View source: R/configuration2.R
configuration2 | R Documentation |
Output design for standard error of the standard error for parameters of the finite mixture models.
configuration2(Y, G, weight, model, mu, sigma, lambda, family, skewness, param,
theta, ofim2_solve, sigma_arrange2, level)
Y |
an |
G |
number of components. |
weight |
a vector of weight parameters (or mixing proportions). |
model |
it must be |
mu |
a list of location vectors of |
sigma |
a list of dispersion matrices of |
lambda |
a list of skewness matrices of |
family |
name of the mixing distribution. By default |
skewness |
logical statement. By default |
param |
name of the elements of |
theta |
a list of maximum likelihood estimator for |
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 |
level |
significance level |
designated form for output of parameters and their standard errors.
Mahdi Teimouri
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)
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