STA 290 Seminar Series
DATE: Tuesday November 14th, 3:00pm
LOCATION: MSB 1147, Colloquium Room
SPEAKER: Eric Fox, Postdoctoral Fellow, US EPA Western Ecology Division
TITLE: “Comparing Spatial Regression to Random Forsts for Large Environmental Data Sets”
ABSTRACT: Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records that are spatially autocorrelated. In this talk, we compare these two techniques using a data set containing an aquatic health index at 1859 stream sites with over 200 landscape covariates. A primary application is mapping predictions and prediction errors for the aquatic health index at 1.1 million perennial stream reaches across the conterminous United States. For the spatial regression model, we develop a novel transformation procedure that estimates Box-Cox transformations to linearize covariate relationships and handles possibly zero-inflated covariates. We find that the spatial regression model with transformations, and a subsequent selection of significant covariates, has cross-validation performance slightly better than random forests. We also find that prediction interval coverage is close to nominal for each method, but that spatial regression prediction intervals tend to be narrower and have less variability than quantile regression forest prediction intervals. A simulation study is used to generalize results and clarify advantages of each modeling approach.