mixbox (version 1.2.3)

configuration2: Output configuration.

Description

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

Usage

configuration2(Y, G, weight, model, mu, sigma, lambda, family, skewness, param,
theta, ofim2_solve, sigma_arrange2, level)

Value

designated form for output of parameters and their standard errors.

Arguments

Y

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

G

number of components.

weight

a vector of weight parameters (or mixing proportions).

model

it must be "canonical" or "unrestricted".

mu

a list of location vectors of G components.

sigma

a list of dispersion matrices of G components.

lambda

a list of skewness matrices of G components. If the mixture model is "canonical", then lambda is a \(p \times q\) matrix. If the mixture model is "unrestricted", then lambda is a \(p \times p\) diagonal matrix.

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.

ofim2_solve

inverse of the observed Fisher information matrix corresponds to the canonical or unrestricted model.

sigma_arrange2

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 <- 100
      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 <- diag( c(-5, -5) )
lambda2 <- diag( c( 5,  5) )
 theta1 <- c( 10, 12 )
 theta2 <- c( 5, 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 = "unrestricted", mu, sigma, lambda, family = "igamma", theta)
  ofim  <- ofim2(Y[, 1:2], G, weight, model = "unrestricted", mu, sigma, lambda,
  family = "igamma", skewness = "TRUE", param, theta, tick, h = 0.01, N = 3000, level = 0.05, PDF)
configuration2(Y[, 1:2], G, weight = weight, model = "unrestricted", mu, sigma, lambda,
family = "igamma", skewness = "TRUE", param, theta, ofim2_solve = ofim$Fisher,
sigma_arrange2 = ofim$index_sigma, level = 0.05)
# }