gbmt-package: Group-Based Multivariate Trajectory Modeling

In alessandromagrini/gbmt: Group-Based Multivariate Trajectory Modeling

Description

Estimation and analysis of group-based multivariate trajectory models.

Details

Package:	gbmt
Type:	Package
Version:	0.1.2
Date:	2021-11-24
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, 2021). 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 <[e-mail]>

References

A. Magrini (2021). Assessment of agricultural sustainability in European Union countries: A group-based multivariate trajectory approach. To be appeared on Advances in Statistical Analysis.

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.

alessandromagrini/gbmt documentation built on Feb. 6, 2022, 10:55 p.m.