bfast01
tries to select a suitable model for the data by choosing
either a model with 0 or with 1 breakpoint. It proceeds in the following
steps:
1. The data is preprocessed with bfastpp using the arguments
order/lag/slag/na.action/stl.
2. A linear model with the given formula is fitted. By default a suitable
formula is guessed based on the preprocessing parameters.
3. The model with 1 breakpoint is estimated as well where the breakpoint
is chosen to minimize the segmented residual sum of squares.
4. A sequence of tests the null hypothesis of zero breaks is performed.
Each test results in a decision for FALSE (no breaks) or TRUE (structural
break(s)). The test decisions are then aggregated to a single decision
(by default using all() but any() or some other function could also be used).
Available methods for the object returned include standard methods for
linear models (coef, fitted, residuals, predict, AIC, BIC, logLik, deviance,
nobs, model.matrix, model.frame), standard methods for breakpoints (breakpoints,
breakdates), coercion to a zoo series with the decomposed components (as.zoo),
and a plot method which plots such a zoo series along with the confidence
interval (if the 1-break model is visualized). All methods take a 'breaks'
argument which can either be 0 or 1. By default the value chosen based on the
'test' decisions is used.
Note that the different tests supported have power for different types of
alternatives. Some tests (such as supLM/supF or BIC) assess changes in all
coefficients of the model while residual-based tests (e.g., OLS-CUSUM or
OLS-MOSUM) assess changes in the conditional mean. See Zeileis (2005) for
a unifying view.