Analysis of multivariate data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. gjam models the joint distribution and provides inference on sensitivity to input variables, correlations between responses, model selection, and prediction.
Importantly, analysis is done on the observation scale. That is, coefficients and covariances are interpreted on the same scale as the data. This contrasts with standard Generalized Linear Models, where coefficients and covariances are difficult to interpret and cannot be compared across responses that are modeled on different scales and nonlinear link functions.
gjam was motivated by species distribution and abundance data in ecology, but can provide an attractive alternative to traditional methods wherever observations are multivariate and combine multiple scales and mixtures of continuous and discrete data.
citation: Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2017. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs,87, 34-56 Clark2017EcolMonogr, clarksupplement.
Dimension reduction: Taylor-Rodrıguez, D., K. Kaufeld, E. M. Schliep, J. S. Clark, and Alan E. Gelfand. 2016. Joint Species distribution modeling: dimension eduction using Dirichlet processes. Bayesian Analysis, in press. bayesanaly2016
Installation in R or RStudio:
> install.packages('gjam') > library('gjam')
> help('gjam') > browseVignettes('gjam')
Below are cluster plots of the correlation matrix for a presence-absence model (a), continuous abundance model (b), and the response to environmental variables (d). The cluster analysis in (c) is based on distances in (d). These plots are obtained by specifying GRIDPLOTS=T in gjamPlot.