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experiments with rho_n

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This commit is contained in:
Niclas
2026-06-17 10:43:33 +02:00
parent d0e0c03428
commit 9d9d274487
2 changed files with 100 additions and 26 deletions
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19
R/singular_values.R
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@@ -33,10 +33,6 @@ source(here::here("R", "graphon_distribution.R"))
#' generated.
#' @param K Positive integer. Number of divisions of the unit interval;
#' the resulting grid has length `K+1`.
#' @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
#' covariate samples.
#' @param fv Density function of the latent variable \eqn{v}. Must be
#' vectorised (i.e. accept a numeric vector and return a numeric
#' vector of the same length). Typical examples are
@@ -44,8 +40,15 @@ 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 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
#' covariate samples. It can be NULL, but then `matrix_X` must be
#' given.
#' @param matrix_X matrix with the covariates at each node. Each row corresponds
#' to a single node with p attributes.
#' to a single node with p attributes. The default value is `NULL`. If it
#' is `NULL` then `sample_X_fun` must be given. If both parameters are provided,
#' then `matrix_X` is used.
#' @param guard Positive numeric guard value. Default is `sqrt(.Machine$double.eps)`,
#' which is about `1.5e‑8` on most platforms – small enough to be negligible
#' for most computations. If it is null, then it is not used.
@@ -106,9 +109,9 @@ compute_matrix <- function(
a,
n,
K,
sample_X_fn,
fv,
Fv,
sample_X_fn=NULL,
matrix_X = NULL,
guard = sqrt(.Machine$double.eps),
scaled = FALSE
@@ -118,10 +121,12 @@ compute_matrix <- function(
if (!is.numeric(a) || !is.vector(a)) stop("'a' must be a numeric vector")
if (!is.numeric(n) || length(n) != 1 || n <= 0) stop("'n' must be a positive integer")
if (!is.numeric(K) || length(K) != 1 || K <= 0) stop("'K' must be a positive integer")
if (!is.function(sample_X_fn)) stop("'sample_X_fn' must be a function")
if (!(is.function(sample_X_fn) || is.null(sample_X_fn))) stop("'sample_X_fn' must be a function or Null and matrix_X must be given")
if (!is.function(fv)) stop("'f_v' must be a function")
if (!is.function(Fv)) stop("'F_v' must be a 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!")
## 1.2 Generate the Matrix X of covariates ===================================
# If the argument matrix_X is present, use this matrix, otherwise generate one