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We propose a Bayesian model for mixed ordinal and continuous multivariatedata to evaluate a latent spatial Gaussian process. Our proposed model can beused in many contexts where mixed continuous and discrete multivariateresponses are observed in an effort to quantify an unobservable continuousmeasurement. In our example, the latent, or unobservable measurement is wetlandcondition. While predicted values of the latent wetland condition variableproduced by the model at each location do not hold any intrinsic value, therelative magnitudes of the wetland condition values are of interest. Inaddition, by including point-referenced covariates in the model, we are able tomake predictions at new locations for both the latent random variable and themultivariate response. Lastly, the model produces ranks of the multivariateresponses in relation to the unobserved latent random field. This is animportant result as it allows us to determine which response variables are mostclosely correlated with the latent variable. Our approach offers an alternativeto traditional indices based on best professional judgment that are frequentlyused in ecology. We apply our model to assess wetland condition in the NorthPlatte and Rio Grande River Basins in Colorado. The model facilitates acomparison of wetland condition at multiple locations and ranks the importanceof in-field measurements.

Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data