The main fitting function `brokenstick()`

returns an object of class
`brokenstick`

. This object collects the fitted broken stick model.

## Details

The package exports S3 methods for the `brokenstick`

class for the following
generic functions: `coef()`

, `fitted()`

, `model.frame()`

, `model.matrix()`

,
`plot()`

, `predict()`

, `print()`

, `residuals()`

and `summary()`

.

The package exports the following helper functions for `brokenstick`

objects:
`get_knots()`

, `get_omega()`

and `get_r2()`

.

A `brokenstick`

object is a list with the following named elements:

## Elements

`call`

Call that created the object

`names`

A named list with three elements (

`"x"`

,`"y"`

,`"g"`

) providing the variable name for time, outcome and subject, respectively.`internal`

Numeric vector of with internal knots.

`boundary`

Numeric vector of length 2 with the boundary knots.

`degree`

The

`degree`

of the B-spline. See`splines::bs()`

. Support only the values of 0 (step model) or 1 (broken stick model).`method`

String, either

`"kr"`

or`"lmer"`

, identifying the fitting model.`control`

List of control options returned by

`set_control()`

used to set algorithmic details.`beta`

Numeric vector with fixed effect estimates.

`omega`

Numeric matrix with variance-covariance estimates of the broken stick estimates.

`sigma2`

Numeric scalar with the mean residual variance.

`sample`

A numeric vector with descriptives of the training data.

`light`

Should the returned object be lighter? If

`light = TRUE`

the returned object will contain only the model settings and parameter estimates and not store the`sigma2j`

,`sample`

,`data`

,`imp`

and`mod`

elements. The light object can be used to predict broken stick estimates for new data, but does not disclose the training data and is small.`hide`

Should the output for boundary knots be hidden? Can be

`"right"`

,`"left"`

,`"boundary"`

,`"internal"`

or`"none"`

. The default is`"right"`

.`sigma2j`

Numeric vector with estimates of the residual variance per group. Only used by method

`"kr"`

.`data`

The training data used to fit the model.

`imp`

The imputations generated for the missing outcome data. Only for

`method = "kr"`

.`mod`

For

`method = "kr"`

: A named list with four components, each of class coda::mcmc. For`method = "lmer"`

: An object of class lme4::merMod.