ModelMHMMR-class function

A Reference Class which represents a fitted MHMMR model.

ModelMHMMR represents an estimated MHMMR model. class

Fields

• param: A ParamMHMMR object. It contains the estimated values of the parameters.
• stat: A StatMHMMR object. It contains all the statistics associated to the MHMMR 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 StatMHMMR ).
           * `"filtered" =` Filtered time series and filtering regime probabilities (fields `filtered` and `filter_prob` of class StatMHMMR ).
           * `"smoothed" =` Smoothed time series, and segmentation (fields `smoothed` and `klas` of class StatMHMMR ).
           * `"regressors" =` Polynomial regression components (fields `regressors` and `tau_tk` of class StatMHMMR ).
           * `"loglikelihood" =` Value of the log-likelihood for each iteration (field `stored_loglik` of class StatMHMMR ).
   - **`...`**: Other graphics parameters.
   
   By default, all the above graphs are produced.
  

• summary(digits = getOption("digits")): Summary method.

   - **`digits`**: The number of significant digits to use when printing.
  

Examples

data(multivtoydataset)

mhmmr <- emMHMMR(multivtoydataset$x, multivtoydataset[,c("y1", "y2", "y3")],
                 K = 5, p = 1, verbose = TRUE)

# mhmmr is a ModelMHMMR object. It contains some methods such as 'summary' and 'plot'
mhmmr$summary()
mhmmr$plot()

# mhmmr has also two fields, stat and param which are reference classes as well

# Log-likelihood:
mhmmr$stat$loglik

# Parameters of the polynomial regressions:
mhmmr$param$beta

See Also

ParamMHMMR , StatMHMMR