GJAM is an R package that analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. The tutorial in the folder tutorialsGJAM describes how to fit a GJAM and interpret results in R. For even more information on how to use GJAM, see the full GJAM vignette

gjam: Generalized Joint Attribute Modeling. 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 et al. (2018) <doi:10.1002/ecm.1241>.

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, …).

2022年5月23日 · 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 …

2022年5月23日 · 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.

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.

2022年5月24日 · Analyzes joint attribute data (e.g., species abundance) with Gibbs sampling. Input can be output from gjamSimData. Returns a list of objects from Gibbs sampling that can be plotted by gjamPlot. gjam(formula, xdata, ydata, modelList) ## S3 method for class 'gjam' print(x, ...) ## S3 method for class 'gjam' summary(object, ...)

2022年5月24日 · 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 et al. (2018) <doi:10.1002/ecm.1241>.

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

2022年1月2日 · 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 …