Nothing
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Copyright (C) 2017 - 2021 Reza Mohammadi |
# |
# This file is part of ssgraph package. |
# |
# "bmixture" is free software: you can redistribute it and/or modify it |
# under the terms of the GNU General Public License as published by the |
# Free Software Foundation: <https://cran.r-project.org/web/licenses/GPL-3>|
# |
# Maintainer: Reza Mohammadi <a.mohammadi@uva.nl> |
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# Main function: BDMCMC algorithm for finite mixture of t-distribution
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# INPUT for bdmcmc funciton
# 1) data: the data with posetive and no missing values
# 2) k number of components of mixture distribution. Defult is unknown
# 2) iter: nuber of iteration of the algorithm
# 3) burnin: number of burn-in iteration
# 4) lambda_r: rate for birth and parameter of prior distribution of k
# 7) k, mu, sig, and pa: initial values for parameters respectively k, mu, sig and pi
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
bmixt = function( data, k = "unknown", iter = 1000, burnin = iter / 2, lambda = 1,
df = 1,
k.start = NULL, mu.start = NULL, sig.start = NULL, pi.start = NULL,
k.max = 30, trace = TRUE )
{
if( any( is.na( data ) ) ) stop( "'data' must contain no missing values" )
if( iter <= burnin ) stop( "'iter' must be higher than 'burnin'" )
burnin = floor( burnin )
n = length( data )
lambda_r = lambda
df_t = df
max_data = max( data )
min_data = min( data )
R = max_data - min_data
# Values for paprameters of prior distributon of mu
epsilon = R / 2 # midpoint of the observed range of the data
kappa_r = 1 / R ^ 2
# Values for paprameters of prior distributon of sigma
alpha = 2
# Values for paprameters of prior distributon of Beta ( Beta is a hyperparameter )
g = 0.2
h = ( 100 * g ) / ( alpha * R ^ 2 )
# initial value
if( k == "unknown" )
{
component_size = "unknown"
if( is.null( k.start ) ) { k = 3 }else{ k = k.start }
}else{
component_size = "fixed"
}
beta_r = stats::rgamma( 1, g, h )
if( is.null( pi.start ) ) pi.start = c( rep( 1 / k, k ) )
if( is.null( mu.start ) ) mu.start = stats::rnorm( k, epsilon, sqrt( 1 / kappa_r ) )
if( is.null( sig.start ) ) sig.start = 1 / stats::rgamma( k, alpha, beta_r )
pi_r = pi.start
mu = mu.start
sig = sig.start
q_t = stats::rgamma( n, df_t / 2, df_t / 2 )
# Sort parameters based on mu
order_pi = order( pi_r )
pi_r = pi_r[ order_pi ]
mu = mu[ order_pi ]
sig = sig[ order_pi ]
############### MCMC
if( component_size == "unknown" )
{
pi_sample = matrix( 0, nrow = iter - burnin, ncol = k.max )
mu_sample = pi_sample
sig_sample = pi_sample
all_k = c( rep( 0, iter ) )
all_weights = all_k
data_r = data
k_max_r = k.max
q_t_r = q_t
df_t_r = df_t
result = .C( "bmix_t_unknown_k", as.double(data_r), as.integer(n), as.integer(k), as.integer(k_max_r), as.integer(iter), as.integer(burnin), as.double(lambda_r),
pi_sample = as.double(pi_sample), mu_sample = as.double(mu_sample), sig_sample = as.double(sig_sample),
all_k = as.integer(all_k), all_weights = as.double(all_weights),
as.double(epsilon), as.double(kappa_r), as.double(alpha), as.double(beta_r), as.double(g), as.double(h),
as.double(mu), as.double(sig), as.double(pi_r),
as.double(q_t_r), as.integer (df_t_r), PACKAGE = "bmixture" )
all_k = result $ all_k
all_weights = result $ all_weights
pi_sample = matrix( result $ pi_sample , nrow = iter - burnin, ncol = k_max_r )
mu_sample = matrix( result $ mu_sample , nrow = iter - burnin, ncol = k_max_r )
sig_sample = matrix( result $ sig_sample, nrow = iter - burnin, ncol = k_max_r )
mcmc_sample = list( all_k = all_k, all_weights = all_weights, pi_sample = pi_sample, mu_sample = mu_sample, sig_sample = sig_sample, data = data_r, df_t = df_t, component_size = "unknown" )
}else{
pi_sample = matrix( 0, nrow = iter - burnin, ncol = k )
mu_sample = pi_sample
sig_sample = pi_sample
data_r = data
q_t_r = q_t
df_t_r = df_t
result = .C( "bmix_t_fixed_k", as.double(data_r), as.integer(n), as.integer(k), as.integer(iter), as.integer(burnin),
pi_sample = as.double(pi_sample), mu_sample = as.double(mu_sample), sig_sample = as.double(sig_sample),
as.double(epsilon), as.double(kappa_r), as.double(alpha), as.double(g), as.double(h),
as.double(mu), as.double(sig), as.double(pi_r),
as.double(q_t_r), as.integer (df_t_r), PACKAGE = "bmixture" )
pi_sample = matrix( result $ pi_sample , nrow = iter - burnin, ncol = k )
mu_sample = matrix( result $ mu_sample , nrow = iter - burnin, ncol = k )
sig_sample = matrix( result $ sig_sample, nrow = iter - burnin, ncol = k )
mcmc_sample = list( pi_sample = pi_sample, mu_sample = mu_sample, sig_sample = sig_sample, data = data, df_t = df_t, component_size = "fixed" )
}
if( trace == TRUE )
{
mes <- paste( c(" ", iter," iteration done. " ), collapse = "" )
cat( mes, "\r" )
cat( "\n" )
utils::flush.console()
}
class( mcmc_sample ) = "bmixt"
return( mcmc_sample )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# summary of bmixt output
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
summary.bmixt = function( object, ... )
{
component_size = object $ component_size
pi_sample = object $ pi_sample
mu_sample = object $ mu_sample
sig_sample = object $ sig_sample
data = object $ data
df_t = object $ df_t
sample_size = nrow( sig_sample )
if( component_size == "unknown" )
{
all_k = object $ all_k
all_weights = object $ all_weights
iter = length( all_k )
burnin = iter - sample_size
k = all_k[( burnin + 1 ):iter]
weights = all_weights[( burnin + 1 ):iter]
op = graphics::par( mfrow = c( 2, 2 ), pty = "s", omi = c( 0.3, 0.3, 0.3, 0.3 ), mai = c( 0.3, 0.3, 0.3, 0.3 ) )
}
graphics::plot( object )
if( component_size == "unknown" )
{
# plot estimated distribution of k
max_k = max( k )
y = vector( mode = "numeric", length = max_k )
for( i in 1:max_k ) y[ i ] <- sum( weights[ k == i ] )
graphics::plot( x = 1:max_k, y, type = "h", main = "", ylab = "Pr(k|data)", xlab = "k(Number of components)" )
# plot k based on iterations
sum_weights = 0 * weights
sum_weights[ 1 ] = weights[ 1 ]
for( i in 2:length( k ) ) sum_weights[ i ] = sum_weights[ i - 1 ] + weights[ i ]
graphics::plot( sum_weights, k, type = "l", xlab = "iteration", ylab = "Number of componants" )
}else{
print( object )
}
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# plot for class bmixt
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
plot.bmixt = function( x, ... )
{
component_size = x $ component_size
pi_sample = x $ pi_sample
mu_sample = x $ mu_sample
sig_sample = x $ sig_sample
data = x $ data
df_t = x $ df_t
sample_size = nrow( sig_sample )
# plot for estimated distribution
graphics::hist( data, prob = T, nclass = 25, col = "gray", border = "white" )
x_seq <- seq( min( data ) * 0.9, max( data ) * 1.2, length = 500 )
f_hat_x_seq <- 0 * x_seq
size_x_seq_r = length( x_seq )
if( component_size == "unknown" )
{
all_k = x $ all_k
iter = length( all_k )
burnin = iter - sample_size
k = all_k[ ( burnin + 1 ) : iter ]
result = .C( "dmixt_hat_x_seq_unknow_k", as.double(x_seq), f_hat_x_seq = as.double(f_hat_x_seq), as.integer(df_t),
as.double(pi_sample), as.double(mu_sample), as.double(sig_sample),
as.integer(k), as.integer(sample_size), as.integer(size_x_seq_r), PACKAGE = "bmixture" )
f_hat_x_seq = result $ f_hat_x_seq
graphics::lines( x_seq, f_hat_x_seq / sample_size, col = "black", lty = 2, lw = 1 )
}else{
size_mix = ncol( sig_sample )
result = .C( "dmixt_hat_x_seq_fixed_k", as.double(x_seq), f_hat_x_seq = as.double(f_hat_x_seq), as.integer(df_t),
as.double(pi_sample), as.double(mu_sample), as.double(sig_sample),
as.integer(size_mix), as.integer(sample_size), as.integer(size_x_seq_r), PACKAGE = "bmixture" )
f_hat_x_seq = result $ f_hat_x_seq
graphics::lines( x_seq, f_hat_x_seq / sample_size, col = "black", lty = 2, lw = 1 )
}
graphics::legend( "topright", c( "predictive density" ), lty = 2, col = "black", lwd = 1 )
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
# print of the bmixt output
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
print.bmixt = function( x, ... )
{
component_size = x $ component_size
pi_sample = x $ pi_sample
mu_sample = x $ mu_sample
sig_sample = x $ sig_sample
df_t = x $ df_t
if( component_size == "unknown" )
{
all_k = x $ all_k
all_weights = x $ all_weights
iter = length( all_k )
sample_size = nrow( pi_sample )
burnin = iter - sample_size
k = all_k[( burnin + 1 ):iter]
weights = all_weights[ ( burnin + 1 ):iter ]
max_k = max( k )
y = vector( mode = "numeric", length = max_k )
for( i in 1:max_k ) y[ i ] <- sum( weights[ k == i ] )
cat( paste( "" ), fill = TRUE )
cat( paste( "Estimation for the number of components = ", which( y == max( y ) ) ), fill = TRUE )
cat( paste( "" ), fill = TRUE )
}else{
cat( paste( "" ), fill = TRUE )
cat( paste( "Number of mixture components = ", ncol( mu_sample ) ), fill = TRUE )
cat( paste( "Estimated 'pi' = "), paste( round( apply( pi_sample , 2, mean ), 2 ) ), fill = TRUE )
cat( paste( "Estimated 'mu' = "), paste( round( apply( mu_sample , 2, mean ), 2 ) ), fill = TRUE )
cat( paste( "Estimated 'sig' = "), paste( round( apply( sig_sample, 2, mean ), 2 ) ), fill = TRUE )
cat( paste( "Estimated 'df' = " ), df_t , fill = TRUE )
cat( paste( "" ), fill = TRUE )
}
}
## - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - |
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.