@
 #' 4) "best_slices" returns the integer for the best slices value among the
 #' values that were tested according to selected metric.
 #' 5) "results_dataframe" returns the results dataframe used to create "plot".
-#' This dataframe has length(alpha_vals)*length(slice_vals) rows,
+#' This dataframe has length(optim_alpha)*length(optim_slices) rows,
 #' where one is for each combination of parameters slices and alpha. There is
 #' one column for "alpha", one for "slices" and one
 #' for each of the evaluation metrics selected in "metrics" argument. Column
@@ -72,8 +72,8 @@
 #' data(MouseData)
 #' explore_params = exploreWSIRParams(X = sample1_exprs,
 #'   coords = sample1_coords,
-#'   alpha_vals = c(0,2,4,8),
-#'   slice_vals = c(3,6,10))
+#'   optim_alpha = c(0,2,4,8),
+#'   optim_slices = c(3,6,10))
 #' explore_params$plot
 #' explore_params$message
 #' best_alpha = explore_params$best_alpha
@@ -94,16 +94,16 @@
 exploreWSIRParams <- function(X,
                               coords,
                               samples = rep(1, nrow(coords)),
-                              alpha_vals = c(0,2,4,10),
-                              slice_vals = c(5,10,15,20),
+                              optim_alpha = c(0,2,4,10),
+                              optim_slices = c(5,10,15,20),
                               metric = "DC",
                               nrep = 5,
                               param = MulticoreParam(workers = 1),
                               ...
 ) {
 
   # vector of all parameter combinations
-  param_combinations <- expand.grid(slice = slice_vals, alpha = alpha_vals,
+  param_combinations <- expand.grid(slices = optim_slices, alpha = optim_alpha,
                                     metric = NA)
 
 
@@ -149,7 +149,7 @@ exploreWSIRParams <- function(X,
       wSIROptimisation(as.matrix(X_train),
                        coords_train, as.matrix(X_test),
                        coords_test,
-                       samples_train, param_combinations$slice[ii],
+                       samples_train, param_combinations$slices[ii],
                        param_combinations$alpha[ii],
                        evalmetrics = metric,
                        ...)
@@ -161,13 +161,13 @@ exploreWSIRParams <- function(X,
   res_df <- param_combinations
   best_metric_index <- which.max(res_df[, "metric"])
   best_alpha <- res_df[best_metric_index, "alpha"]
-  best_slices <- res_df[best_metric_index, "slice"]
+  best_slices <- res_df[best_metric_index, "slices"]
 
   message <- paste0("Optimal (alpha, slices) pair: (",
                     best_alpha, ", ", best_slices, ")")
 
   plot <- ggplot2::ggplot(res_df, mapping = aes(
-    x = .data$alpha, y = .data$slice, size = .data$metric)) +
+    x = .data$alpha, y = .data$slices, size = .data$metric)) +
     ggplot2::geom_point() +
     ggplot2::theme_classic() +
     ggplot2::ggtitle(

R/sirCategorical.R

@@ -9,9 +9,6 @@
 #' @param Y dataframe with 1 column named "coordinate" that is the tile
 #' allocation for each cell. There should
 #' be up to slices^2 unique tile IDs in this column.
-#' @param maxDirections Integer to specify maximum number of directions to
-#' retain in thee low-dimensional embedding
-#' of the data. Use if you need at most a certain number for a downstream task.
 #' @param W Weight matrix created by createWeightMatrix. Entry (i,j)
 #' represents the spatial correlation level
 #' between tiles i and j. The diagonal values should be all 1. If not
@@ -25,7 +22,7 @@
 
 sirCategorical <- function(X,
                            Y,
-                           maxDirections = 50,
+                           # maxDirections = 50,
                            W = NULL,
                            ...
 ) {

R/wSIR.R

@@ -66,17 +66,17 @@
 #' wsir_obj = wSIR(X = sample1_exprs,
 #'   coords = sample1_coords,
 #'   optim_params = TRUE,
-#'   alpha_vals = c(0,2,4),
-#'   slice_vals = c(3,6,10),
+#'   optim_alpha = c(0,2,4),
+#'   optim_slices = c(3,6,10),
 #'   metric = "DC",
 #'   nrep = 1) # create wsir object
 #'
 #' @export
 wSIR <- function(X,
                  coords,
                  optim_params = FALSE,
-                 alpha_vals = c(0,1,2,4,8,12),
-                 slice_vals = c(3,5,7,10,15,20),
+                 optim_alpha = c(0,1,2,4,8,12),
+                 optim_slices = c(3,5,7,10,15,20),
                  metric = "DC",
                  nrep = 5,
                  verbose = FALSE,
@@ -86,8 +86,8 @@ wSIR <- function(X,
     if (verbose) message("Optimising parameters...")
     optim_obj <- exploreWSIRParams(X = X,
                                    coords = coords,
-                                   alpha_vals = alpha_vals,
-                                   slice_vals = slice_vals,
+                                   optim_alpha = optim_alpha,
+                                   optim_slices = optim_slices,
                                    metric = metric,
                                    nrep = nrep,
                                    ...)