gjam exploits censoring to combine multiple data types in a single model, including mixtures of continuous and discrete data. For example, the microbial community (composition data) might be tracked together with host condition (continuous, categorical, binary, ordinal, …).

May 23, 2022 · gjam generates an object of class "gjam", allowing it to appropriate the summary and print functions in R. To avoid conflicts with other packages, gjam function names begin with "gjam". gjam uses the RcppArmadillo linear algebra …

Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data, Ecological Monographs, in press. interpretation is needed on the observation …

Jan 2, 2022 · The first part of this tutorial covers the theoretical background of a generalized joint attribute model (GJAM), which is a multivariate hierarchical Bayesian model. GJAM has many uses; in this tutorial, we will focus on its use to develop Species Distribution Models (SDMs).

Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data, in review. gjam models multivariate responses that can be combinations of discrete and continuous variables, where interpretation is needed on the observation scale.

May 23, 2022 · 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.

Generalized joint attribute modeling - gjam James S. Clark 2016-04-25 Contents Overview ...

We develop a generalized joint attribute model (GJAM), a probabilistic framework that readily applies to data that are combinations of presence- absence, ordinal, continuous, discrete, composition, zero- inflated, and censored.

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark …

We develop a generalized joint attribute model (GJAM), a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored. It does so as a joint distribution over all species providing inference.