matdistl2d: Matrix of L^2 distances between probability densities

In dad: Three-Way / Multigroup Data Analysis Through Densities

Matrix of L^2 distances between probability densities

Description

Computes the matrix of the L^2 distances between several multivariate (p > 1) or univariate (p = 1) probability densities, estimated from samples.

Usage

matdistl2d(x, method = "gaussiand", varwL = NULL)

Arguments

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: "gaussiand" if the densities are considered to be Gaussian. "kern" if they are estimated using the Gaussian kernel method.
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 l2d function).

Value

Positive symmetric matrix whose order is equal to the number of densities, consisting of the pairwise distances between the probability densities.

Author(s)

Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine Demotes-Mainard

See Also

distl2d.

matdistl2dpar when the probability densities are Gaussian, given the parameters (means and variances).

Examples

    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:
    matdistl2d(X)
    
    ## Not run: 
    # Estimated densities using the Gaussian kernel method ()normal reference rule bandwidth):
    matdistl2d(X, method = "kern")   

    # Estimated densities using the Gaussian kernel method (bandwidth provided):
    matdistl2d(X, method = "kern", varwL = var.X)
    
## End(Not run)

    # 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:
    matdistl2d(X1)
    
    # Estimated densities using the Gaussian kernel method (normal reference rule bandwidth):
    matdistl2d(X1, method = "kern")
    
    # Estimated densities using the Gaussian kernel method (normal reference rule bandwidth):
    matdistl2d(X1, method = "kern", varwL = var.X1)

dad documentation built on Aug. 30, 2023, 5:06 p.m.