Computes GLBFP density estimates on a regular or user-supplied grid.
Usage
GLBFP_estimate(
data,
b = compute_bi_optim(data, m = rep(1, ncol(data))),
m = rep(1, ncol(data)),
grid_size = 20,
grid_points = NULL,
min_vals = apply(data, 2, min),
max_vals = apply(data, 2, max)
)
# S3 method for class 'GLBFP_estimate'
print(x, ...)
# S3 method for class 'GLBFP_estimate'
plot(x, contour = FALSE, ...)Arguments
- data
Numeric matrix or data frame of observations (
n x d).- b
Positive numeric vector of bandwidths (length
d).- m
Positive integer vector of shifts (length
d).- grid_size
Integer number of grid points per dimension when
grid_points = NULL.- grid_points
Optional matrix/data frame of explicit evaluation points.
- min_vals
Numeric vector of lower grid bounds (length
d).- max_vals
Numeric vector of upper grid bounds (length
d).- x
Object returned by
GLBFP_estimate().- ...
Additional arguments (unused).
- contour
If
TRUE, draw a contour-like 2D representation for 2D data.
Value
A list with class c("glbfp_grid", "GLBFP_estimate") containing
grid coordinates, densities, uncertainty estimates, and grid metadata.
Details
When grid_points is NULL, a regular grid is constructed from min_vals to
max_vals. Custom grids may be irregular; in that case plotting uses point or
scatter representations instead of a surface.
Methods (by generic)
print(GLBFP_estimate): Print method for object of class"GLBFP_estimate".plot(GLBFP_estimate): Plot method for object of class"GLBFP_estimate".
Examples
b <- c(0.5, 0.5)
# Use a small, representative subset so examples remain fast in checks.
sample_data <- as.matrix(ashua[seq_len(120), -3])
out <- GLBFP_estimate(sample_data, b = b, m = c(1, 1), grid_size = 10)
out
#> GLBFP Density Estimation on Grid:
#> Grid points: 100
#> Dimensions: 2
#> Grid type: rectangular
#> Density range: 0.000, 0.575
#> Bandwidths (b): 0.5, 0.5
#> Shifts (m): 1, 1
#> Median visited cells: 0
#> Median prefix nodes: 6