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General Linear Blend Frequency Polygon (GLBFP) estimator at a single point

Source: R/GLBFP.R
GLBFP.Rd

Computes the GLBFP density estimate at point x.

Usage

GLBFP(
  x,
  data,
  b = compute_bi_optim(data, m = rep(1, ncol(data))),
  m = rep(1, ncol(data)),
  min_vals = apply(data, 2, min),
  max_vals = apply(data, 2, max)
)

# S3 method for class 'GLBFP'
print(x, ...)

Arguments

x

Object returned by GLBFP().

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).

min_vals

Numeric vector of lower grid bounds (length d).

max_vals

Numeric vector of upper grid bounds (length d).

...

Additional arguments (unused).

Value

A list with class c("glbfp_fit", "GLBFP") containing: x, estimation, sd, IC, b, m, method, and dimension.

Details

GLBFP() generalizes the linear blend frequency polygon workflow through the positive integer shift vector m. Missing and non-finite values are not accepted; remove or impute them before calling the estimator.

Methods (by generic)

  • print(GLBFP): Print method for object of class "GLBFP".

References

Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley. doi:10.1002/9780470316849.

The complete methodological citation for GLBFP has not yet been verified in this repository. Add it before using this help page as publication text.

See also

GLBFP_estimate(), ASH(), LBFP(), compute_bi_optim()

Examples

x <- c(200, 30)
b <- c(0.5, 0.5)
m <- c(1, 1)
GLBFP(x, ashua[, -3], b = b, m = m)
#> GLBFP Density Estimation:
#> Point: (200, 30) 
#> Estimated density: 0.00344498 
#> Estimated standard error: 0.00107138 
#> 95% confidence interval: 0.00338158, 0.00350837 
#> Bandwidths (b): 0.5, 0.5 
#> Shifts (m): 1, 1 
#> Relative grid coordinate (u): 0.50, 0.84