trendbreaker-accessors: Accessors for trendbreaker objects
In reconhub/epichange: Detect Changes in Temporal Trends

Description Usage Arguments Details Author(s)

These functions can be used to access information stored in trendbreaker objects. See details.

## S3 method for class 'trendbreaker'
get_model(x, ...)

## S3 method for class 'trendbreaker'
get_formula(x, ...)

## S3 method for class 'trendbreaker'
get_response(x, ...)

## S3 method for class 'trendbreaker'
get_k(x, ...)

## S3 method for class 'trendbreaker'
get_results(x, ...)

## S3 method for class 'trendbreaker'
get_outliers(x, ...)

## S3 method for class 'trendbreaker'
predict(object, newdata, alpha = 0.05, ...)

`x`	an `trendbreaker` object, as returned by asmodee
`...`	further arguments passed to other methods
`object`	an `trendbreaker` object, as returned by asmodee
`newdata`	a `data.frame` containing data for which predictions are to be derived
`alpha`	the alpha threshold to be used for prediction intervals, defaulting to 0.05, i.e. 95% prediction intervals are derived

The following accessors are available:

get_model(): fitted model capturing the temporal trend in the data as an trendbreaker_model_fit object
get_k(): number of recent days excluded from the temporal trend
get_formula(): formula of the retained fitted model
get_response(): response variable of the model
get_results(): main data.frame containing the original data, the predicted values with lower and upper bounds for the prediction interval, a logical variable outlier which is TRUE for data points falling outside the prediction interval, and classification of outliers as a factor.
get_outliers(): returns data points (as rows of get_results(x) corresponding to outliers.
predict(): function to make model predictions from the fitted model in the trendbreaker object; accepts two arguments newdata, a mandatory input containing data for which predictions are derived, and alpha, the threshold used for prediction intervals, defaulting to 0.05.