neuster Stand

R/singular_values.R

@@ -40,6 +40,9 @@ source(here::here("R", "graphon_distribution.R"))
 #' @param Fv     Cumulative distribution function of the latent variable
 #'               \eqn{v}.  Also has to be vectorised.  Typical examples are
 #'               `pnorm`, `pexp`, ….
+#' @param fX Optional function(z) returning the *known* pdf of the *scalar*
+#'            covariate when `ncol(X_matrix)==1 && length(a)==1`.  If supplied,
+#'            the analytic CDF `pgraphon_analytical_1d` is used.
 #' @param sample_X_fn
 #'               Function with a single argument `n`.  It must return an
 #'               \eqn{n \times p} matrix (or an object coercible to a matrix) of
@@ -111,6 +114,7 @@ compute_matrix <- function(
     K,
     fv,
     Fv,
+    fX=NULL,
     sample_X_fn=NULL,
     matrix_X = NULL,
     guard = sqrt(.Machine$double.eps),
@@ -127,6 +131,7 @@ compute_matrix <- function(
   if (!is.null(matrix_X) && !is.matrix(matrix_X)) stop("matrix_X must be either null or a matrix")
   if (is.null(matrix_X) && is.null(sample_X_fn)) stop("Either 'matrix_X' or 'sample_X_fn' must be supplied!")
   if (!is.null(matrix_X) && !is.null(sample_X_fn)) warning("Both arguments 'matrix_X' and `sample_X_fn` is given. Priority is given by to the first!")
+  if (!is.null(fX) && !is.function(fX)) stop("'fX' must be a density function")
   
   ## 1.2 Generate the Matrix X of covariates ===================================
   # If the argument matrix_X is present, use this matrix, otherwise generate one
@@ -146,14 +151,18 @@ compute_matrix <- function(
   }
   
   ## 1.3 Create conditional density ============================================
-  empir_cond_density <- create_cond_density(a, fv, Fv, X)
+  # this is not used in the computation
+  # empir_cond_density <- create_cond_density(a, fv, Fv, X)
   
   ## 1.4 Compute the graphon quantiles =========================================
   k <- seq(0, K) / K
   if (!is.null(guard)) {
     k[1] <- guard
   }
-  graphon_quantiles <- qgraphon(k, a = a, Fv = Fv, X_matrix = X)
+  # here there is an automatic switch included, if fX is not null and we have a
+  # scalar case, then qpgrahon automatically switches to the analytical 
+  # expression. The intended use is for small values of n
+  graphon_quantiles <- qgraphon(k, a = a, Fv = Fv, X_matrix = X, fX= fX)
   
   ## 1.5 Build the matrix Q ====================================================
   inner_products = as.vector(X %*% a)