ModelHMMR-class: A Reference Class which represents a fitted HMMR model.

In fchamroukhi/HMMR_r: Hidden Markov Model Regression ('HMMR') for Time Series Segmentation

Description

ModelHMMR represents an estimated HMMR model.

Fields

param

An object of class ParamHMMR. It contains the estimated values of the parameters.

stat

An object of class StatHMMR. It contains all the statistics associated to the HMMR model.

Methods

plot(what = c("predicted", "filtered", "smoothed", "regressors", "loglikelihood"), ...)

Plot method.

what

The type of graph requested:

• "predicted" = Predicted time series and predicted regime probabilities (fields predicted and predict_prob of class StatHMMR).

• "filtered" = Filtered time series and filtering regime probabilities (fields filtered and filter_prob of class StatHMMR).

• "smoothed" = Smoothed time series, and segmentation (fields smoothed and klas of the class StatHMMR).

• "regressors" = Polynomial regression components (fields regressors and tau_tk of class StatHMMR).

• "loglikelihood" = Value of the log-likelihood for each iteration (field stored_loglik of class StatHMMR).

...

Other graphics parameters.

By default, all the graphs mentioned above are produced.

summary(digits = getOption("digits"))

Summary method.

digits

The number of significant digits to use when printing.

See Also

ParamHMMR, StatHMMR

Examples

data(toydataset)
x <- toydataset$x
y <- toydataset$y

hmmr <- emHMMR(X = x, Y = 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

fchamroukhi/HMMR_r documentation built on Aug. 8, 2019, 2:38 p.m.