This vignette explains how to use functions in legion
package, what they produce, what each field in outputs and what returned
values mean.
The package includes the following functions:
legionThere are several methods that can be used together with the
forecasting functions of the package. When a model is saved to some
object ourModel, these function will do some magic. Here’s
the list of all the available methods with brief explanations:
print(ourModel) – function prints brief output with
explanation of what was fitted, with what parameters, errors etc;summary(ourModel) – alias for
print(ourModel);actuals(ourModel) – returns actual values;fitted(ourModel) – fitted values of the model;residuals(ourModel) – residuals of constructed model;
AIC(ourModel), BIC(ourModel),
AICc(ourModel) and BICc(ourModel) –
information criteria of the constructed model. AICc() and
BICc() functions are not standard stats
functions and are imported from greybox package and
modified in legion for the specific models;plot(ourModel) – produces plots for the diagnostics of
the constructed model. There are 9 options of what to produce, see
?plot.legion() for more details. Prepare the canvas via
par(mfcol=...) before using this function otherwise the
plotting might take time.forecast(ourModel) – point and interval forecasts;plot(forecast(ourModel)) – produces graph with actuals,
forecast, fitted and prediction interval using graphmaker()
function from greybox package.simulate(ourModel) – produces data simulated from
provided model. Only works for ves()for now;logLik(ourModel) – returns log-likelihood of the
model;nobs(ourModel) – returns number of observations
in-sample we had;nparam(ourModel) – number of estimated parameters
(originally from greybox package);nvariate(ourModel) – number of variates, time series in
the model (originally from greybox package);sigma(ourModel) – covariance matrix of the residuals of
the model;modelType(ourModel) – returns the type of the model.
Returns something like “MMM” for ETS(MMM). Can be used with
ves() and vets(). In the latter case can also
accept pic=TRUE, returning the PIC restrictions;errorType(ourModel) – the type of the error of a model
(additive or multiplicative);coef(ourModel) – returns the vector of all the
estimated coefficients of the model;