brokenstick: A package for irregular longitudinal data.
Source:R/brokenstick-package.R
brokenstick-pkg.RdThe 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.
Details
The brokenstick package contains functions for fitting a broken stick model to data, for predicting broken stick curves for new data, and for plotting the results.
Note
This work was supported by the Bill & Melinda Gates Foundation. The contents are the sole responsibility of the authors and may not necessarily represent the official views of the Bill & Melinda Gates Foundation or other agencies that may have supported the primary data studies used in the present study.
brokenstick functions
The main functions are:
brokenstick() | Fit a broken stick model to irregular data |
plot() | Plot observed and fitted trajectories by group |
predict() | Obtain predictions on new data |
summary() | Extract object summaries |
The following functions are user-oriented helpers:
coef() | Extract estimated parameters |
fitted() | Calculate fitted values |
get_knots() | Obtain the knots from a broken stick model |
get_omega() | Extract variance-covariance of random effects |
get_r2() | Obtain proportion of explained variance |
model.frame() | Extract model frame |
model.matrix() | Extract design matrix |
residuals() | Extract residuals from broken stick model |
The following functions perform calculations:
set_control() | Set controls to steer calculations |
control_kr() | Set controls for the kr method |
References
van Buuren, S. (2023). Broken Stick Model for Irregular Longitudinal Data. Journal of Statistical Software, 106(7), 1--51. doi:10.18637/jss.v106.i07
van Buuren, S. (2018). Flexible Imputation of Missing Data. Second Edition. Chapman & Hall/CRC. Chapter 11. https://stefvanbuuren.name/fimd/sec-rastering.html#sec:brokenstick