Description Usage Arguments Value Author(s) See Also Examples
Computes the matrix of the L^2 inner products between several multivariate (p > 1) or univariate (p = 1) probability densities, estimated from samples, using l2d
.
1 
x 
object of class "folder" containing the data. Its elements have only numeric variables (observations of the probability densities). If there are non numeric variables, there is an error. 
method 
string. It can be:

varwL 
list of matrices. The smoothing bandwidths for the estimation of each probability density. If they are omitted, the smoothing bandwidths are computed using the normal reference rule matrix bandwidth (see details of the 
Positive symmetric matrix whose order is equal to the number of densities, consisting of the pairwise inner products between the probability densities.
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine DemotesMainard
l2d
.
matipl2dpar
when the probability densities are Gaussian, given the parameters (means and variances).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  data(roses)
# Multivariate:
X < as.folder(roses[,c("Sha","Den","Sym","rose")], groups = "rose")
summary(X)
mean.X < mean(X)
var.X < var.folder(X)
# Parametrically estimated Gaussian densities:
matipl2d(X)
# Estimated densities using the Gaussian kernel method (normal reference rule bandwidth):
matipl2d(X, method = "kern")
# Estimated densities using the Gaussian kernel method (bandwidth provided):
matipl2d(X, method = "kern", varwL = var.X)
# Univariate :
X1 < as.folder(roses[,c("Sha","rose")], groups = "rose")
summary(X1)
mean.X1 < mean(X1)
var.X1 < var.folder(X1)
# Parametrically estimated Gaussian densities:
matipl2d(X1)
# Estimated densities using the Gaussian kernel method (normal reference rule bandwidth):
matipl2d(X1, method = "kern")
# Estimated densities using the Gaussian kernel method (bandwidth provided):
matipl2d(X1, method = "kern", varwL = var.X1)

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