nonparametric kernel smoothing for JavaScript
Via npm:
npm install kernel-smooth
Require as follows:
var kernel = require('kernel-smooth');
Given input data xs
, a kernel function and a bandwidth (if not supplied,
a default value of 0.5 is used), this function returns a basic kernel density
estimator: a function of one variable, x
, which when invoked returns the
kernel density estimate for x
. The returned function can also be called with a
vector supplied as an argument for x
. In this case, the density is evaluated
is for each element of the vector and the vector of density estimates
is returned.
Given input predictors xs
and observed responses ys
, a kernel function
and a bandwidth (if not supplied, a default value of 0.5 is used),
this function returns the Nadaraya & Watson kernel regression estimator:
a function of one variable, x
, which when invoked returns the
estimate for y
. The returned function can also be called with a
vector supplied as an argument for x
. In this case, predictions are generated
for each element of the vector and the vector of predictions
is returned.
Similar to .regression(), except that Xs should be a 2d array containing multiple predictors. Each element of Xs
should has to be an array of length p
, with p
denoting the number of predictors. The returned estimator generates a prediction for a new data point x = (x_1, ..., x_p). If a 2d array is supplied instead, predictions are generated for multiple data points at once, where each row (= element of the outer array) is assumed to be a datum x = (x_1, ..., x_p).
For the kernel
parameter in above functions, you should supply a univariate function K(x)
which satisfies K(x) >= 0, integrates to one, has zero mean and unit variance.
See the functions in the exported .fun
object for a list of already implemented kernel functions.
This object of the module holds the following kernel functions to be used for kernel smoothing:
Gaussian kernel, pdf of standard normal distribution.
Boxcar kernel, defined as 0.5 if |x| <= 1 and 0 otherwise.
Epanechnikov kernel. Equal to zero if |x| > 1. Otherwise defined as 0.75 * (1 - x^2).
Tricube kernel function. Equal to zero if |x| > 1 and otherwise equal to (70/81) * (1-|x|^3)^3.
For input vector x, calculate the optimal bandwidthe using Silverman's rule of thumb. This utility function can be used to calculate an appropriate bandwidth for the case in which a Gaussian kernel is used and one has reason to believe that the data points x_i are drawn from a normal distribution.
MIT © Philipp Burckhardt