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#' A Reference Class which represents a fitted HMMR model.
#'
#' ModelHMMR represents an estimated HMMR model.
#'
#' @field param An object of class [ParamHMMR][ParamHMMR]. It contains the
#' estimated values of the parameters.
#' @field stat An object of class [StatHMMR][StatHMMR]. It contains all the
#' statistics associated to the HMMR model.
#' @seealso [ParamHMMR], [StatHMMR]
#' @export
#'
#' @examples
#' data(univtoydataset)
#'
#' hmmr <- emHMMR(univtoydataset$x, univtoydataset$y, K = 5, p = 1, verbose = TRUE)
#'
#' # hmmr is a ModelHMMR object. It contains some methods such as 'summary' and 'plot'
#' hmmr$summary()
#' hmmr$plot()
#'
#' # hmmr has also two fields, stat and param which are reference classes as well
#'
#' # Log-likelihood:
#' hmmr$stat$loglik
#'
#' # Parameters of the polynomial regressions:
#' hmmr$param$beta
ModelHMMR <- setRefClass(
"ModelHMMR",
fields = list(
param = "ParamHMMR",
stat = "StatHMMR"
),
methods = list(
plot = function(what = c("predicted", "filtered", "smoothed", "regressors", "loglikelihood"), ...) {
"Plot method.
\\describe{
\\item{\\code{what}}{The type of graph requested:
\\itemize{
\\item \\code{\"predicted\" = } Predicted time series and predicted
regime probabilities (fields \\code{predicted} and
\\code{predict_prob} of class \\link{StatHMMR}).
\\item \\code{\"filtered\" = } Filtered time series and filtering
regime probabilities (fields \\code{filtered} and
\\code{filter_prob} of class \\link{StatHMMR}).
\\item \\code{\"smoothed\" = } Smoothed time series, and
segmentation (fields \\code{smoothed} and \\code{klas} of the
class {StatHMMR}).
\\item \\code{\"regressors\" = } Polynomial regression components
(fields \\code{regressors} and \\code{tau_tk} of class
\\link{StatHMMR}).
\\item \\code{\"loglikelihood\" = } Value of the log-likelihood for
each iteration (field \\code{stored_loglik} of class
\\link{StatHMMR}).
}
}
\\item{\\code{\\dots}}{Other graphics parameters.}
}
By default, all the graphs mentioned above are produced."
what <- match.arg(what, several.ok = TRUE)
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
yaxislim <- c(mean(param$Y) - 2 * sd(param$Y), mean(param$Y) + 2 * sd(param$Y))
colorsvec <- rainbow(param$K)
if (any(what == "predicted")) {
# Predicted time series and predicted regime probabilities
par(mfrow = c(2, 1), mai = c(0.6, 1, 0.5, 0.5), mgp = c(2, 1, 0))
plot.default(param$X, param$Y, type = "l", ylim = yaxislim, xlab = "x", ylab = "y", ...)
lines(param$X, stat$predicted, type = "l", col = "red", lwd = 1.5, ...)
title(main = "Original and predicted HMMR time series")
# Prediction probabilities of the hidden process
plot.default(param$X, stat$predict_prob[, 1], type = "l", xlab = "x", ylab = expression('P(Z'[t] == k ~ '|' ~ list(y[1],..., y[t - 1]) ~ ')'), col = colorsvec[1], lwd = 1.5, main = "Prediction probabilities", ylim = c(0, 1), ...)
if (param$K > 1) {
for (k in 2:param$K) {
lines(param$X, stat$predict_prob[, k], col = colorsvec[k], lwd = 1.5, ...) # Pred Probs: Pr(Z_{t}=k|y_1,\ldots,y_{t-1})
}
}
}
if (any(what == "filtered")) {
# Filtered time series and filtering regime probabilities
par(mfrow = c(2, 1), mai = c(0.6, 1, 0.5, 0.5), mgp = c(2, 1, 0))
plot.default(param$X, param$Y, type = "l", ylim = yaxislim, xlab = "x", ylab = "y", ...)
title(main = "Original and filtered HMMR time series")
lines(param$X, stat$filtered, col = "red", lwd = 1.5, ...)
# Filtering probabilities of the hidden process
plot.default(param$X, stat$filter_prob[, 1], type = "l", xlab = "x", ylab = expression('P(Z'[t] == k ~ '|' ~ list(y[1],..., y[t]) ~ ')'), col = colorsvec[1], lwd = 1.5, ylim = c(0, 1), ...)
title(main = "Filtering probabilities")
if (param$K > 1) {
for (k in 2:param$K) {
lines(param$X, stat$filter_prob[, k], col = colorsvec[k], lwd = 1.5, ...) # Filter Probs: Pr(Z_{t}=k|y_1,\ldots,y_t)
}
}
}
if (any(what == "regressors")) {
# Data, regressors, and segmentation
par(mfrow = c(2, 1), mai = c(0.6, 1, 0.5, 0.5), mgp = c(2, 1, 0))
plot.default(param$X, param$Y, type = "l", ylim = yaxislim, xlab = "x", ylab = "y", ...)
title(main = "Time series, HMMR regimes, and smoothing probabilites")
for (k in 1:param$K) {
model_k <- stat$regressors[, k]
#prob_model_k = HMMR$param$piik[,k]
index <- stat$klas == k
active_model_k <- model_k[index] # prob_model_k >= prob);
active_period_model_k <- param$X[index] # prob_model_k >= prob);
if (length(active_model_k) != 0) {
lines(param$X, model_k, col = colorsvec[k], lty = "dotted", lwd = 1.5, ...)
lines(active_period_model_k, active_model_k, col = colorsvec[k], lwd = 1.5, ...)
}
}
# Smoothing probabilities of the hidden process (segmentation)
plot.default(param$X, stat$tau_tk[, 1], type = "l", xlab = "x", ylab = expression('P(Z'[t] == k ~ '|' ~ list(y[1],..., y[n]) ~ ')'), col = colorsvec[1], lwd = 1.5, ylim = c(0, 1), ...)
title(main = "Smoothing probabilities")
if (param$K > 1) {
for (k in 2:param$K) {
lines(param$X, stat$tau_tk[, k], col = colorsvec[k], lwd = 1.5, ...) # Post Probs: Pr(Z_{t}=k|y_1,\ldots,y_n)
}
}
}
if (any(what == "smoothed")) {
# Data, regression model, and segmentation
par(mfrow = c(2, 1), mai = c(0.6, 1, 0.5, 0.5), mgp = c(2, 1, 0))
plot.default(param$X, param$Y, type = "l", ylim = yaxislim, xlab = "x", ylab = "y", ...)
title(main = "Original and smoothed HMMR time series, and segmentation")
lines(param$X, stat$smoothed, col = "red", lwd = 1.5, ...)
# Transition time points
tk <- which(diff(stat$klas) != 0)
for (i in 1:length(tk)) {
abline(v = param$X[tk[i]], col = "red", lty = "dotted", lwd = 2, ...)
}
# Probablities of the hidden process (segmentation)
plot.default(param$X, stat$klas, type = "l", xlab = "x", ylab = "Estimated class labels", col = "red", lwd = 1.5, yaxt = "n", ...)
axis(side = 2, at = 1:param$K, ...)
}
if (any(what == "loglikelihood")) {
par(mfrow = c(1, 1))
plot.default(1:length(stat$stored_loglik), stat$stored_loglik, type = "l", col = "blue", xlab = "EM iteration number", ylab = "Log-likelihood", ...)
title(main = "Log-likelihood")
}
},
summary = function(digits = getOption("digits")) {
"Summary method.
\\describe{
\\item{\\code{digits}}{The number of significant digits to use when
printing.}
}"
title <- paste("Fitted HMMR model")
txt <- paste(rep("-", min(nchar(title) + 4, getOption("width"))), collapse = "")
# Title
cat(txt)
cat("\n")
cat(title)
cat("\n")
cat(txt)
cat("\n")
cat("\n")
cat(paste0("HMMR model with K = ", param$K, ifelse(param$K > 1, " components", " component"), ":"))
cat("\n")
cat("\n")
tab <- data.frame("log-likelihood" = stat$loglik, "nu" = param$nu, "AIC" = stat$AIC,
"BIC" = stat$BIC, row.names = "", check.names = FALSE)
print(tab, digits = digits)
cat("\nClustering table (Number of observations in each regimes):\n")
print(table(stat$klas))
cat("\nRegression coefficients:\n\n")
if (param$p > 0) {
row.names = c("1", sapply(1:param$p, function(x) paste0("X^", x)))
} else {
row.names = "1"
}
betas <- data.frame(param$beta, row.names = row.names)
colnames(betas) <- sapply(1:param$K, function(x) paste0("Beta(K = ", x, ")"))
print(betas, digits = digits)
cat(paste0(ifelse(param$variance_type == "homoskedastic", "\n\n",
"\nVariances:\n\n")))
sigma2 = data.frame(t(param$sigma2), row.names = NULL)
if (param$variance_type == "homoskedastic") {
colnames(sigma2) = "Sigma2"
print(sigma2, digits = digits, row.names = FALSE)
} else {
colnames(sigma2) = sapply(1:param$K, function(x) paste0("Sigma2(K = ", x, ")"))
print(sigma2, digits = digits, row.names = FALSE)
}
}
)
)
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