This version adds a couple of minor alterations.
plot.brokenstick()with the ability to plot imputed trajectories
degree > 1
predict()when the group variable is a factor
model.matrix()removes rows with
degree = 0
brokenstickobject smaller since no blueprints are stored
recipeinterface to the
recipes::recipe()to inform R package installation process
ggplot2out-of-range/missing messages through better filtering
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.
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.
This version introduces two simple correlation models that may be used to smooth out the variance-covariance matrix of the random effects.
The definition of the
brokenstick class has changed. Objects of class
brokenstick do no longer store the training data.
brokenstick_export class is retired.
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
plot() function is rewritten, and now requires a
ggplotobjects sharper in vignettes by
hbgd(which is no longer developed) by
NEWS.mdfile to track changes to the package
show_referencesflag to FALSE
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.