# MvtLogis: Multivariate Logistic Distribution In NonNorMvtDist: Multivariate Lomax (Pareto Type II) and Its Related Distributions

## Description

Calculation of density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for multivariate logistic distribution with vector parameter parm1 and vector parameter parm2.

## Usage

 1 2 3 4 5 6 7 8 9 dmvlogis(x, parm1 = rep(1, k), parm2 = rep(1, k), log = FALSE) pmvlogis(q, parm1 = rep(1, k), parm2 = rep(1, k)) qmvlogis(p, parm1 = rep(1, k), parm2 = rep(1, k), interval = c(0, 1e+08)) rmvlogis(n, parm1 = rep(1, k), parm2 = rep(1, k)) smvlogis(q, parm1 = rep(1, k), parm2 = rep(1, k)) 

## Arguments

 x vector or matrix of quantiles. If x is a matrix, each row vector constitutes a vector of quantiles for which the density f(x) is calculated (for i-th row x_i, f(x_i) is reported). parm1 a vector of location parameters, see parameter μ_i in Details. parm2 a vector of scale parameters, see parameters σ_i in Details. log logical; if TRUE, probability densities f are given as log(f). q a vector of quantiles. p a scalar value corresponding to probability. interval a vector containing the end-points of the interval to be searched. Default value is set as c(0, 1e8). n number of observations. k dimension of data or number of variates.

## Details

Bivariate logistic distribution was introduced by Gumbel (1961) and its multivariate generalization was given by Malik and Abraham (1973) as

f(x_1, \cdots, x_k) = \frac{k! \exp{(-∑_{i=1}^{k} \frac{x_i - μ_i}{σ_i})}}{[∏_{i=1}^{p} σ_i] [1 + ∑_{i=1}^{k} \exp{(-\frac{x_i - μ_i}{σ_i})}]^{1+k}},

where -∞<x_i, μ_i<∞, σ_i > 0, i=1,\cdots, k.

Cumulative distribution function F(x_1, …, x_k) is given as

F(x_1, \cdots, x_k) = ≤ft[1 + ∑_{i=1}^{k} \exp(-\frac{x_i-μ_i}{σ_i})\right]^{-1}.

Equicoordinate quantile is obtained by solving the following equation for q through the built-in one dimension root finding function uniroot:

\int_{-∞}^{q} \cdots \int_{-∞}^{q} f(x_1, \cdots, x_k) dx_k \cdots dx_1 = p,

where p is the given cumulative probability.

The survival function \bar{F}(x_1, \cdots, x_k) is obtained by the following formula related to cumulative distribution function F(x_1, …, x_k) (Joe, 1997)

\bar{F}(x_1, \cdots, x_k) = 1 + ∑_{S \in \mathcal{S}} (-1)^{|S|} F_S(x_j, j \in S).

Random numbers X_1, \cdots, X_k from multivariate logistic distribution can be generated through transformation of multivariate Lomax random variables Y_1, \cdots, Y_k by letting X_i=μ_i-σ_i\ln(θ_i Y_i), i = 1, \cdots, k; see Nayak (1987).

## Value

dmvlogis gives the numerical values of the probability density.

pmvlogis gives the cumulative probability.

qmvlogis gives the equicoordinate quantile.

rmvlogis generates random numbers.

smvlogis gives the value of survival function

## References

Gumbel, E.J. (1961). Bivariate logistic distribution. J. Am. Stat. Assoc., 56, 335-349.

Joe, H. (1997). Multivariate Models and Dependence Concepts. London: Chapman & Hall.

Malik, H. J. and Abraham, B. (1973). Multivariate logistic distributions. Ann. Statist. 3, 588-590.

Nayak, T. K. (1987). Multivariate Lomax Distribution: Properties and Usefulness in Reliability Theory. Journal of Applied Probability, Vol. 24, No. 1, 170-177.

uniroot for one dimensional root (zero) finding.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # Calculations for the multivariate logistic distribution with parameters: # mu1 = 0.5, mu2 = 1, mu3 = 2, sigma1 = 1, sigma2 = 2 and sigma3 = 3 # Vector of quantiles: c(3, 2, 1) dmvlogis(x = c(3, 2, 1), parm1 = c(0.5, 1, 2), parm2 = c(1, 2, 3)) # Density pmvlogis(q = c(3, 2, 1), parm1 = c(0.5, 1, 2), parm2 = c(1, 2, 3)) # Cumulative Probability # Equicoordinate quantile of cumulative probability 0.5 qmvlogis(p = 0.5, parm1 = c(0.5, 1, 2), parm2 = c(1, 2, 3)) # Random numbers generation with sample size 100 rmvlogis(n = 100, parm1 = c(0.5, 1, 2), parm2 = c(1, 2, 3)) smvlogis(q = c(3, 2, 1), parm1 = c(0.5, 1, 2), parm2 = c(1, 2, 3)) # Survival function