matdistl2dpar: Matrix of L^2 distances between Gaussian densities given...

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Matrix of L^2 distances between Gaussian densities given their parameters

Description

Computes the matrix of the L^2 distances between several multivariate (p > 1) or univariate (p = 1) Gaussian densities, given their parameters (mean vectors and covariance matrices if the densities are multivariate, or means and variances if univariate).

Usage

matdistl2dpar(meanL, varL)

Arguments

meanL	list of the means (p = 1) or vector means (p > 1) of the Gaussian densities.
varL	list of the variances (p = 1) or covariance matrices (p > 1) of the Gaussian densities.

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

distl2dpar.

matdistl2d for the distance matrix between probability densities which are estimated from the data.

Examples

    data(roses)
    
    # Multivariate:
    X <- roses[,c("Sha","Den","Sym","rose")]
    summary(X)
    mean.X <- as.list(by(X[, 1:3], X$rose, colMeans))
    var.X <- as.list(by(X[, 1:3], X$rose, var))
    
    # Gaussian densities, given parameters
    matdistl2dpar(mean.X, var.X)

    # Univariate :
    X1 <- roses[,c("Sha","rose")]
    summary(X1)
    mean.X1 <- by(X1$Sha, X1$rose, mean)
    var.X1 <- by(X1$Sha, X1$rose, var)
    
    # Gaussian densities, given parameters
    matdistl2dpar(mean.X1, var.X1)

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