The broken stick model describes a set of individual curves by a linear mixed model using second-order linear B-splines. The main use of the model is to align irregularly observed data to a user-specified grid of break ages.
All fitting can done in the Z-score scale, so nonlinearities and irregular data can be treated as separate problems. This package contains functions for fitting a broken stick model to data, for exporting the parameters of the model for independent use outside this package, and for predicting broken stick curves for new data.
Install the brokenstick
package from CRAN as follows:
install.packages("brokenstick")
The latest version can be installed from GitHub as follows:
install.packages("remotes") remotes::install_github("growthcharts/brokenstick")
The broken stick model describes a set of individual curves by a linear mixed model using 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:
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) scale. Unique feature of the broken stick model are:
The brokenstick
package contains functions for
We recommend the use of the brokenstick model with standardised Z‐score data. Aside from the accuracy of the fit, another key advantage of the brokenstick model is that it is easier to fit and provides easily interpretable estimates of child growth trajectories.
Anderson, C., R. Hafen, O. Sofrygin, L. Ryan, and HBGDki Community.
de Kroon, M. L. A., C. M. Renders, J. P. van Wouwe, S. van Buuren, and R. A. Hirasing. 2010. “The Terneuzen Birth Cohort: BMI Changes Between 2 and 6 Years Correlate Strongest with Adult Overweight.” PloS ONE 5 (2): e9155.
Ruppert, D., M. P. Wand, and R. J. Carroll. 2003. Semiparametric Regression. Cambridge: Cambridge University Press.
van Buuren, S. 2018. Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL.: CRC Press.