gjam-package: Generalized Joint Attribute Modeling
In dbystrova/GJAM_clust: Generalized Joint Attribute Modeling

Description Details Author(s) References See Also

Inference and prediction for jointly distributed responses that are combinations of continous and discrete data. Functions begin with 'gjam' to avoid conflicts with other packages.

Package:	gjam
Type:	Package
Version:	2.2.7
Date:	2019-4-17
License:	GPL (>= 2)
URL:	http://sites.nicholas.duke.edu/clarklab/code/

The generalized joint attribute model (gjam) analyzes multivariate data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. It does so as a joint distribution over response variables. gjam provides inference on sensitivity to input variables, correlations between responses on the data scale, 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. Contrast this approach 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.

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.

gjam can be used to model ecological trait data, where species traits are translated to locations as community-weighted means and modes.

Posterior simulation is done by Gibbs sampling. Analysis is done by these functions, roughly in order of how frequently they might be used:

gjam fits model with Gibbs sampling.

gjamSimData simulates data for analysis by gjam.

gjamPriorTemplate sets up prior distribution for coefficients.

gjamSensitivity evaluates sensitivity to predictors from gjam.

gjamCensorY defines censored values and intervals.

gjamTrimY trims the response matrix and aggregates rare types.

gjamPlot plots output from gjam.

gjamSpec2Trait constructs plot by trait matrix.

gjamPredict does conditional prediction.

gjamOrdination ordinates the response matrix.

gjamDeZero de-zeros response matrix for storage.

gjamReZero recovers response matrix from de-zeroed format.

gjamIIE evaluates indirect effects and interactions.

gjamIIEplot plots indirect effects and interactions.

gjamSpec2Trait generates trait values.

gjamPoints2Grid aggregates incidence data to counts on a lattice.

Author: James S Clark, jimclark@duke.edu, Daniel Taylor-Rodriquez

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.

Clark, J.S. 2016. Why species tell more about traits than traits tell us about species: Predictive models. Ecology 97, 1979-1993.

Taylor-Rodriguez, D., K. Kaufeld, E. M. Schliep, J. S. Clark, and A. E. Gelfand. 2016. Joint species distribution modeling: dimension eduction using Dirichlet processes. Bayesian Analysis, in press.

gjam, gjamSimData, gjamSensitivity, gjamCensorY, gjamTrimY, gjamPredict, gjamSpec2Trait, gjamPlot, gjamDeZero, gjamReZero