dlg_trivariate: Trivariate density estimation
In hotneim/lg: Locally Gaussian Distributions: Estimation and Methods

Description Usage Arguments Details Value Examples

dlg_trivariate returns the locally Gaussian density estimate of a trivariate distribution on a given grid.

dlg_trivariate(
  x,
  eval_points = NULL,
  grid_size = 15,
  bw = c(1, 1, 1),
  est_method = "trivariate",
  run_checks = TRUE
)

`x`	The data matrix (or data frame). Must have exactly 2 columns.
`eval_points`	The grid where the density should be estimated. Must have exactly 2 columns.
`grid_size`	If `eval_points` is not supplied, then the function will create a suitable grid diagonally through the data, with this many grid points.
`bw`	The two bandwidths, a numeric vector of length 2.
`est_method`	The estimation method, must either be "1par" for estimation with just the local correlation, or "5par" for a full locally Gaussian fit with all 5 parameters.
`run_checks`	Logical. Should sanity checks be run on the arguments? Useful to disable this when doing cross-validation for example.

In some applications it may be desired to produce a full locally Gaussian fit of a trivariate density function without having to resort to bivariate approximations. This function takes a trivariate data set, x, and a trivariate set of grid points eval_points, and returns the trivariate, locally Gaussian density estimate in these points. We also need a vector of bandwidths, bw, with three elements, and an estimation method est_method, which in this case is fixed at "trivariate", and included only to be fully compatible with the other methods in this package.

This function will only work on the marginally standard normal scale! Please use the wrapper function dlg() for density estimation. This will ensure that all parameters have proper values.

A list including the data set $x, the grid $eval_points, the bandwidths $bw, as well as a matrix of the estimated parameter estimates $par_est and the estimated bivariate density $f_est.

  x <- cbind(rnorm(100), rnorm(100), rnorm(100))
  bw <- c(1, 1, 1)
  eval_points <- cbind(seq(-4, 4, 1), seq(-4, 4, 1), seq(-4, 4, 1))

  estimate <- dlg_trivariate(x, eval_points = eval_points, bw = bw)