Abstract
An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation interface covering a broad range of non-linear generalized structural equation models is described. The model and software are demonstrated in data of measurements of the serotonin transporter in the human brain.
| Original language | English |
|---|---|
| Journal | Computational Statistics |
| Volume | 28 |
| Issue number | 4 |
| Pages (from-to) | 1385-1452 |
| Number of pages | 68 |
| ISSN | 0943-4062 |
| DOIs | |
| Publication status | Published - 1 Aug 2013 |
Keywords
- Latent variable model
- Maximum likelihood
- R
- Seasonality
- Serotonin
- SERT
- Structural equation model