brokenstick 2.0.0

Main changes

  1. Function brokenstick() in version 2.0.0 sets the Kasim-Raudenbush sampler as the default method. The former method lme4::lmer() remains available by setting method = "lmer" argument.

  2. Version 2.0.0 adopts the variable names of the coda package (e.g., start, end, thin, niter, and so on) and stores the results of the Kasim-Raudenbush sampler as objects of class mcmc.

  3. For method = "kr" one may now inspect the solution of the sampler by standard functions from the coda package. For method = "lmer" we can apply functions from the lme4 package for merMod objects.

  4. Version 2.0.0 redefines the brokenstick class. New entries include call, formula, internal, sample, light, data, imp and mod. Removed entries are knots (renamed to internal) and draws (renamed to imp). We may omit the newdata argument for the training data. Setting light = TRUE creates a small version of the brokenstick object. Objects of class brokenstick are not backwards compatible, so one should regenerate objects of class brokenstick in order use newer features in 2.0.0.

  5. Version 2.0.0 conforms to classic model fitting interface in R. Renames the new_data argument to newdata to conform to predict.lm(). Methods plot() and predict() no longer require a newdata argument. All special cases of predict() updated and explained in documentation and examples.

  6. Version 2.0.0 adds methods coef(), fitted(), model.frame(), model.matrix(), print() and summary for the brokenstick object.

  7. Simplifies algorithmic control. Renames control_brokenstick() to set_control() and removes a layer in the control list.

Minor changes

brokenstick 1.1.1

brokenstick 1.1.0

This version adds a couple of minor alterations.

brokenstick 1.0.0

brokenstick 0.78.0

brokenstick 0.77.0

brokenstick 0.76.0

brokenstick 0.75.0

brokenstick 0.72.1

brokenstick 0.72.0

brokenstick 0.71.0

brokenstick 0.70.1

brokenstick 0.70.0

  1. brokenstick adopted the tidymodels philosophy, and now includes a dependency on hardhat. It is now possible to fit a model using five different interfaces. There is no need anymore the hardcode variable names in the source data.

  2. This version introduces a new estimation method, the Kasim-Raudenbush sampler. The new method is more flexible and faster than lme4::lmer() when the number of knots is large.

  3. This version introduces two simple correlation models that may be used to smooth out the variance-covariance matrix of the random effects.

  4. The definition of the brokenstick class has changed. Objects of class brokenstick do no longer store the training data.

  5. The brokenstick_export class is retired.

  6. The predict() function is fully rewritten as has now a new interface. Since the brokenstick class does not store the training data anymore, the predict() function now obtains a new_data argument. Syntax that worked for brokenstick package before 0.70.0 does not work anymore and should be updated. The shape argument replaces the output argument.

  7. The plot() function is rewritten, and now requires a new_data specification.

  8. Retired functions: brokenstick() replaces fit_brokenstick(), predict.brokenstick() replaces predict.brokenstick_export(), get_r2() replaces get_pev()

  9. Removed functions: get_data(), get_X(), export()

brokenstick 0.62.0

brokenstick 0.61.0

brokenstick 0.60.0

brokenstick 0.55

brokenstick 0.54

brokenstick 0.53

Here is the abstract of the lecture:

Broken stick model for individual growth curves

Stef van Buuren

  1. Netherlands Organization for Applied Scientific Research TNO
  2. Utrecht University

The broken stick model describes a set of individual curves by a linear mixed model using second-order linear B-splines. The model can be used

The user specifies a set of break ages at which the straight lines connect. Each individual obtains an estimate at each break age, so the set of estimates of the individual form a smoothed version of the observed trajectory.

The main assumptions of the broken stick model are that the development between the break ages follows a straight line, and that the broken stick estimates follow a common multivariate normal distribution. In order to conform to the assumption of multivariate normality, the user may fit the broken stick model on suitably transformed data that yield the standard normal (Z-score) scale.

This lecture outlines the model and introduces the brokenstick R package.