distl2dnorm  R Documentation 
L^2
distance between L^2
normed probability densities
L^2
distance between two multivariate (p > 1
) or univariate (dimension: p = 1
) L^2
normed probability densities, estimated from samples, where a L^2
normed probability density is the original probability density function divided by its L^2
norm.
distl2dnorm(x1, x2, method = "gaussiand", check = FALSE, varw1 = NULL, varw2 = NULL)
x1, x2 
the samples from the probability densities (see 
method 
string. It can be:

check 
logical. When Notice that if 
varw1, varw2 
the bandwidths when the densities are estimated by the kernel method (see 
Given densities f_1
and f_2
, the function distl2dnormpar
computes the distance between the L^2
normed densities f_1 / f_1
and f_2 / f_2
:
2  2 <f_1, f_2> / (f_1 f_2)
For some information about the method used to compute the L^2
inner product or about the arguments, see l2d
.
The L^2
distance between the two L^2
normed densities.
Be careful! If check = FALSE
and one smoothing bandwidth matrix is degenerate, the result returned can not be considered.
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine DemotesMainard
distl2d
for the distance between two probability densities.
matdistl2dnorm
in order to compute pairwise distances between several L^2
normed densities.
require(MASS)
m1 < c(0,0)
v1 < matrix(c(1,0,0,1),ncol = 2)
m2 < c(0,1)
v2 < matrix(c(4,1,1,9),ncol = 2)
x1 < mvrnorm(n = 3,mu = m1,Sigma = v1)
x2 < mvrnorm(n = 5, mu = m2, Sigma = v2)
distl2dnorm(x1, x2, method = "gaussiand")
distl2dnorm(x1, x2, method = "kern")
distl2dnorm(x1, x2, method = "kern", varw1 = v1, varw2 = v2)
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