# wKDE: Weighted kernel density estimator (wKDE) In LICORS: Light Cone Reconstruction of States - Predictive State Estimation From Spatio-Temporal Data

## Description

`wKDE` gives a (weighted) kernel density estimate (KDE) for univariate data.

If weights are not provided, all samples count equally. It evaluates on new data point by interpolation (using `approx`).

`mv_KDE` uses the `locfit.raw` function in the locfit package to estimate KDEs for multivariate data. Note: Use this only for small dimensions, very slow otherwise.

## Usage

 ```1 2 3``` ```wKDE(x, eval.points = x, weights = NULL, kernel = "gaussian", bw = "nrd0") mv_wKDE(x, eval.points = x, weights = NULL, kernel = "gaussian") ```

## Arguments

 `x` data vector `eval.points` points where the density should be evaluated. Default: `eval.points = x`. `weights` vector of weights. Same length as `x`. Default: `weights=NULL` - equal weight for each sample. `kernel` type of kernel. Default: `kernel='Gaussian'`. See `density` and `locfit.raw` for additional options. `bw` bandwidth. Either a character string indicating the method to use or a real number. Default: `bw="nrd0"`. Again see `density` for other options.

## Value

A vector of length `length(eval.points)` (or `nrow(eval.points)`) with the probabilities of each point given the nonparametric fit on `x`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```### Univariate example ### xx <- sort(c(rnorm(100, mean = 1), runif(100))) plot(xx, wKDE(xx), type = "l") yy <- sort(runif(50, -1, 4) - 1) lines(yy, wKDE(xx, yy), col = 2) ### Multivariate example ### XX <- matrix(rnorm(100), ncol = 2) YY <- matrix(runif(40), ncol = 2) dens.object <- mv_wKDE(XX) plot(dens.object) points(mv_wKDE(XX, YY), col = 2, ylab = "") ```

LICORS documentation built on May 29, 2017, 1:02 p.m.