| gbmt-package {gbmt} | R Documentation |
Group-Based Multivariate Trajectory Modeling
Description
Estimation and analysis of group-based multivariate trajectory models.
Details
| Package: | gbmt |
| Type: | Package |
| Version: | 0.1.3 |
| Date: | 2022-03-05 |
| License: | GPL-2 |
Group-based trajectory modeling is a statistical method to determine groups of units based on the trend of a multivariate time series. It is a special case of latent class growth curves where the units in the same group have the same trajectory (Nagin, 2005), but it assumes a multivariate polynomial regression on time within each group, instead of a univariate one, to account for multiple indicators (Nagin et al., 2018; Magrini, 2022). A group-based multivariate trajectory model is estimated through the Expectation-Maximization (EM) algorithm, which allows unbalanced panel and missing values. The main functions currently implemented in the package are:
gbmt: to estimate a group-based multivariate trajectory model;
predict.gbmt: to perform prediction on trajectories;
plot.gbmt: to display estimated and predicted trajectories;
posterior: to compute posterior probabilities for new units.
Author(s)
Alessandro Magrini <alessandro.magrini@unifi.it>
References
A. Magrini (2022). Assessment of agricultural sustainability in European Union countries: A group-based multivariate trajectory approach. Advances in Statistical Analysis, published online: March 2022. DOI: 10.1007/s10182-022-00437-9
D. S. Nagin, B. L. Jones, V. L. Passos and R. E. Tremblay (2018). Group-based multi-trajectory modeling. Statistical Methods in Medical Research, 27(7): 2015-2023. DOI: 10.1177/0962280216673085
D. S. Nagin (2005). Group-based modeling of development. Harvard University Press, Cambridge, US-MA.