mixbox (version 1.2.3)

configuration1: Output configuration.

Description

Output design for standard error of the standard error for parameters of the finite mixture models.

Usage

configuration1(Y, G, weight, mu, sigma, lambda, family, skewness, param, theta,
 ofim1_solve, sigma_arrange1, level)

Value

designated form for output of parameters and their standard errors.

Arguments

Y

an \(n \times d\) matrix of observations.

G

the number of components.

weight

a vector of weight parameters (or mixing proportions).

mu

a list of location vectors of G components.

sigma

a list of dispersion matrices of G components.

lambda

a list of skewness vectors of G components.

family

name of the mixing distribution. By default family = "constant" that corresponds to the finite mixture of multivariate normal (or skew normal) distribution. Other candidates for family name are: "bs" (for Birnbaum-Saunders), "burriii" (for Burr type iii), "chisq" (for chi-square), "exp" (for exponential), "f" (for Fisher), "gamma" (for gamma), "gig" (for generalized inverse-Gausssian), "igamma" (for inverse-gamma), "igaussian" (for inverse-Gausssian), "lindley" (for Lindley), "loglog" (for log-logistic), "lognorm" (for log normal), "lomax" (for Lomax), "pstable" (for positive \(\alpha\)-stable), "rayleigh" (for Rayleigh), "ptstable" (for polynomially tilted \(\alpha\)-stable), and "weibull" (for Weibull).

skewness

logical statement. By default skewness = "TRUE" which means that a skewed model is fitted to each component (cluster). If skewness = "FALSE", then a symmetric model is fitted to each component.

param

name of the elements of \(\bold{\theta}\) as the parameter vector of mixing distribution with density function \(f_W(w; \bold{\theta})\).

theta

a list of maximum likelihood estimator for \(\bold{\theta}\) across G components.

ofim1_solve

inverse of the observed Fisher information matrix corresponds to the restricted model.

sigma_arrange1

orders of the lower triangular elements of the dispersion matrix \(\Sigma\).

level

significance level \(\alpha\) for constructing \(100(1-\alpha)\%\) confidence interval. By default \(\alpha = 0.05\).

Author

Mahdi Teimouri

Examples

# \donttest{
      n <- 200
      G <- 2
 weight <- rep( 0.5, 2 )
    mu1 <- rep(-5  , 2 )
    mu2 <- rep( 5  , 2 )
 sigma1 <- matrix( c( 0.4, -0.20, -0.20, 0.5 ), nrow = 2, ncol = 2 )
 sigma2 <- matrix( c( 0.5,  0.20,  0.20, 0.4 ), nrow = 2, ncol = 2 )
lambda1 <- c(-5, -5 )
lambda2 <- c( 5,  5 )
 theta1 <- c( 10, 12 )
 theta2 <- c( 10, 20 )
     mu <- list(mu1, mu2)
  sigma <- list( sigma1 , sigma2 )
 lambda <- list( lambda1, lambda2)
  theta <- list( theta1 , theta2 )
  param <- c("a","b")
    PDF <- quote( (b/2)^(a/2)*x^(-a/2 - 1)/gamma(a/2)*exp( -b/(x*2) ) )
  tick  <- c(1, 1)
      Y <- rmix(n, G, weight, model = "restricted", mu, sigma, lambda, family = "igamma", theta)
  ofim  <- ofim1(Y[, 1:2], G, weight, mu, sigma, lambda, family = "igamma",
  skewness = "TRUE", param, theta, tick, h = 0.001, N = 3000, level = 0.05, PDF)
configuration1(Y[, 1:2], G, weight = weight, mu, sigma, lambda, family = "igamma",
skewness = "TRUE", param, theta, ofim1_solve = ofim$Fisher,
sigma_arrange1 = ofim$index_sigma, level = 0.05)
# }