Broken Stick Model for Irregular Longitudinal Data
Class brokenstick
brokenstick: A package for irregular longitudinal data.
Fit a brokenstick model to irregular data
Extract Model Coefficients from brokenstick Object
Set controls for Kasim-Raudenbush sampler
Empirical Bayes predictor for random effects
Calculate fitted values
Obtain the knots from a broken stick model
Extract Variance and Correlation Components
Obtain proportion of explained variance from a broken stick model
Kasim-Raudenbush sampler for two-level normal model
Create linear splines basis
Parse formula for brokenstick model
Plot observed and fitted trajectories by group
Plot observed and fitted trajectories from fitted brokenstick model
Predict from a brokenstick model
Print brokenstick object
Extract residuals from brokenstick model
Set controls to steer calculations
Create summary of brokenstick object
Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.
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