## Copyright (C) 2013 Lars Simon Zehnder
#
# This file is part of finmix.
#
# finmix 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, either version 3 of the License, or
# (at your option) any later version.
#
# finmix is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with finmix. If not, see <http://www.gnu.org/licenses/>.
#' Finmix `mcmcoutputpermfixhier` class
#'
#' @description
#' This class defines objects to store the outputs from permuting the MCMC
#' samples. Due to label switching the sampled component parameters are usually
#' not assigned to the same component in each iteration. To overcome this issue
#' the samples are permuted by using a relabeling algorithm (usually K-means)
#' to reassign parameters. Note that due to assignment of parameters from the
#' same iteration to the same component, the sample size could shrink.
#'
#' This class stores the permuted parameters together with the new MCMC sample
#' size and the mixture log-likelihood, the prior log-likelihood, and the
#' complete data posterior log-likelihood.
#'
#' Note this class inherits all slots from its parent classes.
#'
#' @exportClass mcmcoutputpermfixhier
#' @rdname mcmcoutputpermfixhier-class
#' @seealso
#' * [mcmcoutputpermfix-class] for the parent class
#' * [mcmcpermfix-class] for the parent class
#' * [mcmcpermute()] for performing permutation of MCMC samples
.mcmcoutputpermfixhier <- setClass("mcmcoutputpermfixhier",
contains = c("mcmcpermfixhier", "mcmcoutputfixhier"),
validity = function(object) {
## else: OK
TRUE
}
)
#' Initializer of the `mcmcoutputpermfixhier` class
#'
#' @description
#' Only used implicitly. The initializer stores the data into the slots of the
#' passed-in object.
#'
#' @param .Object An object: see the "initialize Methods" section in
#' [initialize].
#' @param mcmcoutput An `mcmcoutput` class containing the results from MCMC
#' sampling.
#' @param Mperm An integer defining the number of permuted MCMC samples.
#' @param parperm A named list containing the permuted component parameter
#' samples from MCMC sampling
#' @param logperm A named list containing the mixture log-likelihood, the
#' prior log-likelihood, and the complete data posterior log-likelihood
#' for the permuted MCMC samples.
#' @param hyperperm A named list containing the permuted parameters of the
#' hierarchical prior.
#'
#' @keywords internal
#'
#' @seealso
#' * [Classes_Details] for details of class definitions, and
#' * [setOldClass] for the relation to S3 classes
setMethod(
"initialize", "mcmcoutputpermfixhier",
function(.Object, mcmcoutput, Mperm = integer(),
parperm = list(), logperm = list(), hyperperm = list()) {
.Object@M <- mcmcoutput@M
.Object@burnin <- mcmcoutput@burnin
.Object@ranperm <- mcmcoutput@ranperm
.Object@par <- mcmcoutput@par
.Object@log <- mcmcoutput@log
.Object@hyper <- mcmcoutput@hyper
.Object@model <- mcmcoutput@model
.Object@prior <- mcmcoutput@prior
.Object@Mperm <- Mperm
.Object@parperm <- parperm
.Object@logperm <- logperm
.Object@hyperperm <- hyperperm
.Object
}
)
#' Shows a summary of an `mcmcoutputpermfixhier` object.
#'
#' @description
#' Calling [show()] on an `mcmcoutputpermfixhier` object gives an overview
#' of the `mcmcoutputpermfixhier` object.
#'
#' @param object An `mcmcoutputpermfixhier` object.
#' @returns A console output listing the slots and summary information about
#' each of them.
#' @exportMethod show
#' @keywords internal
setMethod(
"show", "mcmcoutputpermfixhier",
function(object) {
cat("Object 'mcmcoutputperm'\n")
cat(" class :", class(object), "\n")
cat(" M :", object@M, "\n")
cat(" burnin :", object@burnin, "\n")
cat(" ranperm :", object@ranperm, "\n")
cat(
" par : List of",
length(object@par), "\n"
)
cat(
" log : List of",
length(object@log), "\n"
)
cat(
" hyper : List of",
length(object@hyper), "\n"
)
cat(" Mperm :", object@Mperm, "\n")
cat(
" parperm : List of",
length(object@parperm), "\n"
)
cat(
" logperm : List of",
length(object@logperm), "\n"
)
cat(
" hyperperm : List of",
length(object@hyperperm), "\n"
)
cat(
" model : Object of class",
class(object@model), "\n"
)
cat(
" prior : Object of class",
class(object@prior), "\n"
)
}
)
#' Plot traces of MCMC sampling
#'
#' @description
#' Calling [plotTraces()] plots the MCMC traces of the mixture log-likelihood
#' , the mixture log-likelihood of the prior distribution, the log-likelihood
#' of the complete data posterior, or the weights and parameters if `lik` is
#' set to `1`.s
#'
#' If `lik` is set to `0` the parameters of the components and the posterior
#' parameters are plotted together with `K-1` weights.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param lik An integer indicating, if the log-likelihood traces should be
#' plotted (default). If set to `0` the traces for the parameters
#' and weights are plotted instead.
#' @param col A logical indicating, if the plot should be colored.
#' @param ... Further arguments to be passed to the plotting function.
#' @return A plot of the traces of the MCMC samples.
#' @exportMethod plotTraces
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotTraces(f_outputperm, lik = 0)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotHist()] for plotting histograms of sampled values
#' * [plotDens()] for plotting densities of sampled values
#' * [plotSampRep()] for plotting sampling representations of sampled values
#' * [plotPointProc()] for plotting point processes for sampled values
#' * [plotPostDens()] for plotting the posterior density of component parameters
setMethod(
"plotTraces", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY",
lik = "ANY",
col = "ANY"
),
function(x, dev = TRUE, lik = 1, col = FALSE, ...) {
dist <- x@model@dist
if (lik %in% c(0, 1)) {
if (dist == "poisson") {
.permtraces.Poisson.Hier(x, dev)
} else if (dist == "binomial") {
.permtraces.Binomial(x, dev)
} else if (dist == "exponential") {
callNextMethod(x, dev, lik, col, ...)
} else if (dist == "normal") {
.permtraces.Normal.Hier(x, dev)
} else if (dist == "student") {
.permtraces.Student.Hier(x, dev)
} else if (dist == "normult") {
.permtraces.Normult.Hier(x, dev, col)
} else if (dist == "studmult") {
.permtraces.Studmult.Hier(x, dev, col)
}
}
if (lik %in% c(1, 2)) {
## log ##
.permtraces.Log(x, dev, col)
}
}
)
#' Plot histograms of the parameters and weights
#'
#' @description
#' Calling [plotHist()] plots histograms of the sampled parameters and weights
#' from MCMC sampling.More specifically, all component parameters, `K-1` of the
#' weights and the posterior parameters are considered in the histogram plots.
#'
#' Note, this method is so far only implemented for Poisson and Binomial
#' mixture distributions.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param ... Further arguments to be passed to the plotting function.
#' @return Histograms of the MCMC samples.
#' @exportMethod plotHist
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotHist(f_outputperm)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotTraces()] for plotting the traces of sampled values
#' * [plotDens()] for plotting densities of sampled values
#' * [plotSampRep()] for plotting sampling representations of sampled values
#' * [plotPointProc()] for plotting point processes for sampled values
#' * [plotPostDens()] for plotting the posterior density of component parameters
setMethod(
"plotHist", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY"
),
function(x, dev = TRUE, ...) {
dist <- x@model@dist
if (dist == "poisson") {
.permhist.Poisson.Hier(x, dev)
} else if (dist == "binomial") {
.permhist.Binomial(x, dev)
}
}
)
#' Plot densities of the parameters and weights
#'
#' @description
#' Calling [plotDens()] plots densities of the sampled parameters and weights
#' from MCMC sampling.More specifically, all component parameters, `K-1` of the
#' weights and the posterior parameters are considered in the density plots.
#'
#' Note, this method is so far only implemented for mixtures of Poisson or
#' Binomial distributions.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param ... Further arguments to be passed to the plotting function.
#' @return Densities of the MCMC samples.
#' @exportMethod plotDens
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotDens(f_outputperm)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotTraces()] for plotting the traces of sampled values
#' * [plotHist()] for plotting histograms of sampled values
#' * [plotSampRep()] for plotting sampling representations of sampled values
#' * [plotPointProc()] for plotting point processes for sampled values
#' * [plotPostDens()] for plotting the posterior density of component parameters
setMethod(
"plotDens", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY"
),
function(x, dev = TRUE, ...) {
dist <- x@model@dist
if (dist == "poisson") {
.permdens.Poisson.Hier(x, dev)
} else if (dist == "binomial") {
.permdens.Binomial(x, dev)
}
}
)
#' Plot point processes of the component parameters
#'
#' @description
#' Calling [plotPointProc()] plots point processes of the sampled component
#' parameters from MCMC sampling.
#'
#' Note, this method is so far only implemented for mixture models of Poisson
#' or Binomial distributons.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param ... Further arguments to be passed to the plotting function.
#' @return Point process of the MCMC samples.
#' @exportMethod plotPointProc
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotPointProc(f_outputperm)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotTraces()] for plotting the traces of sampled values
#' * [plotHist()] for plotting histograms of sampled values
#' * [plotDens()] for plotting densities of sampled values
#' * [plotSampRep()] for plotting sampling representations of sampled values
#' * [plotPostDens()] for plotting posterior densities for sampled values
setMethod(
"plotPointProc", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY"
),
function(x, dev = TRUE, ...) {
dist <- x@model@dist
if (dist == "poisson") {
.permpointproc.Poisson(x, dev)
} else if (dist == "binomial") {
.permpointproc.Binomial(x, dev)
}
}
)
#' Plot sampling representations for the component parameters
#'
#' @description
#' Calling [plotSampRep()] plots sampling representations of the sampled
#' component parameters from MCMC sampling.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param ... Further arguments to be passed to the plotting function.
#' @return Sampling representation of the MCMC samples.
#' @exportMethod plotSampRep
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotSampRep(f_outputperm)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotTraces()] for plotting the traces of sampled values
#' * [plotHist()] for plotting histograms of sampled values
#' * [plotDens()] for plotting densities of sampled values
#' * [plotPointProc()] for plotting point processes of sampled values
#' * [plotPostDens()] for plotting posterior densities for sampled values
setMethod(
"plotSampRep", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY"
),
function(x, dev, ...) {
dist <- x@model@dist
if (dist == "poisson") {
.permsamprep.Poisson(x, dev)
} else if (dist == "binomial") {
.permsamprep.Binomial(x, dev)
}
}
)
#' Plot posterior densities of the component parameters
#'
#' @description
#' Calling [plotPostDens()] plots posterior densities of the sampled component
#' parameters from MCMC sampling, if the number of components is two.
#'
#' Note, this method is so far only implemented for Poisson and Binomial
#' mixture distributions.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown by a graphical
#' device. If plots should be stored to a file set `dev` to `FALSE`.
#' @param ... Further arguments to be passed to the plotting function.
#' @return Posterior densities of the MCMC samples.
#' @exportMethod plotPostDens
#' @keywords internal
#'
#' @examples
#' \dontrun{
#' # Define a Poisson mixture model with two components.
#' f_model <- model("poisson", par = list(lambda = c(0.3, 1.2)), K = 2,
#' indicfix = TRUE)
#' # Simulate data from the mixture model.
#' f_data <- simulate(f_model)
#' # Define the hyper-parameters for MCMC sampling.
#' f_mcmc <- mcmc(storepost = FALSE)
#' # Define the prior distribution by relying on the data.
#' f_prior <- priordefine(f_data, f_model)
#' # Start MCMC sampling.
#' f_output <- mixturemcmc(f_data, f_model, f_prior, f_mcmc)
#' f_outputperm <- mcmcpermute(f_output)
#' plotPostDens(f_outputperm)
#' }
#'
#' @seealso
#' * [mixturemcmc()] for performing MCMC sampling
#' * [mcmcpermute()] for permuting MCMC samples
#' * [plotTraces()] for plotting the traces of sampled values
#' * [plotHist()] for plotting histograms of sampled values
#' * [plotDens()] for plotting densities of sampled values
#' * [plotSampRep()] for plotting sampling representations of sampled values
#' * [plotPointProc()] for plotting point processes for sampled values
setMethod(
"plotPostDens", signature(
x = "mcmcoutputpermfixhier",
dev = "ANY"
),
function(x, dev = TRUE, ...) {
dist <- x@model@dist
if (dist == "poisson") {
.permpostdens.Poisson(x, dev)
} else if (dist == "binomial") {
.permpostdens.Binomial(x, dev)
}
}
)
### Private functions.
### These functions are not exported.
### Traces
#' Plots traces of Poisson mixture samples
#'
#' @description
#' For internal usage only. This function plots the traces for sampled values
#' from a Poisson mixture model, if an hierarchical prior has been used in
#' sampling. The hyperparameter `b` of the hierarchical Gamma distribution is
#' plotted next to the component parameter traces.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all samples.
#' @param dev A logical indicating if the plot should be shown by a graphical
#' device.
#' @return A plot of the traces of sampled values.
#' @noRd
#'
#' @seealso
#' * [plotTraces()] for the calling function
".permtraces.Poisson.Hier" <- function(x, dev) {
K <- x@model@K
trace.n <- K + 1
if (.check.grDevice() && dev) {
dev.new(title = "Traceplots")
}
par(
mfrow = c(trace.n, 1), mar = c(1, 0, 0, 0),
oma = c(4, 5, 4, 4)
)
lambda <- x@parperm$lambda
for (k in 1:K) {
plot(lambda[, k],
type = "l", axes = F,
col = "gray20", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = 0.7)
mtext(
side = 2, las = 2, bquote(lambda[k = .(k)]),
cex = 0.6, line = 3
)
}
b <- x@hyperperm$b
plot(b,
type = "l", axes = F,
col = "gray68", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = 0.7)
mtext(side = 2, las = 2, "b", cex = 0.6, line = 3)
axis(1)
mtext(side = 1, "Iterations", cex = 0.7, line = 3)
}
#' Plots traces of Normal mixture samples
#'
#' @description
#' For internal usage only. This function plots the traces for sampled values
#' from a Normal mixture model. The parameters of the hierarchical prior are
#' plotted together with the component parameters.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all samples.
#' @param dev A logical indicating if the plot should be shown by a grapical
#' device.
#' @return A plot of the traces of sampled values.
#' @noRd
#'
#' @seealso
#' * [plotTraces()] for the calling function
".permtraces.Normal.Hier" <- function(x, dev) {
K <- x@model@K
trace.n <- 2 * K + 1
if (.check.grDevice() && dev) {
dev.new(title = "Traceplots")
}
par(
mfrow = c(trace.n, 1), mar = c(1, 0, 0, 0),
oma = c(4, 5, 4, 4)
)
mu <- x@parperm$mu
sigma <- x@parperm$sigma
for (k in 1:K) {
plot(mu[, k],
type = "l", axes = F,
col = "gray20", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(mu[k = .(k)]),
cex = .6, line = 3
)
}
for (k in 1:K) {
plot(sigma[, k],
type = "l", axes = F,
col = "gray30", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(sigma[k = .(k)]),
cex = .6, line = 3
)
}
C <- x@hyperperm$C
plot(c,
type = "l", axes = F,
col = "gray68", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = 0.7)
mtext(side = 2, las = 2, "C", cex = .6, line = 3)
axis(1)
mtext(side = 1, "Iterations", cex = .7, line = 3)
}
#' Plots traces of Student-t mixture samples
#'
#' @description
#' For internal usage only. This function plots the traces for sampled values
#' from a Student-t mixture model. The parameters of the hierarchical prior
#' are plotted together with the component parameters.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all samples.
#' @param dev A logical indicating if the plot should be shown by a graphical
#' device.
#' @return A plot of the traces of sampled values.
#' @noRd
#'
#' @seealso
#' * [plotTraces()] for the calling function
".permtraces.Student.Hier" <- function(x, dev) {
K <- x@model@K
trace.n <- 3 * K + 1
if (.check.grDevice() && dev) {
dev.new(title = "Traceplots")
}
par(
mfrow = c(trace.n, 1), mar = c(1, 0, 0, 0),
oma = c(4, 5, 4, 4)
)
mu <- x@parperm$mu
sigma <- x@parperm$sigma
df <- x@parperm$df
for (k in 1:K) {
plot(mu[, k],
type = "l", axes = F,
col = "gray20", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(mu[k = .(k)]),
cex = .6, line = 3
)
}
for (k in 1:K) {
plot(sigma[, k],
type = "l", axes = F,
col = "gray30", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(sigma[k = .(k)]),
cex = .6, line = 3
)
}
for (k in 1:K) {
plot(df[, k],
type = "l", axes = F,
col = "gray40", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(nu[k = .(k)]),
cex = .6, line = 3
)
}
C <- x@hyperperm$C
plot(C,
type = "l", axes = F,
col = "gray68", xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, "C", cex = .6,
line = 3
)
axis(1)
mtext(side = 1, "Iterations", cex = .7, line = 3)
}
#' Plots traces of multivariate normal mixture samples
#'
#' @description
#' For internal usage only. This function plots the traces for sampled values
#' from a multivariate normal mixture model. The parameters of the hierarchical
#' prior are plotted alongside the component parameters.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all samples.
#' @param dev A logical indicating if the plot should be shown by a grapical
#' device.
#' @return A plot of the traces of sampled values.
#' @noRd
#'
#' @seealso
#' * [plotTraces()] for the calling function
".permtraces.Normult.Hier" <- function(x, dev, col) {
.permtraces.Normult(x, dev, col)
r <- x@model@r
K <- x@model@K
C <- x@hyperperm$C
C.trace <- sapply(
seq(1, x@M),
function(i) sum(diag(qinmatr(C[i, ])))
)
C.logdet <- sapply(
seq(1, x@M),
function(i) log(det(qinmatr(C[i, ])))
)
# C traces
mmax <- max(C.trace)
mmin <- min(C.trace)
if (.check.grDevice() && dev) {
dev.new(title = "Traceplots Hyperparameters")
}
par(
mfrow = c(2, 1), mar = c(1, 2, 0, 0),
oma = c(4, 5, 4, 4)
)
if (col) {
cscale <- rainbow(K, start = 0.5, end = 0)
} else {
cscale <- gray.colors(K, start = 0.5, end = 0.15)
}
plot(C.trace,
type = "l", axes = F,
col = cscale[K], xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(tr(C)),
cex = .6, line = 3
)
plot(C.logdet,
type = "l", axes = F,
col = cscale[K], xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
name <- vector("character", K)
mtext(
side = 2, las = 2, bquote(log(det(C))),
cex = .6, line = 3
)
axis(1)
mtext(side = 1, "Iterations", cex = .7, line = 3)
}
#' Plots traces of multivariate Student-t mixture samples
#'
#' @description
#' For internal usage only. This function plots the traces for sampled values
#' from a multivariate Student-t mixture model. The parameters of the
#' hierarchical prior are plotted alongside the component parameters.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all samples.
#' @param dev A logical indicating if the plot should be shown by a graphical
#' device.
#' @return A plot of the traces of sampled values.
#' @noRd
#'
#' @seealso
#' * [plotTraces()] for the calling function
".permtraces.Studmult.Hier" <- function(x, dev, col) {
.permtraces.Studmult(x, dev, col)
r <- x@model@r
K <- x@model@K
C <- x@hyperperm$C
C.trace <- sapply(
seq(1, x@M),
function(i) sum(diag(qinmatr(C[i, ])))
)
C.logdet <- sapply(
seq(1, x@M),
function(i) log(det(qinmatr(C[i, ])))
)
# C traces
mmax <- max(C.trace)
mmin <- min(C.trace)
if (.check.grDevice() && dev) {
dev.new(title = "Traceplots Hyperparameters")
}
par(
mfrow = c(2, 1), mar = c(1, 2, 0, 0),
oma = c(4, 5, 4, 4)
)
if (col) {
cscale <- rainbow(K, start = 0.5, end = 0)
} else {
cscale <- gray.colors(K, start = 0.5, end = 0.15)
}
plot(C.trace,
type = "l", axes = F,
col = cscale[K], xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(tr(C)),
cex = .6, line = 3
)
plot(C.logdet,
type = "l", axes = F,
col = cscale[K], xlab = "", ylab = ""
)
axis(2, las = 2, cex.axis = .7)
mtext(
side = 2, las = 2, bquote(log(det(C))),
cex = .6, line = 3
)
axis(1)
mtext(side = 1, "Iterations", cex = .7, line = 3)
}
### Histograms
### Histograms Poisson: Plots histograms for all Poisson
### parameters and the hyper-parameter 'b'.
#' Plot histograms of Poisson samples
#'
#' @description
#' For internal usage only. This function plots histograms of sampled Poisson
#' parameters and weights. In addition the parameters of the hierarchical prior
#' `b` are plotted.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown on a graphical
#' device.
#' @return A plot with histograms for the sampled parameters and weights.
#' @noRd
#'
#' @seealso
#' * [plotHist()] for the calling function
".permhist.Poisson.Hier" <- function(x, dev) {
K <- x@model@K
if (.check.grDevice() && dev) {
dev.new(title = "Histograms (permuted)")
}
lambda <- x@parperm$lambda
b <- x@hyperperm$b
vars <- cbind(lambda, b)
lab.names <- vector("list", K + 1)
for (k in 1:K) {
lab.names[[k]] <- bquote(lambda[.(k)])
}
lab.names[[K + 1]] <- "b"
.symmetric.Hist(vars, lab.names)
}
### Densities
#' Plot densities of Poisson samples
#'
#' @description
#' For internal usage only. This function plots densities of sampled Poisson
#' parameters and weights. In addition the parameters of the hierarchical prior
#' are plotted.
#'
#' @param x An `mcmcoutputpermfixhier` object containing all sampled values.
#' @param dev A logical indicating, if the plots should be shown on a graphical
#' device.
#' @return A plot with densities for the sampled parameters and weights.
#' @noRd
#'
#' @seealso
#' * [plotDens()] for the calling function
".permdens.Poisson.Hier" <- function(x, dev) {
K <- x@model@K
if (.check.grDevice() && dev) {
dev.new(title = "Histograms (permuted)")
}
lambda <- x@parperm$lambda
b <- x@hyperperm$b
vars <- cbind(lambda, b)
lab.names <- vector("list", K + 1)
for (k in 1:K) {
lab.names[[k]] <- bquote(lambda[.(k)])
}
lab.names[[K + 1]] <- "b"
.symmetric.Dens(vars, lab.names)
}
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